Why Does the Government Invest in Clean Energy Innovation?

By UNIVERSITY OF CALIFORNIA - BERKELEY OCTOBER 15, 2022

The study also found that current levels of funding are insufficient to help meet climate goals

The study indicates that the primary drivers behind governmental investment in energy innovation are international collaboration and technological competition with China.

Some European nations have started to reduce their use of oil and natural gas as the ongoing Russian invasion of Ukraine continues to put pressure on the world’s energy supplies. However, some nations have sought to increase domestic fossil fuel production in order to reduce costs and alleviate their current fuel shortage.

That strategy is incompatible with the emissions reductions required to achieve the Paris Agreement’s 2-degree climate goal. In order to meet climate targets, we must fundamentally transform how we supply and use energy, which is a challenge that can only be solved through energy innovation.

A new study headed by experts at the University of California, Berkeley, and the University of Cambridge provides insight into the trajectory of energy research, development, and demonstration (RD&D), which might help policymakers recalibrate their strategy to drive innovation. The results, published recently in the journal Nature Energy, demonstrate that participation in Mission Innovation, a new type of international collaboration, and rising technical competition from China are the most powerful drivers of funding for clean energy research and development.

“By contrast, we do not find that stimulus spending after the financial crisis was associated with a boost in clean energy funding,” said Jonas Meckling, a UC Berkeley professor in the Department of Environmental Science, Policy, and Management and first author of the study.

Monitoring growth and change

Tracking the evolution and variation in “new clean” technologies — a category that includes renewables like solar and wind, hydrogen fuel cells, and improvements in energy efficiency and storage — is central to understanding if energy innovation funding is on track to help achieve emissions reductions needed to achieve the Paris climate goals.

Estimates from the International Energy Agency (IEA) indicate that 35% of global emissions reductions rely on prototype technology or innovations that haven’t been fully deployed. Reaching net zero within the global economy will require long-term financial commitments by governments to develop substitutes for fossil fuels.

To conduct their analysis, Meckling and co-authors from the University of Cambridge, Harvard University and the Chinese Academy of Sciences created two datasets: one tracked RD&D funding from China, India, and IEA member countries; the other inventoried 57 public energy innovation institutions relating to decarbonization across eight major economies. They found that energy funding among seven of the eight major economies grew from $10.9 billion to $20.1 billion between 2001 and 2018, an 84-percent increase. “But even though new clean energy funding has grown significantly, it has diverted RD&D funding from nuclear technologies and not from fossil fuel,” said Meckling.

Within that time period, the analysis found, funding for nuclear energy RD&D fell from 42 percent of all money spent to 24 percent. Fossil fuels remain deeply ingrained in public energy RD&D, particularly in China, which increased its spending on fossil fuel RD&D from $90 million in 2001 to $1.673 billion in 2018. That level of investment in clean energy innovation remains insufficient to achieve a meaningful level of global emissions reduction, according to University of Cambridge professor of climate change policy Laura Diaz Anadon.

“Annual funding for public energy RD&D would have needed to have at least doubled between 2010 and 2020 to enable future energy emissions cuts approximately consistent with the 2-degree Celsius goal,” she said.

But even with the growing public investment in clean energy technologies, the authors found that the public institutions tasked with funding, coordinating, and performing RD&D are not transforming at a pace fast enough to facilitate rapid decarbonization. They are also not focusing enough on commercializing clean energy technologies.

“While we have seen the creation of a lot of new energy innovation agencies since 2000, they experimented only marginally with designs that bridge lab to market and manage only a fraction of total energy RD&D funding,” said co-author Esther Shears, a Ph.D. candidate in UC Berkeley’s Energy and Resources Group.

The authors also found that over the last decade, major economies — in particular the U.S., Germany, and Japan — increased their clean energy RD&D funding most, while emerging economies have been losing momentum, though China remains the second-largest contributor. The trend could widen the energy innovation gap between major economies and the rest of the world.

Explaining shifts in RD&D

The researchers were initially uncertain about what drives the expansion of public energy RD&D funding and the transformation of institutions. Past analysis has focused on energy prices.

