physicsworld--2020-08-06--Portable sensor detects biomagnetic signals in noisy outdoor environments - Physics World

A portable sensor that can detect tiny biomagnetic signals from the brain and heart – without the expensive magnetic shielding needed by current magnetoencephalographic techniques – has been developed by researchers in the US .
The low-cost set-up , which is small enough to fit in a backpack , can operate successfully even near to power lines and a railway , and could find application in field triage , brain–machine interfaces and even precise magnetic navigation .
Magnetometry in the form of magneto- and electroencephalography ( MEG and EEG ) can provide vital insight into human brain and cardiac function @ @ @ @ @ @ @ like functional MRI and positron-emission tomography ( PET ) .
Commercial MEG systems , however , are expensive to operate and come with a sizeable footprint – requiring both large-scale magnetic shielding and cryogenic cooling systems – which also limits the activities that they can be used to study .
In their new study , however , physicist Mark Limes of Twinleaf and colleagues from Princeton University have focused on optically pumped atomic magnetometers to measure the biomagnetic fields .
These use lasers to make the atoms in alkali-metal vapours spin-coherent — and @ @ @ @ @ @ @ by the presence of magnetic fields , with the bulk magnetization precessing around the field of interest .
A key part of enabling the sensor to work in less controlled environments , Limes explains , lies in this free-precession measurement .
“ Many magnetic sensors are resonant systems , whether all-optical or requiring some magnetic field manipulation such as applied radio-frequency , ” he says .
“ Any resonant system out in a noisy environment has feedback loops that track the magnetic field and are usually bandwidth-limited in a significant way.
” Issues @ @ @ @ @ @ @ be controlled so well — meaning that techniques that should theoretically work well in Earth-sized magnetic fields end up , in practice , only working viably in highly controlled , magnetically shielded environments .
“ We don ’ t need to track the magnetic field or provide feedback , as the crux of our measurement is passively watching the atoms respond to the total magnetic field , ” Limes explains .
Still , previous atomic magnetometers could also only operate effectively in shielded environments .
The second part of the new design involves @ @ @ @ @ @ @ form a magnetic gradiometer .
“ You can have a really good magnetometer , but in the frequency range that brain and heart signals are in , the magnetometer will be totally swamped by ambient magnetic noise from power lines , transformers or even the local train rail , ” Limes tells Physics World .
The “ impressive leap ” of the present work towards practical applications like medical imaging , he adds , comes from getting two sensors to work together , so that they “ reject distant common-mode magnetic field sources @ @ @ @ @ @ @ retaining sensitivity to nearby biomagnetic sources.
” In field tests , the prototype gradiometer device was able to pick up brain activity in a test subject ’ s audio cortex in response to randomized sound stimuli — despite the experiments being conducted in an unshielded , natural environment both within 750 m of a main railway line and within just 75 m of electrical power lines .
“ Our sensor is really unprecedented , and opens up some new regimes and applications , including handheld or wearable alkali-based sensors , ” Limes concludes @ @ @ @ @ @ @ either the human heart or brain is an arduous task , requiring magnetic sensors with exceptional sensitivity , ” comments Nathanial Wilson , a physicist from The University of Adelaide who was not involved the present study .
He adds : “ This work effectively bridges the gap between tabletop experiments and real-world applications , and paves the way towards lost-cost , portable devices for diagnostic use in a clinical setting.
” “ This is a very significant step forward as this makes femtotesla-level magnetometry practical , ” adds Dmitry Budker , a @ @ @ @ @ @ @ was also not involved in the present study .
“ Scientists can now turn their focus from magnetometer development to research on real-world applications.
” With their initial study complete , the researchers are now looking to improve the accuracy and sensitivity of their sensors – so that they might be able to compete with existing cryogenic superconducting quantum interference device ( SQUID ) MEG detectors – after which , they intend to build an array of sensors to demonstrate that the systems can perform the source localization necessary for MEG studies .
@ @ @ @ @ @ @ Applied .

Researcher Michael Romalis from Princeton University tests out the in-the-field MEG recording apparatus. (Courtesy: Mark Limes)
A portable sensor that can detect tiny biomagnetic signals from the brain and heart – without the expensive magnetic shielding needed by current magnetoencephalographic techniques – has been developed by researchers in the US. The low-cost set-up, which is small enough to fit in a backpack, can operate successfully even near to power lines and a railway, and could find application in field triage, brain–machine interfaces and even precise magnetic navigation.
Magnetometry in the form of magneto- and electroencephalography (MEG and EEG) can provide vital insight into human brain and cardiac function, with resolutions that exceed alternative techniques like functional MRI and positron-emission tomography (PET). Commercial MEG systems, however, are expensive to operate and come with a sizeable footprint – requiring both large-scale magnetic shielding and cryogenic cooling systems – which also limits the activities that they can be used to study.
In their new study, however, physicist Mark Limes of Twinleaf and colleagues from Princeton University have focused on optically pumped atomic magnetometers to measure the biomagnetic fields. These use lasers to make the atoms in alkali-metal vapours spin-coherent — and subsequently to measure how they are perturbed by the presence of magnetic fields, with the bulk magnetization precessing around the field of interest.
A key part of enabling the sensor to work in less controlled environments, Limes explains, lies in this free-precession measurement. “Many magnetic sensors are resonant systems, whether all-optical or requiring some magnetic field manipulation such as applied radio-frequency,” he says. “Any resonant system out in a noisy environment has feedback loops that track the magnetic field and are usually bandwidth-limited in a significant way.”
Issues arise because magnetic field feedback can only be controlled so well — meaning that techniques that should theoretically work well in Earth-sized magnetic fields end up, in practice, only working viably in highly controlled, magnetically shielded environments. “We don’t need to track the magnetic field or provide feedback, as the crux of our measurement is passively watching the atoms respond to the total magnetic field,” Limes explains.
Still, previous atomic magnetometers could also only operate effectively in shielded environments. The second part of the new design involves coupling two of the sensors together to form a magnetic gradiometer. “You can have a really good magnetometer, but in the frequency range that brain and heart signals are in, the magnetometer will be totally swamped by ambient magnetic noise from power lines, transformers or even the local train rail,” Limes tells Physics World. The “impressive leap” of the present work towards practical applications like medical imaging, he adds, comes from getting two sensors to work together, so that they “reject distant common-mode magnetic field sources that serve as background noise, while retaining sensitivity to nearby biomagnetic sources.”
In field tests, the prototype gradiometer device was able to pick up brain activity in a test subject’s audio cortex in response to randomized sound stimuli — despite the experiments being conducted in an unshielded, natural environment both within 750 m of a main railway line and within just 75 m of electrical power lines.
“Our sensor is really unprecedented, and opens up some new regimes and applications, including handheld or wearable alkali-based sensors,” Limes concludes.
“Measuring biomagnetic signals originating from either the human heart or brain is an arduous task, requiring magnetic sensors with exceptional sensitivity,” comments Nathanial Wilson, a physicist from The University of Adelaide who was not involved the present study. He adds: “This work effectively bridges the gap between tabletop experiments and real-world applications, and paves the way towards lost-cost, portable devices for diagnostic use in a clinical setting.”
“This is a very significant step forward as this makes femtotesla-level magnetometry practical,” adds Dmitry Budker, a physicist at Johannes Gutenberg University, who was also not involved in the present study. “Scientists can now turn their focus from magnetometer development to research on real-world applications.”
With their initial study complete, the researchers are now looking to improve the accuracy and sensitivity of their sensors – so that they might be able to compete with existing cryogenic superconducting quantum interference device (SQUID) MEG detectors – after which, they intend to build an array of sensors to demonstrate that the systems can perform the source localization necessary for MEG studies.
The research is described in Physical Review Applied.


physicsworld--2020-08-06--COVID-19: an economic perspective - Physics World

As an American living in the UK , how do you think the US has handled the economic response to the pandemic ?
I can ’ t help but wonder what drugs US stock market investors are taking .
Stock prices have been ridiculously high since Donald Trump was elected and now we are seeing the consequences of that coming to roost with his complete mismanagement of the pandemic .
I think the long-range economic situation is worse than the stock market seems to think it is .
There has been no co-ordination in the management of the pandemic at @ @ @ @ @ @ @ when things will stabilize .
The US has not frozen the economy by keeping workers in place , as most European countries have done .
What are the consequences of this ?
There is a dangerous “ Keynesian ” feedback loop running .
If people become unemployed then they don ’ t spend .
If they don ’ t spend , products don ’ t sell and more people become unemployed .
To prevent this from happening again the US government needs to take firm action .
While there have been large stimulus @ @ @ @ @ @ @ they are large enough and they have been implemented in a very inefficient way .
You recently presented a new model of the economic impact of the current COVID-19 crisis .
Were there any unexpected results ?
We worked together with IHS Markit – a leading data provider – to build a model that could predict the impact of the crisis on different industries .
We saw some surprising network effects .
For example , we discovered that opening more industries can actually make things worse if the wrong industries are opened ( @ @ @ @ @ @ @ during the lockdown period many industries were not able to produce at full capacity .
If the demand for the products of an industry is more than it can supply , it becomes necessary to ration its output How might this happen ?
Suppose industry A produces a critical input for industry B , and industry B produces critical inputs for other industries as well .
Then if industry C is opened , and industry C uses the product of industry A as well , industry A may now supply even less of @ @ @ @ @ @ @ if industry B supplies products to many different industries , this can reduce the output of the whole economy .
So in any partial reopening of the economy , it ’ s important to open the right industries .
Our model allowed us to investigate questions like this and make detailed recommendations .
We are now waiting for government reports and other data that will allow us to test our results and find out how we did .
What can be done to minimize risks of reopening certain parts of the economy that @ @ @ @ @ @ @ begin by saying that we aren ’ t epidemiologists .
We were able to use detailed data about the tasks performed by occupations and we knew which occupations are employed in each industry .
Combining this with epidemiological studies allowed us to estimate how much the infection rate was likely to go up by opening a given industry .
Broadly speaking , we predicted that the increase in the infection rate from industries that supply other industries is substantially smaller than it is for consumer-facing industries .
Our recommendation was to open business-to-business @ @ @ @ @ @ @ This was roughly in line with what the government did in the beginning .
But you are not supportive of the UK government reopening higher-risk consumer-facing industries such as pubs as it did last month ?
I ’ m fairly sceptical .
The UK has not got the epidemic under control in the same way that most European countries have .
It will make a huge difference if we really have an aggressive track-and-trace system with free and widely available testing .
But so far we don ’ t have that .
The @ @ @ @ @ @ @ predict and it depends on how it is done .
Many schools are coming up with creative ways to partially reopen , like organizing students into bubbles or having the students quarantine at regular intervals .
Of course , because so many people depend on schools for childcare , this can not be separated from reopening the economy .
Do you think this crisis is going to increase the disparity between the rich and the poor ?
In the short term , the pandemic is definitely making inequality even worse than it already @ @ @ @ @ @ @ first paper , where we predicted who could work from home and who couldn ’ t , and who is in essential and non-essential industries .
The conclusion is that the poor are bearing the brunt of the pandemic because they can ’ t work from home and they ’ re less likely to work in essential industries .
So in the short term the pandemic is clearly making things worse .
And in the long term ?
It ’ s putting pressure on the system , it ’ s making people more @ @ @ @ @ @ @ for change .
There are a lot of changes we can make to fix the problem .
We need to tax the rich more , we need to give workers more power to organize and we need a higher minimum wage .
My hope is that the pandemic makes inequality so intolerable that people will rise up , take action and implement some lasting changes .

