Aurelia Butler

Researchers at the Beckman Institute for Advanced Science and Technology developed an automated laboratory robot to run complex electrochemical experiments and analyze data.
With affordability and accessibility in mind, the researchers collaboratively created a benchtop robot that rapidly performs electrochemistry. Aptly named the Electrolab, this instrument greatly reduces the effort and time needed for electrochemical studies by automating many basic and repetitive laboratory tasks.
The Electrolab can be used to explore energy storage materials and chemical reactions that promote the use of alternative and renewable power sources like solar or wind energy, which are essential to combating climate change.
“We hope the Electrolab will allow new discoveries in energy storage while helping us share knowledge and data with other electrochemists — and non-electrochemists! We want them to be able to try things they couldn’t before,” said Joaquín Rodríguez-López, a professor in the Department of Chemistry at the University of Illinois Urbana-Champaign.
The interdisciplinary team was co-led by Rodríguez-López and Charles Schroeder, the James Economy professor in the Department of Materials Science and Engineering and a professor of chemical and biomolecular engineering at UIUC. Their work appears in the journal Device.
Electrochemistry is the study of electricity and its relation to chemistry. Chemical reactions release energy that can be converted into electricity — batteries used to power remote controllers or electric vehicles are perfect examples of this phenomenon.
In the opposite direction, electricity can also be used to drive chemical reactions. Electrochemistry can provide a green and sustainable alternative to many reactions that would otherwise require the use of harsh chemicals, and it can even drive chemical reactions that convert greenhouse gasses such as carbon dioxide into chemicals that are useful in other industries. These are relatively simple demonstrations of electrochemistry, but the growing demand to generate and store massive amounts of energy on a much larger scale is currently a prominent challenge. More

Researchers in the Department of Mechanical Engineering at Carnegie Mellon University, in collaboration with paleontologists from Spain and Poland, used fossil evidence to engineer a soft robotic replica of pleurocystitid, a marine organism that existed nearly 450 million years ago and is believed to be one of the first echinoderms capable of movement using a muscular stem.
Published today in The Proceedings of the National Academy of Science (PNAS), the research seeks to broaden modern perspective of animal design and movement by introducing a new a field of study — Paleobionics — aimed at using Softbotics, robotics with flexible electronics and soft materials, to understand the biomechanical factors that drove evolution using extinct organisms.
“Softbotics is another approach to inform science using soft materials to construct flexible robot limbs and appendages. Many fundamental principles of biology and nature can only fully be explained if we look back at the evolutionary timeline of how animals evolved. We are building robot analogues to study how locomotion has changed,” said Carmel Majidi, lead author and Professor of Mechanical Engineering at Carnegie Mellon University.
With humans’ time on earth representing only 0.007% of the planet’s history, the modern-day animal kingdom that influences understanding of evolution and inspires today’s mechanical systems is only a fraction of all creatures that have existed through history.
Using fossil evidence to guide their design and a combination of 3D printed elements and polymers to mimic the flexible columnar structure of the moving appendage, the team demonstrated that pleurocystitids were likely able to move over the sea bottom by means of a muscular stem that pushed the animal forward. Despite the absence of a current day analogue (echinoderms have since evolved to include modern day starfish and sea urchins), pleurocystitids have been of interest to paleontologists due to their pivotal role in echinoderm evolution.
The team determined that wide sweeping movements were likely the most effective motion and that increasing the length of the stem significantly increased the animals’ speed without forcing it to exert more energy.
“Researchers in the bio-inspired robotics community need to pick and choose important features worth adopting from organisms,” explained Richard Desatnik, PhD candidate and co-first author. More

