Aurelia Butler

The semiconductor industry today is working to respond to a threefold mandate: increasing computing power, decreasing chip sizes and managing power in densely packed circuits.
To meet these demands, the industry must look beyond silicon to produce devices appropriate for the growing role of computing.
While unlikely to abandon the workhorse material anytime in the near or distant future, the technology sector will require creative enhancements in chip materials and architectures to produce devices appropriate for the growing role of computing.
One of the biggest shortcomings of silicon is that it can only be made so thin because its material properties are fundamentally limited to three dimensions [3D]. For this reason, two-dimensional [2D] semiconductors — so thin as to have almost no height — have become an object of interest to scientists, engineers and microelectronics manufacturers.
Thinner chip components would provide greater control and precision over the flow of electricity in a device, while lowering the amount of energy required to power it. A 2D semiconductor would also contribute to keeping the surface area of a chip to a minimum, lying in a thin film atop a supporting silicon device.
But until recently, attempts to create such a material have been unsuccessful.
Certain 2D semiconductors have performed well on their own, but required such high temperatures to deposit they destroyed the underlying silicon chip. Others could be deposited at silicon-compatible temperatures, but their electronic properties — energy usage, speed, precision — were lacking. Some fit the bill for temperature and performance but could not be grown to the requisite purity at industry-standard sizes. More

Rutgers researchers have developed a way of detecting the early onset of deadly infectious diseases using a test so ultrasensitive that it could someday revolutionize medical approaches to epidemics.
The test, described in Science Advances, is an electronic sensor contained within a computer chip. It employs nanoballs — microscopic spherical clumps made of tinier particles of genetic material, each of those with diameters 1,000 times smaller than the width of a human hair — and combines that technology with advanced electronics.
“During the COVID pandemic, one of the things that didn’t exist but could have stemmed the spread of the virus was a low-cost diagnostic that could flag people known as the ‘quiet infected’ — patients who don’t know they are infected because they are not exhibiting symptoms,” said Mehdi Javanmard, a professor in the Department of Electrical and Computer Engineering in the Rutgers School of Engineering and an author of the study. “In a pandemic, pinpointing an infection early with accuracy is the Holy Grail. Because once a person is showing symptoms — sneezing and coughing — it’s too late. That person has probably infected 20 people.”
For the past 20 years, Javanmard has been developing biosensors — devices that monitor and transmit information about a life process. During the COVID-19 pandemic, he became disheartened about the extent of infections and the extreme loss of life. He believed there had to be a way of using biosensors as a test to detect illness earlier.
Working with Muhammad Tayyab, a Rutgers doctoral student and co-author of the study, Javanmard and research colleagues at the Karolinska Institute in Sweden and Stanford and Yale universities started brainstorming.
“We thought: How is there a way where we can leverage our individual expertise to build something new?” Javanmard said.
The biosensor developed by the team works through a series of steps. First, it zeroes in on a virus’ characteristic sequence of nucleic acids — naturally occurring chemical compounds that serve as the primary information-carrying molecules in a cell. Next, because it amplifies any nucleic acid sequence found in the sample, it makes many more copies, as many as 10,000. Then, it clumps those thousands of specks of nucleic acids into nanoballs that are “large” enough to be detected. More