Physics World. Members of the Institute of Physics can enjoy the full issue Taken from the August 2020 issue of. Members of the Institute of Physics can enjoy the full issue via the Physics World app
J Doyne Farmer – a physicist who has studied the economic impact of the COVID-19 pandemic – talks to Benjamin Skuse about lockdown easing and the prospects of financial recovery
Forecasting the impact Based at the University of Oxford, J Doyne Farmer is concerned that the COVID-19 pandemic is increasing economic disparity between rich and poor. (Courtesy: Carlotta Cardana)
As an American living in the UK, how do you think the US has handled the economic response to the pandemic?
I can’t help but wonder what drugs US stock market investors are taking. Stock prices have been ridiculously high since Donald Trump was elected and now we are seeing the consequences of that coming to roost with his complete mismanagement of the pandemic. I think the long-range economic situation is worse than the stock market seems to think it is. There has been no co-ordination in the management of the pandemic at a national level and it remains unclear when things will stabilize. The US has not frozen the economy by keeping workers in place, as most European countries have done.
What are the consequences of this?
There is a dangerous “Keynesian” feedback loop running. If people become unemployed then they don’t spend. If they don’t spend, products don’t sell and more people become unemployed. To prevent this from happening again the US government needs to take firm action. While there have been large stimulus packages, I don’t think they are large enough and they have been implemented in a very inefficient way.
You recently presented a new model of the economic impact of the current COVID-19 crisis. Were there any unexpected results?
We worked together with IHS Markit – a leading data provider – to build a model that could predict the impact of the crisis on different industries. We saw some surprising network effects. For example, we discovered that opening more industries can actually make things worse if the wrong industries are opened (arXiv:2005.10585). This problem happens because during the lockdown period many industries were not able to produce at full capacity. If the demand for the products of an industry is more than it can supply, it becomes necessary to ration its output
How might this happen?
Suppose industry A produces a critical input for industry B, and industry B produces critical inputs for other industries as well. Then if industry C is opened, and industry C uses the product of industry A as well, industry A may now supply even less of its product to industry B. And if industry B supplies products to many different industries, this can reduce the output of the whole economy. So in any partial reopening of the economy, it’s important to open the right industries. Our model allowed us to investigate questions like this and make detailed recommendations. We are now waiting for government reports and other data that will allow us to test our results and find out how we did.
What can be done to minimize risks of reopening certain parts of the economy that harbour more epidemiological risk?
Let me begin by saying that we aren’t epidemiologists. We were able to use detailed data about the tasks performed by occupations and we knew which occupations are employed in each industry. Combining this with epidemiological studies allowed us to estimate how much the infection rate was likely to go up by opening a given industry. Broadly speaking, we predicted that the increase in the infection rate from industries that supply other industries is substantially smaller than it is for consumer-facing industries. Our recommendation was to open business-to-business industries, but restrict consumer-facing industries. This was roughly in line with what the government did in the beginning.
My hope is that the pandemic makes inequality so intolerable that people will rise up, take action and implement some lasting changes.
But you are not supportive of the UK government reopening higher-risk consumer-facing industries such as pubs as it did last month?
I’m fairly sceptical. The UK has not got the epidemic under control in the same way that most European countries have. It will make a huge difference if we really have an aggressive track-and-trace system with free and widely available testing. But so far we don’t have that.
Do schools present a similar risk as reopening consumer-facing industries?
The effect of opening schools is difficult to predict and it depends on how it is done. Many schools are coming up with creative ways to partially reopen, like organizing students into bubbles or having the students quarantine at regular intervals. Of course, because so many people depend on schools for childcare, this cannot be separated from reopening the economy.
Do you think this crisis is going to increase the disparity between the rich and the poor?
In the short term, the pandemic is definitely making inequality even worse than it already is. We saw this in our first paper, where we predicted who could work from home and who couldn’t, and who is in essential and non-essential industries. The conclusion is that the poor are bearing the brunt of the pandemic because they can’t work from home and they’re less likely to work in essential industries. So in the short term the pandemic is clearly making things worse.
And in the long term?
It’s putting pressure on the system, it’s making people more aware and it may be a catalyst for change. There are a lot of changes we can make to fix the problem. We need to tax the rich more, we need to give workers more power to organize and we need a higher minimum wage. My hope is that the pandemic makes inequality so intolerable that people will rise up, take action and implement some lasting changes.


physicsworld--2020-08-05--Experiments pin down conditions that make hot water freeze before cold - Physics World

In 1963 , a Tanzanian schoolboy called Erasto Mpemba was making ice cream when he noticed something strange : hot water sometimes freezes faster than cold water .
Though Mpemba was not the first to wonder about this phenomenon , his report nevertheless captured the scientific imagination .
The so-called “ Mpemba effect ” has remained contentious ever since – not least because the complex matrix of interactions at work when freezing a cup of hot water , coupled with water ’ s many anomalies , make it difficult to reproduce .
Researchers at Simon Fraser University in Canada have @ @ @ @ @ @ @ experimental model of a hot system relaxing to equilibrium with a colder heat bath .
According to team leader John Bechhoefer , the technique he developed with PhD student Avinash Kumar enabled them to replicate the Mpemba effect in a reliable way , making it possible to pin down the precise conditions it requires .
The definition of the Mpemba effect has been hotly debated .
Is it concerned with the time it takes for water in a container to start to freeze , completely freeze ( both of which are hard to @ @ @ @ @ @ @ to reach freezing temperatures ?
To complicate matters further , a similar effect has also been observed in magnetic systems , for other phase transitions .
However , these systems , although simpler than water , are still too complex to pin down precise parameters for the effect .
Bechhoefer and Kumar ’ s experiments start with a beaker of water , but they don ’ t change the water ’ s temperature in real terms .
Instead , they release a tiny glass bead in the beaker thousands of times from different @ @ @ @ @ @ @ that defines the effective container size for their experiment .
The locations where they release the bead are determined according to a probability distribution defined by thermodynamics and Boltzmann statistics for the system ’ s chosen initial “ temperature ” .
As the bead falls it is bombarded by water molecules , resulting in Brownian motion , but the researchers also subject it to a “ virtual potential profile ” using a feedback optical tweezer system .
This changes the probability distribution of the bead ’ s position – effectively creating a change @ @ @ @ @ @ @ ” .
This virtual potential profile has two dips in it , in line with the double potential well of water ’ s free energy landscape : one dip where water can “ supercool ” to liquid water at subzero temperatures , and a lower dip where water freezes .
By measuring the probability distribution of the released beads ’ positions after a set time , and establishing how much that distribution differs from an equilibrium probability distribution , the researchers gain information about how quickly the system is equilibrating – an analogue @ @ @ @ @ @ @ would approach freezing temperatures .
While all this may sound more complicated than popping a cup of water in an ice box , Bechoefer says it actually makes it a lot simpler to determine what conditions are required to produce the Mpemba effect .
The idea arose out of a visit to the University of Maryland , where Zhiyue Lu and Oren Raz were then working on a theoretical framework for a simplified model of the Mpemba effect .
During the visit , Lu and Raz button-holed Bechhoefer and persuaded him that his @ @ @ @ @ @ @ allows finer , non-diffraction-limited control over the virtual potential landscape than regular optical tweezers – would make a great way of testing their model .
Thanks to this system , Bechhoefer and Kumar could work with a sample that had an effective width of just 0.4 µm , making experimental runs much faster .
Bechhoefer acknowledges that his team ’ s system is an “ abstract ” and “ almost geometrical ” way of picturing the Mpemba effect .
Nevertheless , he and Kumar were able to identify parameters for which hotter “ @ @ @ @ @ @ @ ones .
“ It sort of suggested that all the peculiarities of water and ice – all the things that made the original effect so hard to study – might be in a way peripheral , ” he says .
Although the double potential well played a crucial role in producing the effect , Bechhoefer and Kumar found this alone was not enough to trigger it .
The system also needed the barrier between the two potential wells to be offset from the midpoint between them .
When the distance to the deeper @ @ @ @ @ @ @ the shallower well , the researchers found that the number of starting positions for which the bead will fall directly into the deeper well ( rather than entering the shallower well first and then jiggling around until Brownian motion eventually nudges it into the deeper one ) is greater .
While the Mpemba effect is not typical behaviour , Bechhoefer and Kumar ’ s study suggests that it is not limited to specialized conditions that would make it impossible to reproduce in a reliable way .
“ What this shows is that there @ @ @ @ @ @ @ just observe , but in some sense create , engineer and control the effect , ” Bechhoefer says .
Controlling the Mpemba effect could have important practical implications too , for example in the heat removal systems that keep electronics cool .
Lu and Raz ’ s theoretical treatment also suggests that there should be a “ reverse Mpemba effect ” for heating systems , and Bechhoefer and Kumar have their sights set on replicating this in future experiments .
“ This work extends the Mpemba phenomenon , ” says Changqing Sun , @ @ @ @ @ @ @ Technological University focuses on properties of water that relate to the Mpemba effect .
However , while Sun asserts that the work “ certainly contributes to the knowledge for general situations of heat removal ” he thinks further caution may be needed when applying it to water .
In water , he adds , “ the microscopic mechanism may differ , such as the higher thermal diffusivity and lower specific heat of water surface and the manner of hydrogen bond ( O : H-O ) energy exchange .
”