Predicting sudden cardiac death, and perhaps even addressing a person’s risk to prevent future death, may be possible through artificial intelligence (AI) and could offer a new move toward prevention and global health strategies, according to preliminary research to be presented at the American Heart Association’s Resuscitation Science Symposium 2023. The meeting, Nov. 11-12, in Philadelphia is a premier global exchange of the most recent advances related to treating cardiopulmonary arrest and life-threatening traumatic injury.
“Sudden cardiac death, a public health burden, represents 10% to 20% of overall deaths. Predicting it is difficult, and the usual approaches fail to identify high-risk people, particularly at an individual level,” said Xavier Jouven, M.D., Ph.D., the lead author of the study and professor of cardiology and epidemiology at the Paris Cardiovascular Research Center, Inserm U970-University of Paris. “We proposed a new approach not restricted to the usual cardiovascular risk factors but encompassing all medical information available in electronic health records.”
The research team analyzed medical information with AI from registries and databases in Paris, France and Seattle for 25,000 people who had died from sudden cardiac arrest and 70,000 people from the general population, with data from the two groups matched by age, sex and residential area. The data, which represented more than 1 million hospital diagnoses and 10 million medication prescriptions, was gathered from medical records up to ten years prior to each death. Using AI to analyze the data, researchers built nearly 25,000 equations with personalized health factors used to identify those people who were at very high risk of sudden cardiac death. Additionally, they developed a customized risk profile for each of the individuals in the study.
The personalized risk equations included a person’s medical details, such as treatment for high blood pressure and history of heart disease, as well as mental and behavioral disorders including alcohol abuse. The analysis identified those factors most likely to decrease or increase the risk of sudden cardiac death at a particular percentage and time frame, for example, 89% risk of sudden cardiac death within three months.
The AI analysis was able to identify people who had more than 90% of risk to die suddenly, and they represented more than one fourth of all cases of sudden cardiac death.
“We have been working for almost 30 years in the field of sudden cardiac death prediction, however, we did not expect to reach such a high level of accuracy. We also discovered that the personalized risk factors are very different between the participants and are often issued from different medical fields (a mix of neurological, psychiatric, metabolic and cardiovascular data) — a picture difficult to catch for the medical eyes and brain of a specialist in one given field” said Jouven, who is also founder of the Paris Sudden Death Expertise Center. “While doctors have efficient treatments such as correction of risk factors, specific medications and implantable defibrillators, the use of AI is necessary to detect in a given subject a succession of medical information registered over the years that will form a trajectory associated with an increased risk of sudden cardiac death. We hope that with a personalized list of risk factors, patients will be able to work with their clinicians to reduce those risk factors and ultimately decrease the potential for sudden cardiac death.”
Among the study’s limitations are the potential use of the prediction models beyond this research. In addition, the medical data collected in electronic health records sometimes include proxies instead of raw data, and the data collected may be different among countries, requiring an adaptation of the prediction models. More

A speech prosthetic developed by a collaborative team of Duke neuroscientists, neurosurgeons, and engineers can translate a person’s brain signals into what they’re trying to say.
Appearing Nov. 6 in the journal Nature Communications, the new technology might one day help people unable to talk due to neurological disorders regain the ability to communicate through a brain-computer interface.
“There are many patients who suffer from debilitating motor disorders, like ALS (amyotrophic lateral sclerosis) or locked-in syndrome, that can impair their ability to speak,” said Gregory Cogan, Ph.D., a professor of neurology at Duke University’s School of Medicine and one of the lead researchers involved in the project. “But the current tools available to allow them to communicate are generally very slow and cumbersome.”
Imagine listening to an audiobook at half-speed. That’s the best speech decoding rate currently available, which clocks in at about 78 words per minute. People, however, speak around 150 words per minute.
The lag between spoken and decoded speech rates is partially due the relatively few brain activity sensors that can be fused onto a paper-thin piece of material that lays atop the surface of the brain. Fewer sensors provide less decipherable information to decode.
To improve on past limitations, Cogan teamed up with fellow Duke Institute for Brain Sciences faculty member Jonathan Viventi, Ph.D., whose biomedical engineering lab specializes in making high-density, ultra-thin, and flexible brain sensors.
For this project, Viventi and his team packed an impressive 256 microscopic brain sensors onto a postage stamp-sized piece of flexible, medical-grade plastic. Neurons just a grain of sand apart can have wildly different activity patterns when coordinating speech, so it’s necessary to distinguish signals from neighboring brain cells to help make accurate predictions about intended speech. More