Virtual reality simulators can help learners improve their technical skills faster and with no risk to patients. In the field of neurosurgery, they allow medical students to practice complex operations before using a scalpel on a real patient. When combined with artificial intelligence, these tutoring systems can offer tailored feedback like a human instructor, identifying areas where the students need to improve and making suggestions on how to achieve expert performance.
A new study from the Neurosurgical Simulation and Artificial Intelligence Learning Centre at The Neuro (Montreal Neurological Institute-Hospital) of McGill University, however, shows that human instruction is still necessary to detect and compensate for unintended, and sometimes negative, changes in neurosurgeon behaviour after virtual reality AI training.
In the study, 46 medical students performed a tumour removal procedure on a virtual reality simulator. Half of them were randomly selected to receive instruction from an AI-powered intelligent tutor called the Virtual Operative Assistant (VOA), which uses a machine learning algorithm to teach surgical techniques and provide personalized feedback. The other half served as a control group by receiving no feedback. The students’ work was then compared to performance benchmarks selected by a team of established neurosurgeons.
Comparing the results, AI-tutored students caused 55 per cent less damage to healthy tissues than the control group. AI-tutored students also showed a 59 per cent reduction in average distance between instruments in each hand and 46 per cent less maximum force applied, both important safety measures.
However, AI-tutored students also showed some negative outcomes. For example, their dominant hand movements had 50 per cent lower velocity and 45 per cent lower acceleration than the control group, making their operations less efficient. The speed at which they removed tumour tissue was also 29 per cent lower in the AI-tutored group than the control group.
These unintended outcomes underline the importance of human instructors in the learning process, to promote both safety and efficiency in students.
“AI systems are not perfect,” says Ali Fazlollahi, a medical student researcher at the Neurosurgical Simulation and Artificial Intelligence Learning Centre and the study’s first author. “Achieving mastery will still require some level of apprenticeship from an expert. Programs adopting AI will enable learners to monitor their competency and focus their intraoperative learning time with instructors more efficiently and on their individual tailored learning goals. We’re currently working towards finding an optimal hybrid mode of instruction in a crossover trial.”
Fazlollahi says his findings have implications beyond neurosurgery because many of the same principles are applied in other fields of skills’ training.
“This includes surgical education, not just neurosurgery, and also a range of other fields from aviation to military training and construction,” he says. “Using AI alone to design and run a technical skills curriculum can lead to unintended outcomes that will require oversight from human experts to ensure excellence in training and patient care.”
“Intelligent tutors powered by AI are becoming a valuable tool in the evaluation and training of the next generation of neurosurgeons,” says Dr. Rolando Del Maestro, the study’s senior author. “However, it is essential that surgical educators are an integral part of the development, application, and monitoring of these AI systems to maximize their ability to increase the mastery of neurosurgical skills and improve patient outcomes.” More

Consumer confidence in driving hydrogen-fueled vehicles could be improved by having station operators adopt a predictive model that helps them anticipate maintenance needs, according to researchers at the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) and Colorado State University (CSU).
Stations shutting down for unscheduled maintenance reduces hydrogen fueling availability to consumers and may slow adoption of these types of fuel cell electric vehicles, the researchers noted. The use of what is known as a prognostics health monitoring (PHM) model would allow hydrogen stations to reduce these unscheduled events.
“Motorists expect to be able to fuel their vehicles without any problems. We want to ensure motorists who drive hydrogen-fueled cars have the same experience,” said Jennifer Kurtz, lead author of the new paper, “Hydrogen Station Prognostics and Health Monitoring Model,” which appears in the International Journal of Hydrogen Energy. “This predictive model can let station operators know in advance when a problem might occur and minimize any disruptions that motorists might experience with hydrogen fueling.”
Co-authored by Spencer Gilleon of NREL and Thomas Bradley of CSU, the article posits the PHM model would improve station availability and consumer confidence.
The availability of hydrogen as a vehicle fuel is low compared to the ubiquity of gasoline, a fact reflected in the number of stations that dispense the low-emission fuel. While California has more than 10,000 gasoline stations, the Hydrogen Fuel Cell Partnership counts just 59 retail hydrogen stations across the state. With relatively few choices, motorists who rely on hydrogen must be confident their needed fuel is available. Station operators must make any necessary repairs to meet the demands of consumers, but they also must investigate the causes of any failures to avoid future problems.
Data from the National Fuel Cell Technology Evaluation Center reveals the single most common reason hydrogen stations shut down for unscheduled maintenance is problems with the dispenser system, which encompasses such items as the hoses and dispenser valves as well as the user interface. By using a data-based PHM, station operators could reduce the frequency of unscheduled maintenance and increase the frequency of preventive maintenance. The researchers have dubbed this particular PHM “hydrogen station prognostics health monitoring,” or H2S PHM.
The H2S PHM calculates the probability a component will continue working without a failure, based on how many fills the station has completed. The model can also be used to estimate the remaining useful life for each of the components, thereby lowering maintenance costs and making the stations more reliable. Using a hypothetical example, the researchers considered a dispenser valve that the H2S PHM has flagged as needing attention. The station operator can then be prepared for upcoming maintenance and schedule a technician to come when demand for hydrogen will be low. That cuts down on the amount of time a station would be unable to fuel vehicles. If the valve were to fail without warning, the station operator would have to call a technician, wait for their arrival and diagnosis of the problem, while at the same time be unable to provide fuel.
Kurtz, the director of NREL’s Energy Conversion and Storage Systems Center, noted that limitations exist when applying H2S PHM to the reliability of a hydrogen station. The method would not predict sudden failures, which can be caused by human error. The H2S PHM is also only as good as the available data, and more data is needed.
The Department of Energy’s Hydrogen and Fuel Cell Technologies Office funded the research.
NREL is the Department of Energy’s primary national laboratory for renewable energy and energy efficiency research and development. NREL is operated for DOE by the Alliance for Sustainable Energy LLC. More

