Computers Math

MIT researchers have developed a battery-free, self-powered sensor that can harvest energy from its environment.
Because it requires no battery that must be recharged or replaced, and because it requires no special wiring, such a sensor could be embedded in a hard-to-reach place, like inside the inner workings of a ship’s engine. There, it could automatically gather data on the machine’s power consumption and operations for long periods of time.
The researchers built a temperature-sensing device that harvests energy from the magnetic field generated in the open air around a wire. One could simply clip the sensor around a wire that carries electricity — perhaps the wire that powers a motor — and it will automatically harvest and store energy which it uses to monitor the motor’s temperature.
“This is ambient power — energy that I don’t have to make a specific, soldered connection to get. And that makes this sensor very easy to install,” says Steve Leeb, the Emanuel E. Landsman Professor of Electrical Engineering and Computer Science (EECS) and professor of mechanical engineering, a member of the Research Laboratory of Electronics, and senior author of a paper on the energy-harvesting sensor.
In the paper, which appeared as the featured article in the January issue of the IEEE Sensors Journal, the researchers offer a design guide for an energy-harvesting sensor that lets an engineer balance the available energy in the environment with their sensing needs.
The paper lays out a roadmap for the key components of a device that can sense and control the flow of energy continually during operation.
The versatile design framework is not limited to sensors that harvest magnetic field energy, and can be applied to those that use other power sources, like vibrations or sunlight. It could be used to build networks of sensors for factories, warehouses, and commercial spaces that cost less to install and maintain.

“We have provided an example of a battery-less sensor that does something useful, and shown that it is a practically realizable solution. Now others will hopefully use our framework to get the ball rolling to design their own sensors,” says lead author Daniel Monagle, an EECS graduate student.
Monagle and Leeb are joined on the paper by EECS graduate student Eric Ponce.
A how-to guide
The researchers had to meet three key challenges to develop an effective, battery-free, energy-harvesting sensor.
First, the system must be able to cold start, meaning it can fire up its electronics with no initial voltage. They accomplished this with a network of integrated circuits and transistors that allow the system to store energy until it reaches a certain threshold. The system will only turn on once it has stored enough power to fully operate.
Second, the system must store and convert the energy it harvests efficiently, and without a battery. While the researchers could have included a battery, that would add extra complexities to the system and could pose a fire risk.

“You might not even have the luxury of sending out a technician to replace a battery. Instead, our system is maintenance-free. It harvests energy and operates itself,” Monagle adds.
To avoid using a battery, they incorporate internal energy storage that can include a series of capacitors. Simpler than a battery, a capacitor stores energy in the electrical field between conductive plates. Capacitors can be made from a variety of materials, and their capabilities can be tuned to a range of operating conditions, safety requirements, and available space.
The team carefully designed the capacitors so they are big enough to store the energy the device needs to turn on and start harvesting power, but small enough that the charge-up phase doesn’t take too long.
In addition, since a sensor might go weeks or even months before turning on to take a measurement, they ensured the capacitors can hold enough energy even if some leaks out over time.
Finally, they developed a series of control algorithms that dynamically measure and budget the energy collected, stored, and used by the device. A microcontroller, the “brain” of the energy management interface, constantly checks how much energy is stored and infers whether to turn the sensor on or off, take a measurement, or kick the harvester into a higher gear so it can gather more energy for more complex sensing needs.
“Just like when you change gears on a bike, the energy management interface looks at how the harvester is doing, essentially seeing whether it is pedaling too hard or too soft, and then it varies the electronic load so it can maximize the amount of power it is harvesting and match the harvest to the needs of the sensor,” Monagle explains.
Self-powered sensor
Using this design framework, they built an energy management circuit for an off-the-shelf temperature sensor. The device harvests magnetic field energy and uses it to continually sample temperature data, which it sends to a smartphone interface using Bluetooth.
The researchers used super-low-power circuits to design the device, but quickly found that these circuits have tight restrictions on how much voltage they can withstand before breaking down. Harvesting too much power could cause the device to explode.
To avoid that, their energy harvester operating system in the microcontroller automatically adjusts or reduces the harvest if the amount of stored energy becomes excessive.
They also found that communication — transmitting data gathered by the temperature sensor — was by far the most power-hungry operation.
“Ensuring the sensor has enough stored energy to transmit data is a constant challenge that involves careful design,” Monagle says.
In the future, the researchers plan to explore less energy-intensive means of transmitting data, such as using optics or acoustics. They also want to more rigorously model and predict how much energy might be coming into a system, or how much energy a sensor might need to take measurements, so a device could effectively gather even more data.
“If you only make the measurements you think you need, you may miss something really valuable. With more information, you might be able to learn something you didn’t expect about a device’s operations. Our framework lets you balance those considerations,” Leeb says.
The work is supported, in part, by the Office of Naval Research and The Grainger Foundation. More