“Oil prices can be a driver for governments to spend more on energy innovation because you want to look at alternative technologies if it’s costly to use oil,” said Clara Galeazzi, co-author and postdoctoral fellow at Harvard University, who pointed to alternative energy investments following global price shocks of the 1970s and 2000s. “But clean energy RD&D continued to grow even after oil prices declined, which required us to think about other drivers.”

In tracking the last two decades of energy funding among major economies, the authors holistically evaluated how the “3 Cs”—financial crisis, international cooperation through Mission Innovation, and technology competition from China — transformed public energy funding and institutions.

“We show that Mission Innovation is associated with major economies scaling their clean energy RD&D funding,” said Shears. “Technological competition with China also matters, as it creates an incentive to invest in future growth sectors where China has taken a lead — including various clean energy technologies.”

Stimulus spending after economic crises like the Great Recession (2007-09) did little to boost clean energy efforts. Instead, the authors found that economic recovery funds typically boosted RD&D funding for fossil fuels and nuclear technology. Stimulus spending during the recession during the global COVID-19 pandemic also reflects this pattern.

Though international cooperation and competition have been effective at driving changes to clean energy RD&D in the past, the authors caution against taking the successful interplay of RD&D cooperation and technology competition for granted going forward.

“We live in times of heightened geopolitical tensions — China recently announced plans to stop climate cooperation with the US,” said Meckling, adding that maintaining the balance of RD&D cooperation and technology competition requires supportive policies. “Government officials need to focus on embedding energy innovation in effective industrial policy strategies to be able to turn innovation into competitive advantages.”

“They also need to strengthen global trade cooperation to facilitate fair and open competition in clean energy technology markets that continue to incentivize governments to invest in clean energy RD&D,” Meckling said.

Reference: “Energy innovation funding and institutions in major economies” by Jonas Meckling, Clara Galeazzi, Esther Shears, Tong Xu and Laura Diaz Anadon, 12 September 2022, Nature Energy.
DOI: 10.1038/s41560-022-01117-3

The study was funded by the U.S. Department of Agriculture.

MIT’s New Optimizer for Improving Any Autonomous Robotic System

By JENNIFER CHU, MASSACHUSETTS INSTITUTE OF TECHNOLOGY OCTOBER 15, 2022

MIT engineers have developed a general design tool for roboticists to use as a sort of automated recipe for success. Their optimization code can be applied to simulations of virtually any autonomous robotic system and can be used to automatically identify how and where to tweak a system to improve a robot’s performance.

A new general-purpose optimizer can speed up the design of autonomous systems including walking robots and self-driving vehicles.

Since the fastidious Roomba vacuum, autonomous robots have come a long way. In recent years, artificially intelligent systems have been deployed in self-driving cars, warehouse packing, patient screening, last-mile food delivery, hospital cleaning, restaurant service, meal prep, and building security.

Each of these robotic systems is a product of an ad hoc design process specific to that particular system. This means that in designing an autonomous robot, engineers must run countless trial-and-error simulations, often informed by intuition. These simulations are tailored to a particular robot’s components and tasks, in order to tune and optimize its performance. Designing an autonomous robot today is, in some respects, a lot like baking a cake from scratch, with no recipe or prepared mix to ensure a successful outcome.

A new general-purpose optimization tool can improve the performance of many autonomous robotic systems. Shown here is a hardware demonstration in which the tool automatically optimizes the performance of two robots working together to move a heavy box. Credit: Courtesy of the researchers

Now, engineers at MIT have developed a general design tool for roboticists to use as a sort of automated recipe for success. Optimization code has been devised by the team that can be applied to simulations of virtually any autonomous robotic system and can be used to automatically identify how and where to tweak a system to improve a robot’s performance.

The engineers showed that the tool was able to quickly improve the performance of two very different autonomous systems: one in which a robot navigated a path between two obstacles, and another in which a pair of robots worked together to move a heavy box.

The group hopes the new general-purpose optimizer can help to speed up the development of a wide range of autonomous systems, from walking robots and self-driving vehicles, to soft and dexterous robots, and teams of collaborative robots.