Researchers from the Max Planck Institute for Human Development, the Institute for Cognitive Sciences and Technologies (ISTC), and the Norwegian University of Science and Technology developed a collective intelligence approach to increase the accuracy of medical diagnoses. Their work was recently presented in the journal PNAS.
An estimated 250,000 people die from preventable medical errors in the U.S. each year. Many of these errors originate during the diagnostic process. A powerful way to increase diagnostic accuracy is to combine the diagnoses of multiple diagnosticians into a collective solution. However, there has been a dearth of methods for aggregating independent diagnoses in general medical diagnostics. Researchers from the Max Planck Institute for Human Development, the Institute for Cognitive Sciences and Technologies (ISTC), and the Norwegian University of Science and Technology have therefore introduced a fully automated solution using knowledge engineering methods.
The researchers tested their solution on 1,333 medical cases provided by The Human Diagnosis Project (Human Dx), each of which was independently diagnosed by 10 diagnosticians. The collective solution substantially increased diagnostic accuracy: Single diagnosticians achieved 46% accuracy, whereas pooling the decisions of 10 diagnosticians increased accuracy to 76%. Improvements occurred across medical specialties, chief complaints, and diagnosticians’ tenure levels. “Our results show the life-saving potential of tapping into the collective intelligence,” says first author Ralf Kurvers. He is a senior research scientist at the Center for Adaptive Rationality of the Max Planck Institute for Human Development and his research focuses on social and collective decision making in humans and animals.
Collective intelligence has been proven to boost decision accuracy across many domains, such as geopolitical forecasting, investment, and diagnostics in radiology and dermatology (e.g., Kurvers et al., PNAS, 2016). However, collective intelligence has been mostly applied to relatively simple decision tasks. Applications in more open-ended tasks, such as emergency management or general medical diagnostics, are largely lacking due to the challenge of integrating unstandardized inputs from different people. To overcome this hurdle, the researchers used semantic knowledge graphs, natural language processing, and the SNOMED CT medical ontology, a comprehensive multilingual clinical terminology, for standardization.
“A key contribution of our work is that, while the human-provided diagnoses maintain their primacy, our aggregation and evaluation procedures are fully automated, avoiding possible biases in the generation of the final diagnosis and allowing the process to be more time- and cost-efficient,” adds co-author Vito Trianni from the Institute for Cognitive Sciences and Technologies (ISTC) in Rome.
The researchers are currently collaborating — along with other partners — within the HACID project to bring their application one step closer to the market. The EU-funded project will explore a new approach that brings together human experts and AI-supported knowledge representation and reasoning in order to create new tools for decision making in various domains. The application of the HACID technology to medical diagnostics showcases one of the many opportunities to benefit from a digitally based health system and accessible data. More

Researchers from the Universities of Freiburg and Ulm have developed a monolithically integrated photo battery using organic materials.
Networked intelligent devices and sensors can improve the energy efficiency of consumer products and buildings by monitoring their consumption in real time. Miniature devices like these being developed under the concept of the Internet of Things require energy sources that are as compact as possible in order to function autonomously. Monolithically integrated batteries that simultaneously generate, convert, and store energy in a single system could be used for this purpose.
A team of scientists at the University of Freiburg’s Cluster of Excellence Living, Adaptive, and Energy-Autonomous Materials Systems (livMatS) has developed a monolithically integrated photo battery consisting of an organic polymer-based battery and a multi-junction organic solar cell. The battery, presented by Rodrigo Delgado Andrés andDr. Uli Würfel, University Freiburg, and Robin Wessling and Prof. Dr. Birgit Esser, University of Ulm, is the first monolithically integrated photo battery made of organic materials to achieve a discharge potential of 3.6 volts. It is thus among the first systems of this kind capable of powering miniature devices. The team published their results in the journal Energy & Environmental Science.
Combination of a multi-junction solar cell and a dual-ion battery
The researchers developed a scalable method for the photo battery which allows them to manufacture organic solar cells out of five active layers. “The system achieves relatively high voltages of 4.2 volts with this solar cell,” explains Wessling. The team combined this multi-junction solar cell with a so-called dual-ion battery, which is capable of being charged at high currents, unlike the cathodes of conventional lithium batteries. With careful control of illumination intensity and discharge rates, a photo battery constructed in this way is capable of rapid charging in less than 15 minutes at discharge capacities of up to 22 milliampere hours per gram (mAh g-1). In combination with the averaged discharge potential of 3.6 volts, the devices can provide an energy density of 69 milliwatt hours per gram (mWh g-1) and a power density of 95 milliwatts per gram (mW g-1). “Our system thus lays the foundation for more in-depth research and further developments in the area of organic photo batteries,” says Wessling. More