Some 2,000 years ago in ancient Rome, glass vessels carrying wine or water, or perhaps an exotic perfumes, tumble from a table in a marketplace, and shatter to pieces on the street. As centuries passed, the fragments were covered by layers of dust and soil and exposed to a continuous cycle of changes in temperature, moisture, and surrounding minerals.
Now these tiny pieces of glass are being uncovered from construction sites and archaeological digs and reveal themselves to be something extraordinary. On their surface is a mosaic of iridescent colors of blue, green and orange, with some displaying shimmering gold-colored mirrors.
These beautiful glass artifacts are often set in jewelry as pendants or earrings, while larger, more complete objects are displayed in museums.
For Fiorenzo Omenetto and Giulia Guidetti, professors of engineering at the Tufts University Silklab and experts in materials science, what’s fascinating is how the molecules in the glass rearranged and recombined with minerals over thousands of years to form what are called photonic crystals — ordered arrangements of atoms that filter and reflect light in very specific ways.
Photonic crystals have many applications in modern technology. They can be used to create waveguides, optical switches and other devices for very fast optical communications in computers and over the internet. Since they can be engineered to block certain wavelengths of light while allowing others to pass, they are used in filters, lasers, mirrors, and anti-reflection (stealth) devices.
In a recent study published in the Proceedings of the National Academy of Sciences (PNAS) USA, Omenetto, Guidetti and collaborators report on the unique atomic and mineral structures that built up from the glass’ original silicate and mineral constituents, modulated by the pH of the surrounding environment, and the fluctuating levels of groundwater in the soil.
The project started by chance during a visit to the Italian Institute of Technology’s (IIT) Center for Cultural Heritage Technology. “This beautiful sparkling piece of glass on the shelf attracted our attention,” said Omenetto. “It was a fragment of Roman glass recovered near the ancient city of Aquileia Italy.” Arianna Traviglia, director of the Center, said her team referred to it affectionately as the ‘wow glass’. They decided to take a closer look. More

Artificial intelligence (AI) and machine learning (ML) can effectively detect and diagnose Polycystic Ovary Syndrome (PCOS), which is the most common hormone disorder among women, typically between ages 15 and 45, according to a new study by the National Institutes of Health. Researchers systematically reviewed published scientific studies that used AI/ML to analyze data to diagnose and classify PCOS and found that AI/ML based programs were able to successfully detect PCOS.
“Given the large burden of under- and mis-diagnosed PCOS in the community and its potentially serious outcomes, we wanted to identify the utility of AI/ML in the identification of patients that may be at risk for PCOS,” said Janet Hall, M.D., senior investigator and endocrinologist at the National Institute of Environmental Health Sciences (NIEHS), part of NIH, and a study co-author. “The effectiveness of AI and machine learning in detecting PCOS was even more impressive than we had thought.”
PCOS occurs when the ovaries do not work properly, and in many cases, is accompanied by elevated levels of testosterone. The disorder can cause irregular periods, acne, extra facial hair, or hair loss from the head. Women with PCOS are often at an increased risk for developing type 2 diabetes, as well as sleep, psychological, cardiovascular, and other reproductive disorders such as uterine cancer and infertility.
“PCOS can be challenging to diagnose given its overlap with other conditions,” said Skand Shekhar, M.D., senior author of the study and assistant research physician and endocrinologist at the NIEHS. “These data reflect the untapped potential of incorporating AI/ML in electronic health records and other clinical settings to improve the diagnosis and care of women with PCOS.”
Study authors suggested integrating large population-based studies with electronic health datasets and analyzing common laboratory tests to identify sensitive diagnostic biomarkers that can facilitate the diagnosis of PCOS.
Diagnosis is based on widely accepted standardized criteria that have evolved over the years, but typically includes clinical features (e.g., acne, excess hair growth, and irregular periods) accompanied by laboratory (e.g., high blood testosterone) and radiological findings (e.g., multiple small cysts and increased ovarian volume on ovarian ultrasound). However, because some of the features of PCOS can co-occur with other disorders such as obesity, diabetes, and cardiometabolic disorders, it frequently goes unrecognized.
AI refers to the use of computer-based systems or tools to mimic human intelligence and to help make decisions or predictions. ML is a subdivision of AI focused on learning from previous events and applying this knowledge to future decision-making. AI can process massive amounts of distinct data, such as that derived from electronic health records, making it an ideal aid in the diagnosis of difficult to diagnose disorders like PCOS. More