The researchers, composed of Charles Dawson, an MIT graduate student, and ChuChu Fan, assistant professor in MIT’s Department of Aeronautics and Astronautics, presented their findings at the annual Robotics: Science and Systems conference in New York.

Inverted design

Dawson and Fan realized the need for a general optimization tool after observing a wealth of automated design tools available for other engineering disciplines.

“If a mechanical engineer wanted to design a wind turbine, they could use a 3D CAD tool to design the structure, then use a finite-element analysis tool to check whether it will resist certain loads,” Dawson says. “However, there is a lack of these computer-aided design tools for autonomous systems.”

Normally, a roboticist optimizes an autonomous system by first developing a simulation of the system and its many interacting subsystems, such as its planning, control, perception, and hardware components. She then must tune certain parameters of each component and run the simulation forward to see how the system would perform in that scenario.

Only after running many scenarios through trial and error can a roboticist then identify the optimal combination of ingredients to yield the desired performance. It’s a tedious, overly tailored, and time-consuming process that Dawson and Fan sought to turn on its head.

“Instead of saying, ‘Given a design, what’s the performance?’ we wanted to invert this to say, ‘Given the performance we want to see, what is the design that gets us there?’” Dawson explains.

The researchers developed an optimization framework, or a computer code, that can automatically find tweaks that can be made to an existing autonomous system to achieve a desired outcome.

The heart of the code is based on automatic differentiation, or “autodiff,” a programming tool that was developed within the machine learning community and was used initially to train neural networks. Autodiff is a technique that can quickly and efficiently “evaluate the derivative,” or the sensitivity to change of any parameter in a computer program. Dawson and Fan built on recent advances in autodiff programming to develop a general-purpose optimization tool for autonomous robotic systems.

“Our method automatically tells us how to take small steps from an initial design toward a design that achieves our goals,” Dawson says. “We use autodiff to essentially dig into the code that defines a simulator, and figure out how to do this inversion automatically.”

Building better robots

The team tested their new tool on two separate autonomous robotic systems, and showed that the tool quickly improved each system’s performance in laboratory experiments, compared with conventional optimization methods.

The first system comprised a wheeled robot tasked with planning a path between two obstacles, based on signals that it received from two beacons placed at separate locations. The team sought to find the optimal placement of the beacons that would yield a clear path between the obstacles.

They found the new optimizer quickly worked back through the robot’s simulation and identified the best placement of the beacons within five minutes, compared to 15 minutes for conventional methods.

The second system was more complex, comprising two-wheeled robots working together to push a box toward a target position. A simulation of this system included many more subsystems and parameters. Nevertheless, the team’s tool efficiently identified the steps needed for the robots to accomplish their goal, in an optimization process that was 20 times faster than conventional approaches.

“If your system has more parameters to optimize, our tool can do even better and can save exponentially more time,” Fan says. “It’s basically a combinatorial choice: As the number of parameters increases, so do the choices, and our approach can reduce that in one shot.”

The team has made the general optimizer available to download, and plans to further refine the code to apply to more complex systems, such as robots that are designed to interact with and work alongside humans.

“Our goal is to empower people to build better robots,” Dawson says. “We are providing a new building block for optimizing their system, so they don’t have to start from scratch.”

Reference: “Certifiable Robot Design Optimization using Differentiable Programming” by Charles B Dawson and Chuchu Fan, June 2022, Robotics: Science and Systems 2022.
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This research was supported, in part, by the Defense Science and Technology Agency in Singapore and by the MIT-IBM Watson AI Lab.

Could a Refuted 120-Year-Old Theory Actually Be True? Similarity Between Dementia and Schizophrenia Discovered

By MAX PLANCK INSTITUTE FOR HUMAN COGNITIVE AND BRAIN SCIENCES OCTOBER 16, 2022

Frontotemporal dementia (FTD), especially the behavioral variant (bvFTD), is difficult to recognize in the early stages because it is often confused with psychiatric illnesses such as schizophrenia.

For the first time, researchers compared schizophrenia and frontotemporal dementia, two conditions that affect the frontal and temporal lobes of the brain.