Optical tweezers: Avinash Kumar, who built the Mpemba effect apparatus and carried out the experiments under the guidance of John Bechhoefer, at work in the lab. (Courtesy: Prithviraj Basak)
In 1963, a Tanzanian schoolboy called Erasto Mpemba was making ice cream when he noticed something strange: hot water sometimes freezes faster than cold water. Though Mpemba was not the first to wonder about this phenomenon, his report nevertheless captured the scientific imagination. The so-called “Mpemba effect” has remained contentious ever since – not least because the complex matrix of interactions at work when freezing a cup of hot water, coupled with water’s many anomalies, make it difficult to reproduce.
Researchers at Simon Fraser University in Canada have now overcome this problem with a simplified experimental model of a hot system relaxing to equilibrium with a colder heat bath. According to team leader John Bechhoefer, the technique he developed with PhD student Avinash Kumar enabled them to replicate the Mpemba effect in a reliable way, making it possible to pin down the precise conditions it requires.
A simplified model system
The definition of the Mpemba effect has been hotly debated. Is it concerned with the time it takes for water in a container to start to freeze, completely freeze (both of which are hard to observe accurately in practice) or simply to reach freezing temperatures? To complicate matters further, a similar effect has also been observed in magnetic systems, for other phase transitions. However, these systems, although simpler than water, are still too complex to pin down precise parameters for the effect.
Bechhoefer and Kumar’s experiments start with a beaker of water, but they don’t change the water’s temperature in real terms. Instead, they release a tiny glass bead in the beaker thousands of times from different positions across the width of a sample that defines the effective container size for their experiment. The locations where they release the bead are determined according to a probability distribution defined by thermodynamics and Boltzmann statistics for the system’s chosen initial “temperature”.
As the bead falls it is bombarded by water molecules, resulting in Brownian motion, but the researchers also subject it to a “virtual potential profile” using a feedback optical tweezer system. This changes the probability distribution of the bead’s position – effectively creating a change in the system’s “temperature”. This virtual potential profile has two dips in it, in line with the double potential well of water’s free energy landscape: one dip where water can “supercool” to liquid water at subzero temperatures, and a lower dip where water freezes. By measuring the probability distribution of the released beads’ positions after a set time, and establishing how much that distribution differs from an equilibrium probability distribution, the researchers gain information about how quickly the system is equilibrating – an analogue to how quickly a cup of water would approach freezing temperatures.
While all this may sound more complicated than popping a cup of water in an ice box, Bechoefer says it actually makes it a lot simpler to determine what conditions are required to produce the Mpemba effect. The idea arose out of a visit to the University of Maryland, where Zhiyue Lu and Oren Raz were then working on a theoretical framework for a simplified model of the Mpemba effect. During the visit, Lu and Raz button-holed Bechhoefer and persuaded him that his specialist feedback optical tweezer system – which allows finer, non-diffraction-limited control over the virtual potential landscape than regular optical tweezers – would make a great way of testing their model. Thanks to this system, Bechhoefer and Kumar could work with a sample that had an effective width of just 0.4 µm, making experimental runs much faster.
Reliably reproducing the Mpemba effect
Bechhoefer acknowledges that his team’s system is an “abstract” and “almost geometrical” way of picturing the Mpemba effect. Nevertheless, he and Kumar were able to identify parameters for which hotter “initial temperatures” cooled faster than chillier ones. “It sort of suggested that all the peculiarities of water and ice – all the things that made the original effect so hard to study – might be in a way peripheral,” he says.
Although the double potential well played a crucial role in producing the effect, Bechhoefer and Kumar found this alone was not enough to trigger it. The system also needed the barrier between the two potential wells to be offset from the midpoint between them. When the distance to the deeper well is greater than the distance to the shallower well, the researchers found that the number of starting positions for which the bead will fall directly into the deeper well (rather than entering the shallower well first and then jiggling around until Brownian motion eventually nudges it into the deeper one) is greater.
While the Mpemba effect is not typical behaviour, Bechhoefer and Kumar’s study suggests that it is not limited to specialized conditions that would make it impossible to reproduce in a reliable way. “What this shows is that there are systems where you can reproducibly not just observe, but in some sense create, engineer and control the effect,” Bechhoefer says.
Controlling the Mpemba effect could have important practical implications too, for example in the heat removal systems that keep electronics cool. Lu and Raz’s theoretical treatment also suggests that there should be a “reverse Mpemba effect” for heating systems, and Bechhoefer and Kumar have their sights set on replicating this in future experiments.
“This work extends the Mpemba phenomenon,” says Changqing Sun, whose research at Singapore’s Nanyang Technological University focuses on properties of water that relate to the Mpemba effect. However, while Sun asserts that the work “certainly contributes to the knowledge for general situations of heat removal” he thinks further caution may be needed when applying it to water. In water, he adds, “the microscopic mechanism may differ, such as the higher thermal diffusivity and lower specific heat of water surface and the manner of hydrogen bond (O:H-O) energy exchange.”
Full details are reported in Nature.


physicsworld--2020-08-05--Autonomous discovery of battery electrolytes with robotic experimentation and machine learning - Phy

Autonomous discovery of battery electrolytes with robotic experimentation and machine learning - Physics World

Innovations in batteries take years to formulate and commercialize , requiring extensive experimentation during the design and optimization phases .
@ @ @ @ @ selection of a battery electrolyte through a black-box optimization algorithm directly integrated into a robotic @ @ @ @ @ here the discovery of a novel battery electrolyte by this experiment completely guided by the @ @ @ @ @ .
Motivated by the recent trend toward super-concentrated aqueous electrolytes for high-performance batteries , we @ @ @ @ @ machine-learning software package – to search mixtures of commonly used lithium and sodium salts for @ @ @ @ @ electrochemical stability windows .
This webinar presented by Venkat Viswanathan , will help the audience @ @ @ @ @ the importance of robotic experimentation • Learn about the frontier of remote experimentation Venkat Viswanathan @ @ @ @ @ Mechanical Engineering at Carnegie Mellon University .
He received his PhD from Stanford University working @ @ @ @ @ current research focus is on understanding and developing novel electrochemical devices for energy storage and @ @

Available to watch now, The Electrochemical Society in partnership with the Royal Society of Chemistry on the discovery of a novel battery electrolyte that was guided by machine-learning software without human intervention
Want to learn more on this subject? Watch now
Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases. We approached the design and selection of a battery electrolyte through a black-box optimization algorithm directly integrated into a robotic test stand. We report here the discovery of a novel battery electrolyte by this experiment completely guided by the machine-learning software without human intervention.
Motivated by the recent trend toward super-concentrated aqueous electrolytes for high-performance batteries, we utilize Dragonfly – a Bayesian machine-learning software package – to search mixtures of commonly used lithium and sodium salts for super-concentrated aqueous electrolytes with wide electrochemical stability windows.
This webinar presented by Venkat Viswanathan, will help the audience to:
Learn about the importance of robotic experimentation
Learn about machine-learning guided design of experiments
Learn about the frontier of remote experimentation
Want to learn more on this subject? Watch now
Venkat Viswanathan is an Associate Professor of Mechanical Engineering at Carnegie Mellon University. He received his PhD from Stanford University working on lithium-air batteries. His current research focus is on understanding and developing novel electrochemical devices for energy storage and utilization.


physicsworld--2020-08-05--Solar flares are predicted by new model - Physics World

A model that predicts when and where large solar flares will occur has been developed by a team led by Kanya Kusano at Japan ’ s Nagoya University .
Their technique works by monitoring regions of high magnetic activity on the Sun ’ s surface and focusses on the instabilities triggered by reconnecting magnetic fields .
Called the “ κ-scheme ” , their model could soon be part of an early warning system for incoming solar storms .
Solar flares are bright flashes on the Sun ’ s surface and are among the most dramatic and fascinating events in the @ @ @ @ @ @ @ trigger them are still unknown , flares are often associated with the “ active regions ” close to visible sunspots .
These regions contain strong magnetic fields that store vast amounts of energy .
When the topologies of these fields change suddenly , this energy is violently released ; often resulting in powerful bursts of X-rays , plasma , and energetic particles .
Known as coronal mass ejections , these bursts can trigger powerful solar storms if they interact with Earth ’ s upper atmosphere – risking the safety of astronauts , spacecraft @ @ @ @ @ @ @ grids and radio communications on Earth .
It is critical , therefore , that we can predict precisely when and where solar flares will occur .
Currently , however , early warning systems are limited in their efficacy because they rely on empirical models that can not fully capture the complex , multi-scale processes associated with solar flare formation .
Kusano ’ s team has taken a new approach based around the process of “ double-arc instability ” .
In their κ-scheme model , two surface regions with opposite magnetic flux are connected @ @ @ @ @ @ @ lines .
Due to shearing , these loops become crossed and reconnect with each other , forming a single , double-arc loop .
This field line then moves upwards as the instability grows , allowing further , smaller pairs of loops to reconnect underneath it .
Over time , this creates a positive feedback loop that ultimately releases vast amounts of energy .
To test κ-scheme , Kusano and colleagues used the model to analyse 205 active regions on the Sun that were monitored by NASA ’ s Solar Dynamics Observatory between 2006-2019 @ @ @ @ @ @ @ were responsible for solar flares powerful enough to trigger long-lasting storms on Earth .
By monitoring the location and time evolution of each region , the κ-scheme accurately predicted when most of these flares would occur , up to 24 h in advance .
The model failed in its prediction of just two flares , which came from one specific active region that produced large flares without any accompanying mass ejections .
Kusano ’ s team now hope to improve the κ-scheme ’ s predictions through upcoming observations from the 4 m Daniel @ @ @ @ @ @ @ began operation in December 2019 .
The instrument will measure the Sun ’ s magnetic field structures and dynamics with unprecedented resolution , potentially allowing the team to produce far better forecasts of when and where flares will occur .
The research is described in Science .