Fledgling technology startups need to hire skilled scientists and engineers to bring their cutting-edge products from the proverbial Silicon Valley garage to the market. But to attract the best and the brightest, startups also must routinely compete with established firms for top talent.
Commonly held views on job-choice decision-making would point to highly sought-after tech workers choosing jobs with established companies that offer the highest pay and benefits, ostensibly leaving resource-constrained startups to sift through a weaker talent pool. But new research co-written by a University of Illinois Urbana-Champaign expert in technology entrepreneurship and scientific labor markets proposes an alternative theory: Some high-ability, in-demand tech workers would prefer to join startup firms despite the lower pay and riskier prospects for the company’s long-term survival because they’re attracted to the startup culture and environment.
Non-monetary benefits such as independence, autonomy and the ability to work on innovative technologies are among the key selling points for talented scientists and engineers who spurn working for a bigger technology firm in favor of a startup, said Michael Roach, a professor of business administration at the Gies College of Business at Illinois.
“Certain workers are willing to take a job for lower pay in exchange for other benefits such as working for a smaller firm and feeling like they’re contributing to the creation of something new and novel,” he said. “For some high-ability tech workers, there’s more significance to being employee number 20 than employee number 2,000.”
The paper, which was published by the journal Management Science, was co-written by Henry Sauermann of the European School of Management and Technology Berlin.
Using a longitudinal survey that followed more than 2,300 science and engineering doctoral students from graduate school through their first job, the researchers found that both an individual’s ability and career preferences strongly predicted post-graduate employment with a startup as opposed to a bigger, more-established tech firm.
“There’s a lot of evidence using U.S. Census and other administrative data that shows that employees at small firms are paid less, which has been interpreted as startups not being able to attract high-ability people,” Roach said. “But we found that startups are able to recruit high-ability workers despite paying their new hires approximately 20% less than established firms.”
The findings are consistent with preference-based job sorting in that working at a startup may be a better fit for some workers, Roach said. More

More than 200 years later, historians are still gleaning some unexpected insights from the French Revolution — not about tyranny or liberty — but rather, inflation.
“Revolutionary France experienced the first modern hyperinflation,” said Louis Rouanet, Ph.D., assistant professor at The University of Texas at El Paso. “Although it happened more than two centuries ago, it offers relevant lessons for today.”
Rouanet is the lead author of the new study, “Assignats or death: The politics and dynamics of hyperinflation in revolutionary France,” published recently in the European Economic Review. A faculty member of the UTEP Department of Economics and Finance, Rouanet is an expert in economic history, specializing in revolutionary France, and a Frenchman himself. The study advances a new framework for understanding the monetary phenomenon hyperinflation, a period of rapid and extreme price increases.
Rouanet’s analysis found that political instability and shifting public expectations were key in explaining the scenario that unfolded between May 1794 and May 1796, when the French revolutionary governments’ decision to issue a paper currency called the assignat led to extreme inflation. Price levels increased more than 50% per month, complicating an already volatile economic situation. The currency was primarily supported by a political group known as the Jacobins, a party whose power waned throughout the revolution.
The French Revolution began at the end of the 18th century when extreme popular discontent with feudal institutions erupted into revolution, Rouanet said. The conflict reshaped the French government and led to the end of the feudal system, a hierarchical system of government that placed the king at the top, nobility and clergy below him, and peasants below all.
During the revolution, the government was bankrupt and expropriated substantial amounts of land and assets held by the Catholic Church in order to sell them. However, they were unable to sell the land fast enough to pay back creditors. To stimulate purchases, the government began issuing a paper currency called assignat. In order to prevent inflation, revolutionary officials promised to retire the assignat from circulation and burn the notes once they were used to buy property, but this commitment was not always honored, prompting public mistrust.
At the same time, the strength of the Jacobin party was weakening. From failing insurrections in Paris and the establishment of a new regime known as the Directory, the key drivers of the assignat were on their way out. More