A team of researchers from UCLA has unveiled a first-of-its-kind stable and fully solid-state thermal transistor that uses an electric field to control a semiconductor device’s heat movement.
The group’s study, which will be published in the Nov. 3 issue of Science, details how the device works and its potential applications. With top speed and performance, the transistor could open new frontiers in heat management of computer chips through an atomic-level design and molecular engineering. The advance could also further the understanding of how heat is regulated in the human body.
“The precision control of how heat flows through materials has been a long-held but elusive dream for physicists and engineers,” said the study’s co-author Yongjie Hu, a professor of mechanical and aerospace engineering at the UCLA Samueli School of Engineering.” This new design principle takes a big leap toward that, as it manages the heat movement with the on-off switching of an electric field, just like how it has been done with electrical transistors for decades.”
Electrical transistors are the foundational building blocks of modern information technology. They were first developed by Bell Labs in the 1940s and have three terminals — a gate, a source and a sink. When an electrical field is applied through the gate, it regulates how electricity (in the form of electrons) moves through the chip. These semiconductor devices can amplify or switch electrical signals and power. But as they continue to shrink in size over the years, billions of transistors can fit on one chip, resulting in more heat generated from the movement of electrons, which affects chip performance. Conventional heat sinks passively draw heat away from hotspots, but it has remained a challenge to find a more dynamic control to actively regulate heat.
While there have been efforts in tuning thermal conductivity, their performances have suffered due to reliance on moving parts, ionic motions, or liquid solution components. This has resulted in slow switching speeds for heat movement on the order of minutes or far slower, creating issues in performance reliability as well as incompatibility with semiconductor manufacturing.
The new thermal transistor, which boasts a field effect (the modulation of the thermal conductivity of a material by the application of an external electric field) and a full solid state (no moving parts), offers high performance and compatibility with integrated circuits in semiconductor manufacturing processes. The team’s design incorporates the field effect on charge dynamics at an atomic interface to allow high performance using a negligible power to switch and amplify a heat flux continuously.
The UCLA team demonstrated electrically gated thermal transistors that achieved record-high performance with switching speed of more than 1 megahertz, or 1 million cycles per second. They also offered a 1,300% tunability in thermal conductance and reliable performance for more than 1 million switching cycles. More

‘Hard-to-decarbonize’ (HtD) houses are responsible for over a quarter of all direct housing emissions — a major obstacle to achieving net zero — but are rarely identified or targeted for improvement.
Now a new ‘deep learning’ model trained by researchers from Cambridge University’s Department of Architecture promises to make it far easier, faster and cheaper to identify these high priority problem properties and develop strategies to improve their green credentials.
Houses can be ‘hard to decarbonize’ for various reasons including their age, structure, location, social-economic barriers and availability of data. Policymakers have tended to focus mostly on generic buildings or specific hard-to-decarbonise technologies but the study, published in the journal Sustainable Cities and Society, could help to change this.
Maoran Sun, an urban researcher and data scientist, and his PhD supervisor Dr Ronita Bardhan, who leads Cambridge’s Sustainable Design Group, show that their AI model can classify HtD houses with 90% precision and expect this to rise as they add more data, work which is already underway.
Dr Bardhan said: “This is the first time that AI has been trained to identify hard-to-decarbonize buildings using open source data to achieve this.
“Policymakers need to know how many houses they have to decarbonize, but they often lack the resources to perform detail audits on every house. Our model can direct them to high priority houses, saving them precious time and resources.”
The model also helps authorities to understand the geographical distribution of HtD houses, enabling them to efficiently target and deploy interventions efficiently. More