Rarely do researchers in basic science revisit more than 120-year-old findings that appear to be obsolete. This was even a drive for researchers and phsicians Nikolaos Koutsouleris and Matthias Schroeter. It concerns Emil Kraepelin, who established the Ludwig Maximilian University of Munich’s (LMU) mental hospital and the Max Planck Institute for Psychiatry (MPI), as well as his 1899 term “dementia praecox.”

This was his definition of young individuals who retreat from reality and enter an irreversible, dementia-like condition. Kraepelin lived to see his theory refuted. By the early twentieth century, doctors were starting to adopt the name “schizophrenia” for these people, since the disease did not always follow such a bad course.

Kraepelin proposed the concept of a frontotemporal disease, assuming that the frontal and temporal lobe regions of the brain are responsible for the sometimes-debilitating course of the patients. That is where empathy, social behavior, and personality are controlled. “But this idea was lost as no pathological evidence for neurodegenerative processes seen in Alzheimer’s Disease was found in the brains of these patients,” says Koutsouleris, who works at Kraepelin’s places of work, the MPI and LMU.

He continues: “Ever since I became a psychiatrist, I wanted to work on this question.”

With sufficiently large data sets, imaging techniques, and machine learning algorithms, the professor had the ability to potentially find answers fifteen years later. He partnered up with Matthias Schroeter, a researcher at the Max Planck Institute for Human Cognitive and Brain Sciences who studies neurodegenerative diseases and specializes in frontotemporal dementias. 

Similarities between schizophrenia and frontotemporal dementia?

Frontotemporal dementia (FTD), especially the behavioral variant (bvFTD), is difficult to recognize in its early stages because it is often confused with schizophrenia. Thus, the similarities are obvious: in sufferers of both groups, personality as well as behavioral changes occur. An often dramatic development for affected persons and relatives sets in. Since both disorders are located in the frontal, temporal and insular regions of the brain, it was obvious to compare them directly as well. “They seem to be on a similar symptom spectrum, so we wanted to look for common signatures or patterns in the brain,” Koutsouleris says, describing his plan.

With an international team, Koutsouleris and Schroeter used artificial intelligence to train neuroanatomical classifiers of both disorders, which they applied to brain data from different cohorts. The result, recently published in the prestigious journal JAMA Psychiatry, was that 41 percent of schizophrenia patients met the classifier’s criteria for bvFTD. “When we saw this in schizophrenic patients as well, it rang a bell – indicating a similarity between the two disorders,” Koutsouleris and Schroeter recall.

The research team found that the higher the patients’ bvFTD score, which measured the similarity between the two disorders, the more likely they were to have a “bvFTD-like” phenotype and the less likely they were to improve their symptoms over two years.

A 23-year-old patient does not recover

“I just wanted to know why my 23-year-old patient with onset symptoms of schizophrenia, such as hallucinations, delusions, and cognitive deficits, had not improved at all, even after two years, while another who started out just as bad was continuing his education and had found a girlfriend. Again and again, I saw these young people who did not recover at all,” Koutsouleris describes.

When the researchers also checked the correlations in high-risk patients such as the 23-year-old, they found confirmation at the neuroanatomical level of what Kraepelin had been the first to decisively describe: no improvement whatsoever in the condition of some patients, quite the opposite. Similar neuronal structures were affected, in particular the so-called “default mode” network and the salience network of the brain, responsible for attention control, empathy and social behavior, showed volume decreases in the gray matter area that houses the neurons. In bvFTD, certain neurons (von Economo neurons) perish; in schizophrenia, these neurons are also altered. This was reflected by the neuroanatomical score: after one year, it had doubled in these severely affected persons. As a comparison, the scientists had also calculated the Alzheimer’s score using a specific classifier and did not find these effects there. “This means that the concept of dementia praecox can no longer be completely wiped away; we provide the first valid evidence that Kraepelin was not wrong, at least in some of the patients,” Schroeter sums up.

Today, or in the near future, this means that experts will be able to predict which subgroup patients belong to. “Then intensive therapeutic support can be initiated at an early stage to exploit any remaining recovery potential,” Koutsouleris urges. In addition, new personalized therapies could be developed for this subgroup that promote a proper maturation and connectivity of the affected neurons and prevent their progressive destruction as part of the disease process.