Star burst: a solar flare as seen by NASA's Solar Dynamics Observatory. (Courtesy: NASA/SDO)
A model that predicts when and where large solar flares will occur has been developed by a team led by Kanya Kusano at Japan’s Nagoya University. Their technique works by monitoring regions of high magnetic activity on the Sun’s surface and focusses on the instabilities triggered by reconnecting magnetic fields. Called the “κ-scheme”, their model could soon be part of an early warning system for incoming solar storms.
Solar flares are bright flashes on the Sun’s surface and are among the most dramatic and fascinating events in the solar system. Although the conditions that trigger them are still unknown, flares are often associated with the “active regions” close to visible sunspots. These regions contain strong magnetic fields that store vast amounts of energy. When the topologies of these fields change suddenly, this energy is violently released; often resulting in powerful bursts of X-rays, plasma, and energetic particles.
Known as coronal mass ejections, these bursts can trigger powerful solar storms if they interact with Earth’s upper atmosphere – risking the safety of astronauts, spacecraft and satellites, as well as electrical grids and radio communications on Earth. It is critical, therefore, that we can predict precisely when and where solar flares will occur. Currently, however, early warning systems are limited in their efficacy because they rely on empirical models that cannot fully capture the complex, multi-scale processes associated with solar flare formation.
Double-arc loop
Kusano’s team has taken a new approach based around the process of “double-arc instability”. In their κ-scheme model, two surface regions with opposite magnetic flux are connected by two current-carrying loops of magnetic field lines. Due to shearing, these loops become crossed and reconnect with each other, forming a single, double-arc loop. This field line then moves upwards as the instability grows, allowing further, smaller pairs of loops to reconnect underneath it. Over time, this creates a positive feedback loop that ultimately releases vast amounts of energy.
To test κ-scheme, Kusano and colleagues used the model to analyse 205 active regions on the Sun that were monitored by NASA’s Solar Dynamics Observatory between 2006-2019. Overall, seven of these regions were responsible for solar flares powerful enough to trigger long-lasting storms on Earth. By monitoring the location and time evolution of each region, the κ-scheme accurately predicted when most of these flares would occur, up to 24 h in advance.
The model failed in its prediction of just two flares, which came from one specific active region that produced large flares without any accompanying mass ejections. Kusano’s team now hope to improve the κ-scheme’s predictions through upcoming observations from the 4 m Daniel K Inouye Solar Telescope, which first began operation in December 2019. The instrument will measure the Sun’s magnetic field structures and dynamics with unprecedented resolution, potentially allowing the team to produce far better forecasts of when and where flares will occur.
The research is described in Science.


physicsworld--2020-08-05--Novel X-ray technology depicts real-time airflow through the lungs - Physics World

The ability to quantify and localize spatial variations in lung function could help doctors more effectively diagnose , monitor and treat many respiratory diseases .
A multidisciplinary team of researchers in Australia has developed a novel tool for measuring regional lung function and used it to detect and characterize a disease in mice similar to cystic fibrosis ( CF ) .
CF is a hereditary disease that causes the body to make abnormally thick and sticky mucus .
This mucus can hinder breathing and lead to severe lung infections and permanent damage .
CF progressively reduces quality-of-life and ultimately leads @ @ @ @ @ @ @ by CF is often heterogeneous , or non-uniform , so local treatments targeting patches of damaged tissue can slow the progression of the disease to preserve quality-of-life and lengthen overall survival .
The best method for assessing lung function consists of measuring how much air a patient can inhale and exhale , in addition to how quickly this volume of air is exhaled – a technique called spirometry .
This information is then used to identify breathing patterns commonly associated with respiratory conditions , like CF .
Critically , spirometry only assesses the @ @ @ @ @ @ @ can not localize subtle , non-uniform deficiencies .
Recently , Freda Werdiger from Monash University ’ s Department of Mechanical and Aerospace Engineering led a team of biomedical engineers , physicists , mechanical engineers and respiratory physicians from Adelaide Women ’ s and Children ’ s Hospital and the University of Adelaide to overcome these limitations .
This collaborative team applied X-ray velocimetry ( XV ) to non-invasively generate high-definition and sensitive images of the real-time airflow through the lungs of mice .
XV is a combination of high-speed imaging and post-processing analysis @ @ @ @ @ @ @ the lungs .
To achieve high-speed image acquisition , Werdiger and her team utilized propagation-based phase-contrast X-ray imaging ( PCXI ) .
Unlike conventional radiography , which relies solely on X-ray absorption , PCXI additionally leverages the diffraction of X-rays at material interfaces .
By placing the detector further from the sample than is typical for radiography , diffracted X-rays can interfere with the un-diffracted X-rays .
This produces an interference pattern that highlights structural boundaries .
This results in enhanced image contrast and enables high-resolution images to be acquired in a brief @ @ @ @ @ @ @ , PCXI can rapidly generate detailed , three-dimensional images of fine lung structures .
The post-processing analysis for XV involved the multiple tomographic PCXI images acquired throughout the breathing cycle .
The researchers used a computational method known as particle image velocimetry to calculate displacement vectors between consecutive images and determine the three-dimensional speed and direction of lung motion throughout the breathing cycle .
This provided an entirely non-invasive way to calculate the volume of air that flows through the individual airways of the lungs .
The researchers tested the feasibility of using @ @ @ @ @ @ @ of respiratory function in a cohort of mice with CF-like lung disease and their healthy littermates .
Their results were recently published in Scientific Reports .
First , they generated maps of regional lung expansion for both healthy and diseased mice .
These maps clearly showed the presence and location of confined areas of airflow deficits in the diseased animals , a characteristic of the patchy nature of CF .
Next , the researchers developed a method to quantify the distribution of disease in individual mice .
Their novel quantification correlated well with @ @ @ @ @ @ @ , while offering a higher level of specificity .
“ This study shows how researchers from many different backgrounds can come together in collaboration and work together to improve the lives of people living with this terrible disease , ” says Werdiger .
“ I hope to see XV being used to diagnose and monitor many different diseases , ” she added , a wish that is likely to be realised .
With its recent commercialization by Andreas Fouras of 4DMedical , this technology is well positioned to assist in global research and @ @ @ @ @ @ @ and quality-of-lives of people with CF and other respiratory diseases .

Freda Werdiger in the lab with the X-ray velocimetry system. (Courtesy: Freda Werdiger)
The ability to quantify and localize spatial variations in lung function could help doctors more effectively diagnose, monitor and treat many respiratory diseases. A multidisciplinary team of researchers in Australia has developed a novel tool for measuring regional lung function and used it to detect and characterize a disease in mice similar to cystic fibrosis (CF).
CF is a hereditary disease that causes the body to make abnormally thick and sticky mucus. This mucus can hinder breathing and lead to severe lung infections and permanent damage. CF progressively reduces quality-of-life and ultimately leads to premature death. Lung damage caused by CF is often heterogeneous, or non-uniform, so local treatments targeting patches of damaged tissue can slow the progression of the disease to preserve quality-of-life and lengthen overall survival.
The best method for assessing lung function consists of measuring how much air a patient can inhale and exhale, in addition to how quickly this volume of air is exhaled – a technique called spirometry. This information is then used to identify breathing patterns commonly associated with respiratory conditions, like CF. Critically, spirometry only assesses the global health of the entire lung and cannot localize subtle, non-uniform deficiencies.
Recently, Freda Werdiger from Monash University’s Department of Mechanical and Aerospace Engineering led a team of biomedical engineers, physicists, mechanical engineers and respiratory physicians from Adelaide Women’s and Children’s Hospital and the University of Adelaide to overcome these limitations. This collaborative team applied X-ray velocimetry (XV) to non-invasively generate high-definition and sensitive images of the real-time airflow through the lungs of mice.
X-ray velocimetry
XV is a combination of high-speed imaging and post-processing analysis that produces a detailed ventilation map of the lungs.
To achieve high-speed image acquisition, Werdiger and her team utilized propagation-based phase-contrast X-ray imaging (PCXI). Unlike conventional radiography, which relies solely on X-ray absorption, PCXI additionally leverages the diffraction of X-rays at material interfaces.
The interference pattern that enables edge enhancement in propagation-based phase-contrast X-ray imaging. (Courtesy: CC BY-SA 3.0/Mcm222)
By placing the detector further from the sample than is typical for radiography, diffracted X-rays can interfere with the un-diffracted X-rays. This produces an interference pattern that highlights structural boundaries. This results in enhanced image contrast and enables high-resolution images to be acquired in a brief time.
When combined with tomography methods, PCXI can rapidly generate detailed, three-dimensional images of fine lung structures.
The post-processing analysis for XV involved the multiple tomographic PCXI images acquired throughout the breathing cycle. The researchers used a computational method known as particle image velocimetry to calculate displacement vectors between consecutive images and determine the three-dimensional speed and direction of lung motion throughout the breathing cycle. This provided an entirely non-invasive way to calculate the volume of air that flows through the individual airways of the lungs.
Characterization of CF-like disease in mice
The researchers tested the feasibility of using XV to acquire reliable and quantitative measures of respiratory function in a cohort of mice with CF-like lung disease and their healthy littermates. Their results were recently published in Scientific Reports.
First, they generated maps of regional lung expansion for both healthy and diseased mice. These maps clearly showed the presence and location of confined areas of airflow deficits in the diseased animals, a characteristic of the patchy nature of CF.
Map of the fractional regional lung expansion at peak inspiration relative to the lung volume at base exhale. Coronal slices for (a) a mouse with CF-like disease and (b) a healthy littermate. The red arrow indicates a region of airflow deficit. (Courtesy: Monash University)
Next, the researchers developed a method to quantify the distribution of disease in individual mice. Their novel quantification correlated well with measurements by the conventional forced oscillation technique, while offering a higher level of specificity.
“This study shows how researchers from many different backgrounds can come together in collaboration and work together to improve the lives of people living with this terrible disease,” says Werdiger. “I hope to see XV being used to diagnose and monitor many different diseases,” she added, a wish that is likely to be realised. With its recent commercialization by Andreas Fouras of 4DMedical, this technology is well positioned to assist in global research and clinical care that could improve the length and quality-of-lives of people with CF and other respiratory diseases.