Researchers at the Georgia Institute of Technology have developed a light-based means of printing nano-sized metal structures that is significantly faster and cheaper than any technology currently available. It is a scalable solution that could transform a scientific field long reliant on technologies that are prohibitively expensive and slow. The breakthrough has the potential to bring new technologies out of labs and into the world.
Technological advances in many fields rely on the ability to print metallic structures that are nano-sized — a scale hundreds of times smaller than the width of a human hair. Sourabh Saha, assistant professor in the George W. Woodruff School of Mechanical Engineering, and Jungho Choi, a Ph.D. student in Saha’s lab, developed a technique for printing metal nanostructures that is 480 times faster and 35 times cheaper than the current conventional method.
Their research was published in the journal Advanced Materials.
Printing metal on the nanoscale — a technique known as nanopatterning — allows for the creation of unique structures with interesting functions. It is crucial for the development of many technologies, including electronic devices, solar energy conversion, sensors, and other systems.
It is generally believed that high-intensity light sources are required for nanoscale printing. But this type of tool, known as a femtosecond laser, can cost up to half a million dollars and is too expensive for most research labs and small businesses.
“As a scientific community, we don’t have the ability to make enough of these nanomaterials quickly and affordably, and that is why promising technologies often stay limited to the lab and don’t get translated into real-world applications,” Saha said.
“The question we wanted to answer is, ‘Do we really need a high-intensity femtosecond laser to print on the nanoscale?’ Our hypothesis was that we don’t need that light source to get the type of printing we want.”
They searched for a low-cost, low-intensity light that could be focused in a way similar to femtosecond lasers, and chose superluminescent light emitting diodes (SLEDs) for their commercial availability. SLEDs emit light that is a billion times less intense than that of femtosecond lasers.

Saha and Choi set out to create an original projection-style printing technology, designing a system that converts digital images into optical images and displays them on a glass surface. The system operates like digital projectors but produces images that are more sharply focused. They leveraged the unique properties of the superluminescent light to generate sharply focused images with minimal defects.
They then developed a clear ink solution made up of metal salt and added other chemicals to make sure the liquid could absorb light. When light from their projection system hit the solution, it caused a chemical reaction that converted the salt solution into metal. The metal nanoparticles stuck to the surface of the glass, and the agglomeration of the metal particles creates the nanostructures. Because it is a projection type of printing, it can print an entire structure in one go, rather than point by point — making it much faster.
After testing the technique, they found that projection-style nanoscale printing is possible even with low-intensity light, but only if the images are sharply focused. Saha and Choi believe that researchers can readily replicate their work using commercially available hardware. Unlike a pricey femtosecond laser, the type of SLED that Saha and Choi used in their printer costs about $3,000.
“At present, only top universities have access to these expensive technologies, and even then, they are located in shared facilities and are not always available,” Choi said. “We want to democratize the capability of nanoscale 3D printing, and we hope our research opens the door for greater access to this type of process at a low cost.”
The researchers say their technique will be particularly useful for people working in the fields of electronics, optics, and plasmonics, which all require a variety of complex metallic nanostructures.
“I think the metrics of cost and speed have been greatly undervalued in the scientific community that works on fabrication and manufacturing of tiny structures,” Saha said.
“In the real world, these metrics are important when it comes to translating discoveries from the lab to industry. Only when we have manufacturing techniques that take these metrics into account will we be able to fully leverage nanotechnology for societal benefit.” More