Reference: “Exploring Links Between Psychosis and Frontotemporal Dementia Using Multimodal Machine Learning Dementia Praecox Revisited” by Nikolaos Koutsouleris, MD, Christos Pantelis, PhD, MD, Dennis Velakoulis, PhD, MD, Philip McGuire, PhD, MD, Dominic B. Dwyer, PhD, Maria-Fernanda Urquijo-Castro, MSc, Riya Paul, PhD, Sen Dong, MSc, David Popovic, MD, Oemer Oeztuerk, MD, Joseph Kambeitz, MD, Raimo K. R. Salokangas, PhD, MD, Jarmo Hietala, PhD, MD, Alessandro Bertolino, PhD, MD, Paolo Brambilla, MD, Rachel Upthegrove, MBBS, PhD, Stephen J. Wood, PhD, Rebekka Lencer, MD, Stefan Borgwardt, MD, Carlo Maj, PhD, Markus Nöthen, MD, Franziska Degenhardt, MD, Maryna Polyakova, MD, Karsten Mueller, PhD, Arno Villringer, MD, Adrian Danek, MD, Klaus Fassbender, MD, Klaus Fliessbach, MD, Holger Jahn, MD, Johannes Kornhuber, MD, Bernhard Landwehrmeyer, MD, Sarah Anderl-Straub, PhD, Johannes Prudlo, MD, Matthis Synofzik, MD, Jens Wiltfang, MD, Lina Riedl, MD, Janine Diehl-Schmid, MD, Markus Otto, MD, Eva Meisenzahl, MD, Peter Falkai, MD, Matthias L. Schroeter, PhD, MA, MD, for the International FTD-Genetics Consortium (IFGC), the German Frontotemporal Lobar Degeneration (FTLD) Consortium and the PRONIA Consortium, 3 August 2022, JAMA Psychiatry.
DOI: 10.1001/jamapsychiatry.2022.2075

The study was funded by the European Commission and the German Federal Ministry of Education and Research.

Up to 1.9 Billion Cases – New Research Indicates Far More People Caught COVID Then Official Estimates

By MEDICAL COLLEGE OF GEORGIA AT AUGUSTA UNIVERSITY OCTOBER 16, 2022

This underreporting results in worldwide pandemic estimates ranging from 600 million to 2.4 billion cases.

A new mathematical model suggests that as few as 1 in 5 COVID cases were counted globally. 

According to mathematical models, as few as one in every five instances of COVID-19 that occurred during the first 29 months of the pandemic are accounted for in the half billion cases officially recorded.

According to the Centers for Disease Control and Prevention, the World Health Organization reported 6,190,349 deaths and 513,955,910 cases between January 1, 2020, and May 6, 2022. These figures have already elevated COVID-19 to the position of a top killer in some nations, including the United States, right behind heart disease and cancer.

Still, mathematical models show an overall underreporting of cases ranging from 1 in 1.2 to 1 in 4.7, according to researchers reporting in the journal Current Science. This underreporting results in worldwide pandemic estimates ranging from 600 million to 2.4 billion cases.

“We all acknowledge a huge impact on us as individuals, a nation, and the world, but the true number of cases is very likely much higher than we realize,” says Dr. Arni S.R. Srinivasa Rao, director of the Laboratory for Theory and Mathematical Modeling in the Division of Infectious Diseases at the Medical College of Georgia at Augusta University. “We are trying to understand the extent of underreported cases.”

Dr. Arni Rao. Credit: Michael Holahan, Augusta University

Rao and his colleagues Dr. Steven G. Krantz, a mathematics professor at Washington University in St. Louis, Missouri, and Dr. David A. Swanson, an Edward A. Dickson Emeritus Professor in the Department of Sociology at the University of California, Riverside, write that the wide range of estimated cases produced by their models show the problems with the accuracy of reported numbers, which include data tampering, the inability to conduct accurate case tracking, and the lack of uniformity in how cases are reported.