physicsworld--2020-08-05--Superconductivity theory comes a step closer - Physics World

How do materials that are normally insulators manage to conduct electricity without resistance ?
Physicists have been debating this basic question about cuprates – ceramic compounds made up of layers of copper and oxygen atoms , interleaved with atoms of other elements – for more than three decades and have yet to come up with a satisfactory answer .
Now , however , researchers in the US have put forward a model that at least provides clues as to what such an answer might look like , based on studies of so-called “ overdoped ” cuprates .
In 1986 , @ @ @ @ @ @ @ , by tweaking cuprates ’ chemical composition , they could make these normally-insulating materials superconduct .
What is more , the superconducting transition temperature of the doped cuprates far exceeded those of previously-recognized metallic superconductors – meaning that the established Bardeen–Cooper–Schrieffer ( BCS ) theory of low-temperature superconductors must be incomplete .
This finding led to much excitement , and subsequent studies succeeded in pushing the cuprates ’ superconducting transition temperature up to around 150K .
More recently , other materials – hydrides subject to very high pressure – have been found to @ @ @ @ @ @ @ the dream of room-temperature superconductivity remains elusive , and – frustratingly – physicists still haven ’ t agreed on a theory to explain the source of cuprates ’ superconductivity .
Multiple studies have shown that cuprates superconduct when their electrons jump from one copper atom to another .
Because electrons strongly repel each other , however , they usually exist in a static formation , with one electron sitting immobile on each atom .
Undoped , therefore , cuprates remain in an insulating , antiferromagnetic state known as a Mott insulator , with @ @ @ @ @ @ @ When a cuprate is doped with atoms of other elements such as lanthanum , strontium or yttrium , some of its electrons are replaced with holes , freeing the remaining electrons to flow without resistance .
Too much doping , though , and the effect reverses : beyond a specific concentration of dopant atoms , the cuprate ’ s transition temperature drops until it eventually becomes a metal .
The latest research focuses on these “ overdoped ” cuprates , putting forward a model that aims to reconcile some apparently contradictory experimental results @ @ @ @ @ @ @ become superconducting when electrons partially overcome their mutual repulsion by creating what are known as Cooper pairs .
These pairs form thanks to vibrations in the metallic crystal lattice : when one electron draws slower-moving ions towards it as it passes through the lattice , it generates a small concentration of positive charge that then pulls another electron in behind it .
Since electrons are fermions , they obey the Pauli exclusion principle , forming a Fermi gas in which the system ’ s energy levels fill from the bottom up with one @ @ @ @ @ @ @ The boundary between the highest occupied energy level and the next is known as the Fermi surface , and the electrons that form Cooper pairs are those nearest this surface .
In a metallic superconductor , all electrons contribute to superconductivity , not just those bound up as Cooper pairs .
In practice , this means that the density of superconducting electrons should be similar to the overall electron density .
However , in 2016 Ivan Božović and colleagues at the Brookhaven National Laboratory in the US showed that this is not the @ @ @ @ @ @ @ after devising a new way of preparing overdoped copper oxides ( which are chemically unstable ) , they found that the density of superconducting electrons in these materials is actually far lower than would be expected from BCS theory .
The new model seeks to explain this disparity .
In doing so , it takes inspiration from work carried out by Japanese physicists Yasuhiro Hatsugai and Mahito Kohmoto .
In the early 1990s , Hatsugai and Kohmoto proposed a way of overcoming the apparent contradiction between the statistics of a Fermi gas ( @ @ @ @ @ @ @ and spin ) and a Mott insulator ( which allow just one electron at each site ) .
Their solution was to introduce what is , in effect , a new repulsive interaction that prohibits two electrons with opposite spins from occupying the same momentum state .
Philip Phillips and colleagues at the University of Illinois at Urbana-Champaign have now revised this model by adding a further interaction that draws electrons together to create Cooper pairs .
As they report in Nature Physics , this model includes a Fermi surface , but also @ @ @ @ @ @ @ with a normal Fermi gas – bringing it more in line with the observations made by Božović and colleagues at Brookhaven .
Jan Zaanen of the University of Leiden in the Netherlands , who was not involved in the present work , points out that the Urbana-Champaign team ’ s modelled density falls off more gradually with increased doping than was observed in the Brookhaven experiment .
Nevertheless , he believes that the new scheme provides a “ proof of principle ” for showing how a non-Fermi liquid could have a Fermi surface @ @ @ @ @ @ @ s model and the Phillips ’ group ’ s adaptation posit new interactions between electrons , while also describing those electrons as waves rather than particles , Zaanen acknowledges that it is hard to envisage them as real physical descriptions of what is taking place .
However , he points out that a similar problem occurs in the well-established theory of Lev Landau that describes helium-3 and other systems in terms of strongly-interacting fermions .
As Zaanen explains , Landau ’ s scheme relies on emergent physics involving highly collective quasiparticles .
A @ @ @ @ @ @ @ be needed to understand overdoped cuprates .
“ Although nonsense on the microscopic scale , ” he says , “ the Hatsugai-Kohmoto interactions may spring into existence in the collectivization process to become eventually literal on the macroscopic scale ” .

Spin statistics: A new theoretical model of cuprate superconductivity seeks to overcome differences in the statistical behaviour of electrons in different states. (Courtesy: Inna Bigun/Shutterstock)
How do materials that are normally insulators manage to conduct electricity without resistance? Physicists have been debating this basic question about cuprates – ceramic compounds made up of layers of copper and oxygen atoms, interleaved with atoms of other elements – for more than three decades and have yet to come up with a satisfactory answer. Now, however, researchers in the US have put forward a model that at least provides clues as to what such an answer might look like, based on studies of so-called “overdoped” cuprates.
In 1986, Georg Bednorz and Alex Müller discovered that, by tweaking cuprates’ chemical composition, they could make these normally-insulating materials superconduct. What is more, the superconducting transition temperature of the doped cuprates far exceeded those of previously-recognized metallic superconductors – meaning that the established Bardeen–Cooper–Schrieffer (BCS) theory of low-temperature superconductors must be incomplete.
This finding led to much excitement, and subsequent studies succeeded in pushing the cuprates’ superconducting transition temperature up to around 150K. More recently, other materials – hydrides subject to very high pressure – have been found to superconduct at even higher temperatures. Yet the dream of room-temperature superconductivity remains elusive, and – frustratingly – physicists still haven’t agreed on a theory to explain the source of cuprates’ superconductivity.
Jumping electrons
Multiple studies have shown that cuprates superconduct when their electrons jump from one copper atom to another. Because electrons strongly repel each other, however, they usually exist in a static formation, with one electron sitting immobile on each atom. Undoped, therefore, cuprates remain in an insulating, antiferromagnetic state known as a Mott insulator, with neighbouring electrons having opposite magnetic spins.
When a cuprate is doped with atoms of other elements such as lanthanum, strontium or yttrium, some of its electrons are replaced with holes, freeing the remaining electrons to flow without resistance. Too much doping, though, and the effect reverses: beyond a specific concentration of dopant atoms, the cuprate’s transition temperature drops until it eventually becomes a metal. The latest research focuses on these “overdoped” cuprates, putting forward a model that aims to reconcile some apparently contradictory experimental results.
Contradictory behaviour
According to BCS theory, metals become superconducting when electrons partially overcome their mutual repulsion by creating what are known as Cooper pairs. These pairs form thanks to vibrations in the metallic crystal lattice: when one electron draws slower-moving ions towards it as it passes through the lattice, it generates a small concentration of positive charge that then pulls another electron in behind it.
Since electrons are fermions, they obey the Pauli exclusion principle, forming a Fermi gas in which the system’s energy levels fill from the bottom up with one electron per lattice site and spin. The boundary between the highest occupied energy level and the next is known as the Fermi surface, and the electrons that form Cooper pairs are those nearest this surface.
In a metallic superconductor, all electrons contribute to superconductivity, not just those bound up as Cooper pairs. In practice, this means that the density of superconducting electrons should be similar to the overall electron density. However, in 2016 Ivan Božović and colleagues at the Brookhaven National Laboratory in the US showed that this is not the case for overdoped cuprates. Instead, after devising a new way of preparing overdoped copper oxides (which are chemically unstable), they found that the density of superconducting electrons in these materials is actually far lower than would be expected from BCS theory.
Explaining the disparity
The new model seeks to explain this disparity. In doing so, it takes inspiration from work carried out by Japanese physicists Yasuhiro Hatsugai and Mahito Kohmoto. In the early 1990s, Hatsugai and Kohmoto proposed a way of overcoming the apparent contradiction between the statistics of a Fermi gas (which stipulate one electron per lattice site and spin) and a Mott insulator (which allow just one electron at each site). Their solution was to introduce what is, in effect, a new repulsive interaction that prohibits two electrons with opposite spins from occupying the same momentum state.
Philip Phillips and colleagues at the University of Illinois at Urbana-Champaign have now revised this model by adding a further interaction that draws electrons together to create Cooper pairs. As they report in Nature Physics, this model includes a Fermi surface, but also a much-reduced density of superfluid electrons compared with a normal Fermi gas – bringing it more in line with the observations made by Božović and colleagues at Brookhaven.
Jan Zaanen of the University of Leiden in the Netherlands, who was not involved in the present work, points out that the Urbana-Champaign team’s modelled density falls off more gradually with increased doping than was observed in the Brookhaven experiment. Nevertheless, he believes that the new scheme provides a “proof of principle” for showing how a non-Fermi liquid could have a Fermi surface.
Since both Hatsugai and Kohmoto’s model and the Phillips’ group’s adaptation posit new interactions between electrons, while also describing those electrons as waves rather than particles, Zaanen acknowledges that it is hard to envisage them as real physical descriptions of what is taking place. However, he points out that a similar problem occurs in the well-established theory of Lev Landau that describes helium-3 and other systems in terms of strongly-interacting fermions.
As Zaanen explains, Landau’s scheme relies on emergent physics involving highly collective quasiparticles. A similar logic, he concludes, may be needed to understand overdoped cuprates. “Although nonsense on the microscopic scale,” he says, “the Hatsugai-Kohmoto interactions may spring into existence in the collectivization process to become eventually literal on the macroscopic scale”.