Viral respiratory infections are a significant public health concern. A study publishing January 18 in the open access journal PLOS Biology by Marta Galanti at Columbia University, New York, United States and colleagues used longitudinal cohort data to create an interactive, publicly-available website, The Virome of Manhattan Project: Virome Data Explorer to visualize cohort characteristics, infection events, and illness severity factors.
Viral respiratory infections may lead to severe outcomes. However, better understanding of host response, host genetic makeup, and bacterial coinfections is required to develop effective therapeutics. In order to contribute to epidemiological research on factors contributing to disease severity, the researchers conducted a longitudinal cohort study, surveilling respiratory viruses for 19 months between 2016-2018 in New York City. They analyzed over 800 nasopharyngeal samples with clinical data, including self-reported symptoms from 214 participants. From these data, researchers created the Virome Data Explorer, a publicly-available database. Users can access cohort data to visualize and analyze changes and patterns in infections, symptoms, and illness outcomes.
While the database shares important cohort data related to infections, symptoms, and gene activity, the project has several limitations. Adults over the age of 65 were excluded from the cohort, even though according to the authors, respiratory viruses may lead to “extremely serious complications, particularly in infants, elders, and immunocompromised hosts.” Ages of children under 10 were not stratified, obscuring symptom and illness information specific to infants, another high-risk demographic. Vaccination status, immunocompromised conditions, and medicine uptake during infection course were also not among the data collected from study participants, which may limit the applications of the Virome Data Explorer.
According to the authors, “We present a cohort study, consisting of hundreds of samples, that depicts the transcriptional changes driven by respiratory viral infection. We have compiled these data to build a publicly-available, user-friendly web-based resource where any user can compare, longitudinally over the course of 19 months, patterns of viral positivity, symptomatology and transcriptomic changes for the individuals enrolled.”
The authors add, “This is a resource paper aiming at characterizing the host response to common and often asymptomatic viral respiratory infections. We collected and made available a 2-year longitudinal dataset including molecular data and symptoms records for over 100 participants from different age groups in NYC.” More

Two insect-like robots, a mini-bug and a water strider, developed at Washington State University, are the smallest, lightest and fastest fully functional micro-robots ever known to be created.
Such miniature robots could someday be used for work in areas such as artificial pollination, search and rescue, environmental monitoring, micro-fabrication or robotic-assisted surgery. Reporting on their work in the proceedings of the IEEE Robotics and Automation Society’s International Conference on Intelligent Robots and Systems, the mini-bug weighs in at eight milligrams while the water strider weighs 55 milligrams. Both can move at about six millimeters a second.
“That is fast compared to other micro-robots at this scale although it still lags behind their biological relatives,” said Conor Trygstad, a PhD student in the School of Mechanical and Materials Engineering and lead author on the work. An ant typically weighs up to five milligrams and can move at almost a meter per second.
The key to the tiny robots is their tiny actuators that make the robots move. Trygstad used a new fabrication technique to miniaturize the actuator down to less than a milligram, the smallest ever known to have been made.
“The actuators are the smallest and fastest ever developed for micro-robotics,” said Néstor O. Pérez-Arancibia, Flaherty Associate Professor in Engineering at WSU’s School of Mechanical and Materials Engineering who led the project.
The actuator uses a material called a shape memory alloy that is able to change shapes when it’s heated. It is called ‘shape memory’ because it remembers and then returns to its original shape. Unlike a typical motor that would move a robot, these alloys don’t have any moving parts or spinning components.
“They’re very mechanically sound,” said Trygstad. “The development of the very lightweight actuator opens up new realms in micro-robotics.”
Shape memory alloys are not generally used for large-scale robotic movement because they are too slow. In the case of the WSU robots, however, the actuators are made of two tiny shape memory alloy wires that are 1/1000 of an inch in diameter. With a small amount of current, the wires can be heated up and cooled easily, allowing the robots to flap their fins or move their feet at up to 40 times per second. In preliminary tests, the actuator was also able to lift more than 150 times its own weight.