A dearth of information and inconsistency in reporting cases has been a major problem with getting a true picture of the impact of the pandemic, Rao says.

Mathematical models use whatever information is available as well as relevant factors like global transmission rates and the number of people in the world, including the average population over the 29-month timeframe. That average, referred to as the effective population, better accounts for those who were born and died for any reason and so provides a more realistic number of the people out there who could potentially be infected, Rao says.

“You have to know the true burden on patients and their families, on hospitals and caregivers, on the economy and the government,” Rao says. More accurate numbers also help in assessing indirect implications like the underdiagnosis of potentially long-term neurological and mental disorders that are now known to be directly associated with infection, he says.

The mathematics experts had published similar model-based estimates for eight countries earlier in the pandemic in 2020, to provide more perspective on what they said then was clear underreporting. Their modeling predicted countries like Italy, despite their diligence in reporting, were likely capturing 1 in 4 actual cases while in China, where population numbers are tremendous, they calculated a huge range of potential underreporting, from 1 in 149 to 1 in 1,104 cases.

Other contributors to underreporting include the reality that everyone who has gotten COVID-19 has not been tested. Also, a significant percentage of people, even vaccinated and boosted individuals, are getting infected more than once, and may only go to the doctor for PCR resting the first time and potentially use at-home tests or even no test for subsequent illnesses. For example, a recent report in JAMA on reinfection rates in Iceland during the first 74 days of the Omicron variant wave there indicates, based on PCR testing, that reinfection rates were at 10.9% — a high of 15.1% among those 18-29-year-olds — for those who received two or more doses of a vaccine.

The number of fully vaccinated individuals globally reached a reported 5.1 billion by the end of their 29-month study timeframe.

The CDC was reporting downward trends in new cases, hospitalizations, and deaths in the United States from August to September

Reference: “Global underreporting of COVID-19 cases during 1 January 2020 to 6 May 2022” by Steven G. Krantz, David A. Swanson and Arni S. R. Srinivasa Rao, 3 August 2022, Current Science.
The report can be found here. 

Tighter School Security Reduces Academic Performance

By WASHINGTON UNIVERSITY IN ST. LOUIS OCTOBER 16, 2022

The researchers call their findings a “safety tax,” or a cost for increased security.

Recently, schools have been increasing security measures. However, could this increased security be impacting children’s test scores?

According to recent research from the Brown School at Washington University in St. Louis, increased surveillance is having a negative effect on academic performance as schools throughout the nation consider ramping up security measures in reaction to recent school shootings.

According to Jason Jabbari, research assistant professor and co-author of a recent study that was published in the Journal of Criminal Justice, more security lowers math test scores, lowers the percentage of kids entering college, and increases suspensions.

The authors discovered that in addition to being utilized to prevent school shootings, surveillance measures may have enhanced schools’ ability to recognize and discipline pupils for less severe and more frequent offenses, which may have a detrimental effect on the learning environment.

“Our research shows that greater detection of student offenses leads to more punishment regardless of the students who attend these schools,” Jabbari said. “Moreover, while increased surveillance has collateral consequences on academic achievement that extend to all students, because Black students are more likely to attend high-surveillance schools, the burdens of the safety tax fall most heavily on Black students, ultimately increasing racial inequities in education.”

The results, which Jabbari and his co-author Odis Johnson Jr. of Johns Hopkins University called a “safety tax,” refer to what it costs students to have more security and surveillance at school.

They discover that this price is disproportionately imposed on Black students of both genders due to their overrepresentation in high-surveillance schools. Black students are four times more likely to enroll in a school with extensive surveillance.

Jabbari and Johnson analyzed data from the Educational Longitudinal Study of the National Center for Education Statistics. Even after adjusting for school social disorder and student misconduct, kids in high-surveillance schools were more likely to be suspended in addition to experiencing academic consequences.

“In addition to suspending more students, the infrastructure of surveillance reduces test scores in mathematics and college enrollment altogether for suspended and non-suspended alike, suggesting the presence of negative spillover effects,” the authors wrote.

The best way to end violence in schools, Jabbari and Johnson suggest, is to support students’ mental health, socio-emotional attachment, and feelings of belonging to schools and to end re-traumatizing students through systemic racism in schools.