physicsworld--2020-08-05--NASCAR: the science of racing safely - Physics World

Ryan Newman must have been feeling pretty good as he rounded turn four on the last lap of the Daytona 500 on 16 February this year .
The 42-year-old NASCAR driver was not only poised to break a 104-race winless streak , he was about to do it at the season ’ s most prestigious race .
Newman ’ s car was hit from behind and turned .
It slammed into the outside track wall so hard that it flipped over , then slid – upside down – back into traffic , where an oncoming car smashed it into the @ @ @ @ @ @ @ s battered # 6 car slid a couple of hundred metres on its roof before finally coming to a stop .
No NASCAR driver has died or been permanently injured in any of its top three series since 2001 .
As Newman was helicoptered off by emergency personnel , I couldn ’ t help but fear that this streak was about to end .
But 42 hours later , Ryan Newman walked out of the hospital .
It wasn ’ t until NASCAR cleared him to race again at the end of April @ @ @ @ @ @ @ about the crash .
Three months and one day after his accident , Newman finished 15th in the first race since the coronavirus shut down NASCAR in March .
The National Association for Stock Car Auto Racing – NASCAR – grew out of the illegal liquor trade in the south-east US .
Moonshine runners who had souped up their “ stock ” cars to outrun the law started racing each other .
Today , NASCAR ’ s top level , the NASCAR Cup Series , runs 36 races each season at ovals and @ @ @ @ @ @ @ tracks are about 800 m and the longest 4.1 km , with race lengths ranging from 500 to 965 km .
The racecars themselves have gone through six generations of development , each featuring major changes and improvements from its predecessor .
However , since its founding in 1947 , some 32 NASCAR drivers have died during racing , qualifying , practice and testing in the top three series – 28 in the Cup Series alone .
In the past , NASCAR was mostly reactive when it came to safety , and some @ @ @ @ @ @ @ innovations came from drivers themselves .
That changed after four deaths over a 10-month period in 2000–2001 .
The string of tragedies started with Adam Petty , the fourth generation of a legendary NASCAR family , and ended with Dale Earnhardt ’ s death at the 2001 Daytona 500 .
As NASCAR then started developing its fifth-generation racecar following Earnhardt ’ s death , the newly created NASCAR R & D Center was charged with making safety a top priority .
Nowadays , Newman – a Purdue University-educated engineer – is one of @ @ @ @ @ @ @ , especially when it comes to superspeedways like Daytona .
The 4 km tri-oval ( a cross between a triangle and an oval ) has long straightaways and 31° banked turns that make it one of the fastest tracks in NASCAR .
In fact , it ’ s a little too fast .
At high speed and high yaw angle ( the angle between the front of the car and the direction the car is heading ) , the laws of aerodynamics make stock cars behave more like aeroplanes than automobiles .
Over @ @ @ @ @ @ @ have died at Daytona , including Earnhardt – more than at any other track .
After Bobby Allison ’ s car became airborne and almost went over the catchfence at Daytona ’ s sister track , Talladega Superspeedway , in 1987 , NASCAR mandated “ restrictor plates ” at both tracks .
These limit airflow into the engine , which in turn restricts how much fuel can combust , therefore capping the car ’ s speed .
But even with the engine throttled back to about 410 kW ( 550 horsepower ) , @ @ @ @ @ @ @ km/h ( ~200 mph ) .
Yet , this is still not fast enough for racers .
The only way to get more speed out of a car with a restricted engine is to follow another so closely that air flows over both cars as if they were one .
The decrease in net drag from this “ drafting ” lets two cars move 5–8 km/h ( 3–5 mph ) faster than either car could alone .
But when packs of cars travel centimetres from each other at speeds high enough to traverse @ @ @ @ @ @ @ an eye , even one tiny misjudgement can set off a massive chain-reaction crash .
Racecar crashes are more dangerous than street-car accidents because racecars have so much more kinetic energy .
A typical passenger car going 110 m/s ( ~70 mph ) has 0.5 MJ of kinetic energy .
A NASCAR racecar at top speed carries 12 times that – about the energy stored in 1.4 kg of TNT .
When a racecar stops , all this kinetic energy must be converted to other forms of energy .
This happens over a @ @ @ @ @ @ @ in for a pitstop .
Kinetic energy changes into heat ( e.g .
in tyres and brakes ) , sound ( squealing brakes and screeching tyres ) and light ( glowing brake rotors ) .
In a crash , energy is converted over a much shorter period of time , which leads to higher peak forces .
In addition to heat , light and sound , energy may go into deforming the car , or producing rotational motion like spinning or flipping .
Managing that energy is the key to keeping drivers safe @ @ @ @ @ @ @ made by putting roll cages inside ordinary road vehicles .
As speeds increased and aerodynamics became more important , it was easier ( and safer ) to build the car around the roll cage .
The fifth-generation racecar marked the first time NASCAR sent a computer-aided design file of the chassis to teams .
While each team was allowed to individually develop features such as suspension set-up , NASCAR specified the dimensions , positions and wall thicknesses of every single tube in the chassis to ensure safety standards were kept across all teams @ @ @ @ @ @ @ chassis a team makes over the course of a season is inspected by NASCAR using digital indexing arms and 3D laser scanning .
Those that pass are certified with tamperproof radio-frequency identification ( RFID ) tags that are checked each time the car is presented for competition .
The strongest tubes in the chassis are closest to the driver , allowing the parts of the car furthest away to crush first .
The front section is designed to push the engine downward instead of into the driver ’ s compartment in the event @ @ @ @ @ @ @ doorbars on each side are staggered so that the top bar crushes first , then the next one down and so on , and they ’ re covered by a metal anti-intrusion plate .
The car ’ s “ greenhouse ” ( the windscreen , side and rear windows , roof , and the structure supporting it all ) poses a more formidable challenge .
The driver needs a clear field of vision and an ability to escape the car quickly in the event of fire .
That requires a design providing maximum @ @ @ @ @ @ @ components .
The windows themselves are made of high-strength laminated polycarbonate .
While they can fracture , they are very hard to break , and because there is a layer of polymer film between the two sections , and adhesive mylar tear-offs on the front , any broken pieces can ’ t go flying too far .
An “ Earnhardt Bar ” was mandated after a 1996 Talladega crash in which another car ’ s nose crashed through Earnhardt ’ s windscreen and broke his sternum .
This bar runs vertically down the @ @ @ @ @ @ @ large – like the nose of another car – from getting into the driver ’ s cockpit .
The current Gen-6 car , introduced in 2013 , also features a “ Newman Bar ” that provides extra roof reinforcement .
The bar was named after Newman , who was vocal in calling for stronger roofs after going airborne twice in one year at Talladega .
It then helped saved his life in the Daytona 2020 crash .
The Gen-6 car also enlarged the greenhouse to put more distance between the roof and the @ @ @ @ @ @ @ ’ s seat was moved 5 cm away from the door for extra protection against T-bone crashes , in which the nose of one car perpendicularly impacts the driver ’ s-side door of the other .
NASCAR chassis are built from magnetic steel , rather than materials like titanium aluminides to keep the cost down , but there are plenty of hi-tech materials protecting the driver .
The thermoplastic composite Tegris ( made by the US materials manufacturer Milliken & Co. ) was first used for the newly introduced “ splitter ” on @ @ @ @ @ @ @ the bottom of the front bumper and creating higher pressure on top than on the bottom , the device “ splits ” the air .
The increased force on the car ’ s front end provides more friction – or “ grip ” – between front tyres and the track .
A spoiler accomplishes the same task in the rear .
Tegris starts out as a cold-drawn , polypropylene oxide ( PPO ) tape yarn that is then woven into sheets .
Unlike carbon-fibre composites , Tegris doesn ’ t require resin .
@ @ @ @ @ @ @ PPO core surrounded by an amorphous coating .
Because the amorphous material melts at a lower temperature than crystalline PPO , the amorphous sections fuse together when the fabric is heated under pressure .
Although Tegris has about 70 % the strength of carbon fibre , it is more impact-resistant , lighter and about a tenth of the price .
A sheet of Tegris on the driver ’ s-side door over the chassis frame prevents anything from getting through the doorbars and into the cockpit .
Before the exterior sheet metal goes on @ @ @ @ @ @ @ highly engineered , extruded thermoplastic foam – are fitted over the doors .
In contrast to squishy foams that extend the time of collision and decrease the peak force , IMPAXX is rigid .
Rather than temporarily storing energy , the stiff foam deforms , absorbing energy equal to the applied force multiplied by the foam ’ s displacement .
The energy used to crush the foam is energy that never reaches the driver .
Everything inside the car is also optimized to manage kinetic energy .
In fact , today ’ s @ @ @ @ @ @ @ like something you ’ d find on a rocket than in a car .
Custom-poured resilient-foam inserts conform to the driver ’ s body and padded arms wrap around the driver ’ s shoulders , pelvis and head to minimize side-to-side and backward motion .
One thing seats can ’ t protect against , however , is forward motion .
NASCAR requires a six-point driver restraint harness , with wider belts than on a street car to distribute force better .
One polyester belt runs over each shoulder , another two hold the @ @ @ @ @ @ @ around the legs to prevent “ submarining ” – the driver slipping under the belt in an impact .
Racing restraint belts are sturdier than passenger-car belts because they must tolerate higher forces , but they still stretch a little in an impact to lengthen the time over which the driver comes to a stop .
In the 1990s restraint belts had been improved so that they held drivers ’ bodies tightly to their seats during a crash , but there wasn ’ t anything holding their heads .
A typical human head @ @ @ @ @ @ @ with a 1.3 kg helmet , and in a 40g collision ( deceleration at 392 m/s2 ) , that head can snap forward at an acceleration of 107g ( almost 1050 m/s2 ) – more than enough to break the driver ’ s neck .
Basilar skull fractures – caused by a driver ’ s head whipping forward – became one of the most common causes of on-track deaths , including that of Earnhardt .
Since 2001 NASCAR has mandated the use of head-and-neck restraints .
These devices consist of a harness worn @ @ @ @ @ @ @ , with tethers that connect to the helmet .
When the car stops suddenly , the tethers stop the head from snapping forward and instead slow it down gradually .
Solving one problem , however , often uncovers others .
In the last few years , Dale Earnhardt Jr – the late Earndardt ’ s son who retired from NASCAR racing in 2017 – has raised awareness of crash-induced concussions , which has caused many drivers to re-examine their helmet choice .
In fact , the 2020 Daytona 500 was only the second @ @ @ @ @ @ @ a new type of helmet .
His head gear came from Arai , a company specializing in the more demanding needs of motorcycle racers , who have only their helmet between them and the track .
Helmets must serve a range of purposes : they need to be fireproof , cushioned to minimize forces on the head , and hard enough to prevent anything from piercing or crushing them .
They also need to be as light as possible .
Arai uses a proprietary combination of fibres and resin to create a fibreglass @ @ @ @ @ @ @ fibreglass but weighing just 0.5–0.6 kg rather than the 1.3–1.5 kg of a typical motorsports helmet .
Newman ’ s helmet also incorporated a layer of Zylon ( a thermoset liquid-crystalline polyoxazole ) around the crown area .
Zylon , which has a tensile strength 1.6 times that of Kevlar , is just starting to appear in NASCAR , although Formula 1 has used Zylon tethers since 2001 to prevent wheels that come off cars in crashes from becoming projectiles .
Indeed , Formula 1 also requires Zylon reinforcement in all helmets after @ @ @ @ @ @ @ car and hit driver Felipe Massa ’ s helmet in 2009 , penetrating the headgear and fracturing his skull .
Newman and his team made his car as safe as possible .
But there ’ s one more element that played a big role in helping him survive the accident .
Originally , concrete racetrack walls and the wire catchfences that followed were designed to keep cars out of the grandstands rather than to protect drivers .
But by 1998 , a staggering 41 drivers had died at Indianapolis Motor Speedway ( which @ @ @ @ @ @ @ through collisions with walls and other cars – some of the latter caused by track walls redirecting the car back into the traffic .
Tony George , then-president of IndyCar ( another US racing series ) and owner of the speedway , set out to make the track safer .
The probability of two cars crashing into the same spot within moments of each other on a motorway is low , so barriers that cushion an impact by breaking or permanently deforming are sufficient .
But they won ’ t work on a @ @ @ @ @ @ @ be involved in a single accident .
In addition , a racetrack barrier must be quickly repairable ( and quickly cleaned up ) so the race can continue .
The first instinct was to make the walls softer .
Many tracks still use bundled-up old tyres , which works well for lower-energy impacts , but not the high-speed collisions at Indianapolis .
In 1998 IndyCar tested 40 cm-diameter , high-density polyethylene ( HDPE ) tubes that were placed in front of the speedway ’ s existing concrete walls and covered by inch-thick overlapping @ @ @ @ @ @ @ Energy Dissipation System ( PEDS ) , it allowed the car ’ s kinetic energy to move the plates and deform the cylinders .
Unfortunately , although the PEDS successfully decreased the accelerations the drivers experienced when hitting the wall , it scattered debris all over the track .
The HDPE also tended to “ grab ” a car when it hit , stopping it instead of slowing it down .
IndyCar engaged Dean Sicking ( an engineer and inventor who was then at the University of Nebraska ) to redesign the PEDS @ @ @ @ @ @ @ ’ t the concept , it was the material .
So instead of using plastic , Sicking proposed steel .
After overcoming plenty of scepticism that a harder material would provide better protection – and with funding from Indycar – he started work on the Steel And Foam Energy Reducing ( SAFER ) barrier .
When NASCAR joined the project a few years later , he had to modify the design because the two types of cars carried different amounts of kinetic energy .
Any barrier had to work for both .
The @ @ @ @ @ @ @ five hollow-steel tubes , each with a 20 cm2 cross section and 0.5-cm-thick wall , stacked one atop another and stitch-welded together .
High-strength nylon straps connect the impact surface to the track ’ s existing concrete wall .
The space between the two walls is filled with 50-cm-thick wedges of foam , with the wedges ’ narrow ends closest to the track .
When a car hits the SAFER barrier , its kinetic energy is used to move the massive steel wall and crush the foam wedges .
The wedge shape helps @ @ @ @ @ @ @ energy scales of different cars .
Indy officials routinely saw 100g and greater peak accelerations when cars hit concrete walls .
The SAFER barrier brought most hits down to the 60–65g range .
Today , SAFER barriers are mandatory on all IndyCar and NASCAR tracks ( except Eldora Speedway , a small dirt track ) .
The walls aren ’ t exactly soft , but they do save lives .
I would never deny that there was an element of luck involved in Ryan Newman being able to walk away from a spectacular @ @ @ @ @ @ @ of the credit goes to the scientists and engineers – and the drivers – who refuse to accept that death is an inevitable part of racing .