Compared to other technologies used to make robots move, the SMA technology also requires only a very small amount of electricity or heat to make them move.
“The SMA system requires a lot less sophisticated systems to power them,” said Trygstad.
Trygstad, an avid fly fisherman, has long observed water striders and would like to further study their movements. While the WSU water strider robot does a flat flapping motion to move itself, the natural insect does a more efficient rowing motion with its legs, which is one of the reasons that the real thing can move much faster.
The researchers would like to copy another insect and develop a water strider-type robot that can move across the top of the water surface as well as just under it. They are also working to use tiny batteries or catalytic combustion to make their robots fully autonomous and untethered from a power supply. More

Researchers at Kyushu University, in collaboration with Osaka University and the Fine Ceramics Center, have developed a framework that uses machine learning to speed up the discovery of materials for green energy technology. Using the new approach, the researchers identified and successfully synthesized two new candidate materials for use in solid oxide fuel cells — devices that can generate energy using fuels like hydrogen, which don’t emit carbon dioxide. Their findings, which were reported in the journal, Advanced Energy Materials, could also be used to accelerate the search for other innovative materials beyond the energy sector.
In response to a warming climate, researchers have been developing new ways to generate energy without using fossil fuels. “One path to carbon neutrality is by creating a hydrogen society. However, as well as optimizing how hydrogen is made, stored and transported, we also need to boost the power-generating efficiency of hydrogen fuel cells,” explains Professor Yoshihiro Yamazaki, of Kyushu University’s Department of Materials Science and Technology, Platform of Inter-/Transdisciplinary Energy Research (Q-PIT).
To generate an electric current, solid oxide fuel cells need to be able to efficiently conduct hydrogen ions (or protons) through a solid material, known as an electrolyte. Currently, research into new electrolyte materials has focused on oxides with very specific crystal arrangements of atoms, known as a perovskite structure.
“The first proton-conducting oxide discovered was in a perovskite structure, and new high-performing perovskites are continually being reported,” says Professor Yamazaki. “But we want to expand the discovery of solid electrolytes to non-perovskite oxides, which also have the capability of conducting protons very efficiently.”
However, discovering proton-conducting materials with alternative crystal structures via traditional “trial and error” methods has numerous limitations. For an electrolyte to gain the ability to conduct protons, small traces of another substance, known as a dopant, must be added to the base material. But with many promising base and dopant candidates — each with different atomic and electronic properties — finding the optimal combination that enhances proton conductivity becomes difficult and time-consuming.
Instead, the researchers calculated the properties of different oxides and dopants. They then used machine learning to analyze the data, identify the factors that impact the proton conductivity of a material, and predict potential combinations.
Guided by these factors, the researchers then synthesized two promising materials, each with unique crystal structures, and assessed how well they conducted protons. Remarkably, both materials demonstrated proton conductivity in just a single experiment.
One of the materials, the researchers highlighted, is the first-known proton conductor with a sillenite crystal structure. The other, which has a eulytite structure, has a high-speed proton conduction path that is distinct from the conduction paths seen in perovskites. Currently, the performance of these oxides as electrolytes is low, but with further exploration, the research team believes their conductivity can be improved.
“Our framework has the potential to greatly expand the search space for proton-conducting oxides, and therefore significantly accelerate advancements in solid oxide fuel cells. It’s a promising step forward to realizing a hydrogen society,” concludes Professor Yamazaki. “With minor modifications, this framework could also be adapted to other fields of materials science, and potentially accelerate the development of many innovative materials.” More