Reference: “Infrastructure of social control: A multi-level counterfactual analysis of surveillance and Black education” by Odis Johnson Jr. and Jason Jabbari, 20 September 2022, Journal of Criminal Justice.
DOI: 10.1016/j.jcrimjus.2022.101983

 

Battery Tech Breakthrough: 10-Minute Charge Time Paves Way for Mass Adoption of Affordable Electric Car

By PENN STATE UNIVERSITY OCTOBER 16, 2022

This 10-min fast-charging battery was developed for electric cars, with the black box on the top containing a battery management system to control the module. Credit: EC Power

Scientists develop a new technique that charges EV batteries in just 10 minutes.

A design breakthrough has enabled a 10-minute charge time for a typical electric vehicle battery. A paper detailing the record-breaking combination of a shorter charge time and more energy acquired for a longer travel range was published on October 12 in the journal Nature.

“The need for smaller, faster-charging batteries is greater than ever,” said Chao-Yang Wang, lead author on the study. “There are simply not enough batteries and critical raw materials, especially those produced domestically, to meet anticipated demand.” Wang is the William E. Diefenderfer Professor of Mechanical Engineering at Penn State.

The Air Resources Board of California adopted a comprehensive plan in August to impose restrictions on and eventually outlaw the sale of gasoline-powered vehicles in the state. This means that by 2035, the largest auto market in the United States will effectively retire the internal combustion engine.

Wang explained that if new car sales are going to shift to battery-powered electric vehicles (EVs), they’ll need to overcome two major drawbacks. First, they are too slow to recharge. Second, they are too large to be efficient and affordable. Instead of taking a few minutes at the gas pump, some EVs can take all day to recharge depending on the battery.

“Our fast-charging technology works for most energy-dense batteries and will open a new possibility to downsize electric vehicle batteries from 150 to 50 kWh without causing drivers to feel range anxiety,” said Wang, whose lab partnered with State College-based startup EC Power to develop the technology. “The smaller, faster-charging batteries will dramatically cut down battery cost and usage of critical raw materials such as cobalt, graphite, and lithium, enabling mass adoption of affordable electric cars.”

The technology relies on internal thermal modulation, an active method of temperature control to demand the best performance possible from the battery, Wang explained. Batteries operate most efficiently when they are hot, but not too hot. Keeping batteries consistently at just the right temperature has been major challenge for battery engineers. Historically, they have relied on external, bulky heating and cooling systems to regulate battery temperature, which respond slowly and waste a lot of energy, Wang said. 

Wang and his team decided to instead regulate the temperature from inside the battery. The researchers developed a new battery structure that adds an ultrathin nickel foil as the fourth component besides anode, electrolyte and cathode. Acting as a stimulus, the nickel foil self-regulates the battery’s temperature and reactivity which allows for 10-minute fast charging on just about any EV battery, Wang explained.

“True fast-charging batteries would have immediate impact,” the researchers write. “Since there are not enough raw minerals for every internal combustion engine car to be replaced by a 150 kWh-equipped EV, fast charging is imperative for EVs to go mainstream.”

The study’s partner, EC Power, is working to manufacture and commercialize the fast-charging battery for an affordable and sustainable future of vehicle electrification, Wang said. 

Reference: “Fast charging of energy-dense lithium-ion batteries” by Chao-Yang Wang, Teng Liu, Xiao-Guang Yang, Shanhai Ge, Nathaniel V. Stanley, Eric S. Rountree, Yongjun Leng and Brian D. McCarthy, 12 October 2022, Nature.
DOI: 10.1038/s41586-022-05281-0

The other coauthors on the study are Teng Liu, Xiao-Guang Yang, Shanhai Ge and Yongjun Leng of Penn State and Nathaniel Stanley, Eric Rountree and Brian McCarthy of EC Power.

The work was supported by the U.S. Department of Energy, the U.S. Department of Defense, the U.S. Air Force and the William E. Diefenderfer Endowment.