Physics World where it appeared under the title "The science of racing safely". Members of the Institute of Physics can enjoy the full issue Taken from the August 2020 issue ofwhere it appeared under the title "The science of racing safely". Members of the Institute of Physics can enjoy the full issue via the Physics World app
Being a racecar driver in NASCAR is a dangerous job. One small mistake can send cars flying, cause multi-car crashes and, in some cases, be fatal. Diandra Leslie-Pelecky explores the science behind some of the features in NASCAR that are designed to keep the drivers safe
Ryan Newman must have been feeling pretty good as he rounded turn four on the last lap of the Daytona 500 on 16 February this year. The 42-year-old NASCAR driver was not only poised to break a 104-race winless streak, he was about to do it at the season’s most prestigious race.
Then everything changed.
Newman’s car was hit from behind and turned. It slammed into the outside track wall so hard that it flipped over, then slid – upside down – back into traffic, where an oncoming car smashed it into the air. After landing, Newman’s battered #6 car slid a couple of hundred metres on its roof before finally coming to a stop.
No NASCAR driver has died or been permanently injured in any of its top three series since 2001. As Newman was helicoptered off by emergency personnel, I couldn’t help but fear that this streak was about to end. But 42 hours later, Ryan Newman walked out of the hospital. It wasn’t until NASCAR cleared him to race again at the end of April that he revealed he remembers almost nothing about the crash. Three months and one day after his accident, Newman finished 15th in the first race since the coronavirus shut down NASCAR in March.
The risk in racing
The National Association for Stock Car Auto Racing – NASCAR – grew out of the illegal liquor trade in the south-east US. Moonshine runners who had souped up their “stock” cars to outrun the law started racing each other. Today, NASCAR’s top level, the NASCAR Cup Series, runs 36 races each season at ovals and a few road courses. The shortest tracks are about 800 m and the longest 4.1 km, with race lengths ranging from 500 to 965 km. The racecars themselves have gone through six generations of development, each featuring major changes and improvements from its predecessor. However, since its founding in 1947, some 32 NASCAR drivers have died during racing, qualifying, practice and testing in the top three series – 28 in the Cup Series alone.
In the past, NASCAR was mostly reactive when it came to safety, and some of the most vocal opposition to safety innovations came from drivers themselves. That changed after four deaths over a 10-month period in 2000–2001. The string of tragedies started with Adam Petty, the fourth generation of a legendary NASCAR family, and ended with Dale Earnhardt’s death at the 2001 Daytona 500. As NASCAR then started developing its fifth-generation racecar following Earnhardt’s death, the newly created NASCAR R&D Center was charged with making safety a top priority.
Nowadays, Newman – a Purdue University-educated engineer – is one of NASCAR’s most vocal safety advocates, especially when it comes to superspeedways like Daytona. The 4 km tri-oval (a cross between a triangle and an oval) has long straightaways and 31° banked turns that make it one of the fastest tracks in NASCAR. In fact, it’s a little too fast. At high speed and high yaw angle (the angle between the front of the car and the direction the car is heading), the laws of aerodynamics make stock cars behave more like aeroplanes than automobiles. Over the years, eight NASCAR Cup drivers have died at Daytona, including Earnhardt – more than at any other track.
After Bobby Allison’s car became airborne and almost went over the catchfence at Daytona’s sister track, Talladega Superspeedway, in 1987, NASCAR mandated “restrictor plates” at both tracks. These limit airflow into the engine, which in turn restricts how much fuel can combust, therefore capping the car’s speed. But even with the engine throttled back to about 410 kW (550 horsepower), the cars routinely reach speeds of 320 km/h (~200 mph).
When packs of cars travel centimetres from each other at speeds above 300 km/h, even one tiny misjudgement can set off a massive chain-reaction crash
Yet, this is still not fast enough for racers. The only way to get more speed out of a car with a restricted engine is to follow another so closely that air flows over both cars as if they were one. The decrease in net drag from this “drafting” lets two cars move 5–8 km/h (3–5 mph) faster than either car could alone. But when packs of cars travel centimetres from each other at speeds high enough to traverse a football field in the blink of an eye, even one tiny misjudgement can set off a massive chain-reaction crash.
Racecar crashes are more dangerous than street-car accidents because racecars have so much more kinetic energy. A typical passenger car going 110 m/s (~70 mph) has 0.5 MJ of kinetic energy. A NASCAR racecar at top speed carries 12 times that – about the energy stored in 1.4 kg of TNT.
When a racecar stops, all this kinetic energy must be converted to other forms of energy. This happens over a timescale of seconds when a car comes in for a pitstop. Kinetic energy changes into heat (e.g. in tyres and brakes), sound (squealing brakes and screeching tyres) and light (glowing brake rotors). In a crash, energy is converted over a much shorter period of time, which leads to higher peak forces. In addition to heat, light and sound, energy may go into deforming the car, or producing rotational motion like spinning or flipping. Managing that energy is the key to keeping drivers safe.
A science experiment on wheels
When NASCAR started, racecars were made by putting roll cages inside ordinary road vehicles. As speeds increased and aerodynamics became more important, it was easier (and safer) to build the car around the roll cage. The fifth-generation racecar marked the first time NASCAR sent a computer-aided design file of the chassis to teams. While each team was allowed to individually develop features such as suspension set-up, NASCAR specified the dimensions, positions and wall thicknesses of every single tube in the chassis to ensure safety standards were kept across all teams. Each of the couple of dozen chassis a team makes over the course of a season is inspected by NASCAR using digital indexing arms and 3D laser scanning. Those that pass are certified with tamperproof radio-frequency identification (RFID) tags that are checked each time the car is presented for competition.
The strongest tubes in the chassis are closest to the driver, allowing the parts of the car furthest away to crush first. The front section is designed to push the engine downward instead of into the driver’s compartment in the event of a collision. The four horizontal doorbars on each side are staggered so that the top bar crushes first, then the next one down and so on, and they’re covered by a metal anti-intrusion plate.
The car’s “greenhouse” (the windscreen, side and rear windows, roof, and the structure supporting it all) poses a more formidable challenge. The driver needs a clear field of vision and an ability to escape the car quickly in the event of fire. That requires a design providing maximum strength with a minimum number of structural components. The windows themselves are made of high-strength laminated polycarbonate. While they can fracture, they are very hard to break, and because there is a layer of polymer film between the two sections, and adhesive mylar tear-offs on the front, any broken pieces can’t go flying too far.
Luck or tech? Ryan Newman survived a major crash in February 2020 (left) thanks to safety features in NASCAR such as the Newman Bar in the chassis (right). (Courtesy: Chris O’Meara / AP / Shutterstock; Terry Renna / AP / Shutterstock)
An “Earnhardt Bar” was mandated after a 1996 Talladega crash in which another car’s nose crashed through Earnhardt’s windscreen and broke his sternum. This bar runs vertically down the centre of the windscreen, preventing anything large – like the nose of another car – from getting into the driver’s cockpit. The current Gen-6 car, introduced in 2013, also features a “Newman Bar” that provides extra roof reinforcement. The bar was named after Newman, who was vocal in calling for stronger roofs after going airborne twice in one year at Talladega. It then helped saved his life in the Daytona 2020 crash. The Gen-6 car also enlarged the greenhouse to put more distance between the roof and the driver’s head. The driver’s seat was moved 5 cm away from the door for extra protection against T-bone crashes, in which the nose of one car perpendicularly impacts the driver’s-side door of the other.