The Science Behind CBD’s Health Benefits – The Endocannabinoid System

By SCITECHDAILY.COM OCTOBER 16, 2022

Throughout the 1990s, researchers looking into cannabis for its health effects began to unravel a mystery that culminated in the discovery of a whole new system in the body. Called the Endocannabinoid System (ECS), this vast network of transmitters, receptors, and enzymes can be thought of as a sort of “house manager” that keeps many of the other systems in your body in a healthy state of balance.^[1]

What’s unique about your ECS is that it contains chemical agents called cannabinoids. These are special compounds that work to relay information along neural pathways and interact with key receptors in your brain and body so that processes such as metabolism, digestion, memory, cognition, and immunity can function optimally. This helps ensure that your body stays healthy and strong.

However, just like with vitamins and minerals, cannabinoids can become depleted or damaged. When this happens, a condition known as clinical endocannabinoid deficiency (CED) may set in.^[2]

How CBD Helps Your Endocannabinoid System

Just like your body’s naturally occurring cannabinoids (which are often referred to as endocannabinoids), the CBD that’s found in cannabis is also a cannabinoid. It is considered one of the most powerful in terms of its health-balancing benefits.^[3] Here’s why:

• CBD works to help limit the breakdown, deactivation, and uptake of your body’s natural endocannabinoids.
• CBD has also been shown to help increase your brain’s endocannabinoid levels.

Because of CBD’s protective properties, it may assist in reducing your risk for CED and all the detrimental health conditions that can come with it. For instance, CBD works to positively regulate a key endocannabinoid called anandamide,^[4] better known as “the bliss molecule.” Anandamide’s many health benefits include:^[5]

• Lowering inflammation, which is associated with many chronic conditions^[6]
• Relieving pain
• Enhancing memory
• Protecting against stress, anxiety, addiction, and PTSD
• Helping inhibit the production of certain cancer cells
• Balancing mood and bridging in feelings of happiness

So rather than healing a specific disease, CBD’s job as a cannabinoid is to help balance your internal endocannabinoids and system responses so that your risk for diseases may be reduced and the symptoms of health conditions can be better managed.^[7]

What to Look for When Shopping for CBD

Hemp Oil vs. Hemp Extract

CBD and other healthy cannabinoids, terpenes, and flavonoids are extracted from the flowers and leaves of the hemp plant and form an oil known as hemp extract. This product contains the healthy ingredients needed to help regulate your ECS and balance the release and uptake of hormones, signaling agents, and other key chemicals. That’s why when you’re shopping for CBD products, it’s best to choose those that contain hemp extract.

Hemp oil, on the other hand, comes from hemp seeds and contains only trace amounts of CBD. It provides a health boost through its dietary omega-3 and omega-6 essential fats in much the same ways the seeds do.^[8]

Third-Party Testing of Hemp Extract Products

To ensure you find high-quality CBD free of pesticides, heavy metals, and contaminants, look for a company that has “third-party testing” done on all its products. Independent lab analysis will analyze a product’s contents to confirm it contains the amount of CBD and other healthy ingredients that it claims it does.

Companies who do third-party testing of their hemp products will often list a Certificate of Analysis (COA) from their testing lab on the website so that you can compare it against the product description.

The Importance of Organic Hemp Farming Practices

Hemp is what’s known as a “bioaccumulator,” which is a plant that can naturally remove pesticides and other harmful toxins from the soil.^[9] This is why you’ll want to purchase CBD that’s sourced from growers who use organic farming methods so that you can be sure the soil and cultivating methods are pure.

References:

1. healthline.com/health/endocannabinoid-system
2. youtube.com/watch?v=IffacdsVsG4
3. projectcbd.org/science/how-cbd-works
4. ncbi.nlm.nih.gov/pmc/articles/PMC6460372/
5. bebrainfit.com/anandamide/
6.  health.harvard.edu/staying-healthy/understanding-acute-and-chronic-inflammation
7. cbdcentral.com/cbd-ultimate-benefits-guide/
8. webmd.com/diet/health-benefits-hemp-seed-oil
9. medium.com/@ministryofhemp/hemp-bioremediation-the-miracle-crop-that-actually-cleans-soil-97a7a654991f