NASCAR chassis are built from magnetic steel, rather than materials like titanium aluminides to keep the cost down, but there are plenty of hi-tech materials protecting the driver. The thermoplastic composite Tegris (made by the US materials manufacturer Milliken & Co.) was first used for the newly introduced “splitter” on the fifth-generation car. Jutting out from the bottom of the front bumper and creating higher pressure on top than on the bottom, the device “splits” the air. The increased force on the car’s front end provides more friction – or “grip” – between front tyres and the track. A spoiler accomplishes the same task in the rear.
Tegris starts out as a cold-drawn, polypropylene oxide (PPO) tape yarn that is then woven into sheets. Unlike carbon-fibre composites, Tegris doesn’t require resin. The drawing process produces a highly oriented PPO core surrounded by an amorphous coating. Because the amorphous material melts at a lower temperature than crystalline PPO, the amorphous sections fuse together when the fabric is heated under pressure. Although Tegris has about 70% the strength of carbon fibre, it is more impact-resistant, lighter and about a tenth of the price. A sheet of Tegris on the driver’s-side door over the chassis frame prevents anything from getting through the doorbars and into the cockpit.
Before the exterior sheet metal goes on, blocks of energy-absorbing IMPAXX – a highly engineered, extruded thermoplastic foam – are fitted over the doors. In contrast to squishy foams that extend the time of collision and decrease the peak force, IMPAXX is rigid. Rather than temporarily storing energy, the stiff foam deforms, absorbing energy equal to the applied force multiplied by the foam’s displacement. The energy used to crush the foam is energy that never reaches the driver.
The final layer of protection
Everything inside the car is also optimized to manage kinetic energy. In fact, today’s aluminium or carbon-fibre driver-containment seats look more like something you’d find on a rocket than in a car. Custom-poured resilient-foam inserts conform to the driver’s body and padded arms wrap around the driver’s shoulders, pelvis and head to minimize side-to-side and backward motion.
One thing seats can’t protect against, however, is forward motion. NASCAR requires a six-point driver restraint harness, with wider belts than on a street car to distribute force better. One polyester belt runs over each shoulder, another two hold the pelvis, while two further belts go around the legs to prevent “submarining” – the driver slipping under the belt in an impact. Racing restraint belts are sturdier than passenger-car belts because they must tolerate higher forces, but they still stretch a little in an impact to lengthen the time over which the driver comes to a stop.
Restraining action NASCAR driver seats comprise foam inserts that conform to the driver’s body and a six-point body harness. (Courtesy: Mary Paulk / HMS Motorsport)
In the 1990s restraint belts had been improved so that they held drivers’ bodies tightly to their seats during a crash, but there wasn’t anything holding their heads. A typical human head weighs around 4.5 kg. Combine that with a 1.3 kg helmet, and in a 40g collision (deceleration at 392 m/s2), that head can snap forward at an acceleration of 107g (almost 1050 m/s2) – more than enough to break the driver’s neck. Basilar skull fractures – caused by a driver’s head whipping forward – became one of the most common causes of on-track deaths, including that of Earnhardt.
Since 2001 NASCAR has mandated the use of head-and-neck restraints. These devices consist of a harness worn around the chest or on the shoulders, with tethers that connect to the helmet. When the car stops suddenly, the tethers stop the head from snapping forward and instead slow it down gradually.
Solving one problem, however, often uncovers others. In the last few years, Dale Earnhardt Jr – the late Earndardt’s son who retired from NASCAR racing in 2017 – has raised awareness of crash-induced concussions, which has caused many drivers to re-examine their helmet choice. In fact, the 2020 Daytona 500 was only the second race in which Ryan Newman was using a new type of helmet. His head gear came from Arai, a company specializing in the more demanding needs of motorcycle racers, who have only their helmet between them and the track.
Helmets must serve a range of purposes: they need to be fireproof, cushioned to minimize forces on the head, and hard enough to prevent anything from piercing or crushing them. They also need to be as light as possible. Arai uses a proprietary combination of fibres and resin to create a fibreglass that is 30% stronger than traditional fibreglass but weighing just 0.5–0.6 kg rather than the 1.3–1.5 kg of a typical motorsports helmet.
Newman’s helmet also incorporated a layer of Zylon (a thermoset liquid-crystalline polyoxazole) around the crown area. Zylon, which has a tensile strength 1.6 times that of Kevlar, is just starting to appear in NASCAR, although Formula 1 has used Zylon tethers since 2001 to prevent wheels that come off cars in crashes from becoming projectiles. Indeed, Formula 1 also requires Zylon reinforcement in all helmets after a 700 g spring came off another car and hit driver Felipe Massa’s helmet in 2009, penetrating the headgear and fracturing his skull.
Newman and his team made his car as safe as possible. But there’s one more element that played a big role in helping him survive the accident.
SAFER tracks
Originally, concrete racetrack walls and the wire catchfences that followed were designed to keep cars out of the grandstands rather than to protect drivers. But by 1998, a staggering 41 drivers had died at Indianapolis Motor Speedway (which is not just used for NASCAR) through collisions with walls and other cars – some of the latter caused by track walls redirecting the car back into the traffic. Tony George, then-president of IndyCar (another US racing series) and owner of the speedway, set out to make the track safer.
The probability of two cars crashing into the same spot within moments of each other on a motorway is low, so barriers that cushion an impact by breaking or permanently deforming are sufficient. But they won’t work on a racetrack, where a dozen cars may be involved in a single accident. In addition, a racetrack barrier must be quickly repairable (and quickly cleaned up) so the race can continue.
The first instinct was to make the walls softer. Many tracks still use bundled-up old tyres, which works well for lower-energy impacts, but not the high-speed collisions at Indianapolis. In 1998 IndyCar tested 40 cm-diameter, high-density polyethylene (HDPE) tubes that were placed in front of the speedway’s existing concrete walls and covered by inch-thick overlapping HDPE plates. Known as the Polyethylene Energy Dissipation System (PEDS), it allowed the car’s kinetic energy to move the plates and deform the cylinders. Unfortunately, although the PEDS successfully decreased the accelerations the drivers experienced when hitting the wall, it scattered debris all over the track. The HDPE also tended to “grab” a car when it hit, stopping it instead of slowing it down.
Absorbing impact The SAFER barrier, which combines hard steel and soft foam, is now mandatory at NASCAR and IndyCar tracks. The newer version of the barrier uses wedge-shaped foam inserts instead of the original flat pieces. (Courtesy: UNebraska)
IndyCar engaged Dean Sicking (an engineer and inventor who was then at the University of Nebraska) to redesign the PEDS, but he realized the problem wasn’t the concept, it was the material. So instead of using plastic, Sicking proposed steel. After overcoming plenty of scepticism that a harder material would provide better protection – and with funding from Indycar – he started work on the Steel And Foam Energy Reducing (SAFER) barrier. When NASCAR joined the project a few years later, he had to modify the design because the two types of cars carried different amounts of kinetic energy. Any barrier had to work for both.
The track-facing part of a SAFER barrier has five hollow-steel tubes, each with a 20 cm2 cross section and 0.5-cm-thick wall, stacked one atop another and stitch-welded together. High-strength nylon straps connect the impact surface to the track’s existing concrete wall. The space between the two walls is filled with 50-cm-thick wedges of foam, with the wedges’ narrow ends closest to the track.
When a car hits the SAFER barrier, its kinetic energy is used to move the massive steel wall and crush the foam wedges. The wedge shape helps the wall respond to the different kinetic energy scales of different cars. Indy officials routinely saw 100g and greater peak accelerations when cars hit concrete walls. The SAFER barrier brought most hits down to the 60–65g range. Today, SAFER barriers are mandatory on all IndyCar and NASCAR tracks (except Eldora Speedway, a small dirt track). The walls aren’t exactly soft, but they do save lives.
I would never deny that there was an element of luck involved in Ryan Newman being able to walk away from a spectacular crash with minimal injury. But most of the credit goes to the scientists and engineers – and the drivers – who refuse to accept that death is an inevitable part of racing.