Quasicrystals are intermetallic materials that have garnered significant attention from researchers aiming to advance condensed matter physics understanding. Unlike normal crystals, in which atoms are arranged in an ordered repeating pattern, quasicrystals have non-repeating ordered patterns of atoms. Their unique structure leads to many exotic and interesting properties, which are particularly useful for practical applications in spintronics and magnetic refrigeration.
A unique quasicrystal variant, known as the Tsai-type icosahedral quasicrystal (iQC) and their cubic approximant crystals (ACs), display intriguing characteristics. These include long-range ferromagnetic (FM) and anti-ferromagnetic (AFM) orders, as well as unconventional quantum critical phenomenon, to name a few. Through precise compositional adjustments, these materials can also exhibit intriguing features like aging, memory, and rejuvenation, making them suitable for the development of next-generation magnetic storage devices. Despite their potential, however, the magnetic phase diagram of these materials remains largely unexplored.
To uncover more, a team of researchers, led by Professor Ryuji Tamura from the Department of Materials Science and Technology at Tokyo University of Science (TUS) in collaboration with researchers fromTohoku University recently conducted magnetization and powder neutron diffraction (PND) experiments on the non-Heisenberg Tsai-type 1/1 gold-gallium-terbium AC.
“For the first time, the phase diagrams of the non-Heisenberg Tsai-type AC have been unravelled. This will boost applied physics research on magnetic refrigeration and spintronics,” remarks Professor Tamura.
Through several experiments, the researchers developed the first comprehensive magnetic phase diagram of the non-Heisenberg Tsai-type AC, covering a broad range of electron-per-atom (e/a) ratios (a parameter crucial for understanding the fundamental nature of QCs). Additionally, measurements using the powder neutron diffraction (PND) revealed the presence of a noncoplanar whirling AFM order at an e/a ratio of 1.72 and a noncoplanar whirling FM order at the e/a ratio of 1.80. The team further elucidated the ferromagnetic and anti-ferromagnetic phase selection rule of magnetic interactions by analyzing the relative orientation of magnetic moments between nearest-neighbour and next-nearest neighbour sites.
Professor Tamura adds that their findings open up new doors for the future of condensed matter physics. “These results offer important insights into the intricate interplay between magnetic interactions in non-Heisenberg Tsai-type ACs. They lay the foundation for understanding the intriguing properties of not only non-Heisenberg ACs but also non-Heisenberg iQCs that are yet to be discovered.”
In summary, the present breakthrough propels condensed matter physics and quasicrystal research into uncharted territories, paving the way for advanced electronic devices and next-generation refrigeration technologies. More

In a groundbreaking study published on January 18, 2024, in Cancer Discovery, scientists at University of California San Diego School of Medicine leveraged a machine learning algorithm to tackle one of the biggest challenges facing cancer researchers: predicting when cancer will resist chemotherapy.
All cells, including cancer cells, rely on complex molecular machinery to replicate DNA as part of normal cell division. Most chemotherapies work by disrupting this DNA replication machinery in rapidly dividing tumor cells. While scientists recognize that a tumor’s genetic composition heavily influences its specific drug response, the vast multitude of mutations found within tumors has made prediction of drug resistance a challenging prospect.
The new algorithm overcomes this barrier by exploring how numerous genetic mutations collectively influence a tumor’s reaction to drugs that impede DNA replication. Specifically, they tested their model on cervical cancer tumors, successfully forecasting responses to cisplatin, one of the most common chemotherapy drugs. The model was able to identify tumors at most risk for treatment resistance and was also able to identify much of the underlying molecular machinery driving treatment resistance.
“Clinicians were previously aware of a few individual mutations that are associated with treatment resistance, but these isolated mutations tended to lack significant predictive value. The reason is that a much larger number of mutations can shape a tumor’s treatment response than previously appreciated,” Trey Ideker, PhD, professor in Department of Medicine at UC San Diego of Medicine, explained. “Artificial intelligence bridges that gap in our understanding, enabling us to analyze a complex array of thousands of mutations at once.”
One of the challenges in understanding how tumors respond to drugs is the inherent complexity of DNA replication — a mechanism targeted by numerous cancer drugs.
“Hundreds of proteins work together in complex arrangements to replicate DNA,” Ideker noted. “Mutations in any one part of this system can change how the entire tumor responds to chemotherapy.”
The researchers focused on the standard set of 718 genes commonly used in clinical genetic testing for cancer classification, using mutations within these genes as the initial input for their machine learning model. After training it with publicly accessible drug response data, the model pinpointed 41 molecular assemblies — groups of collaborating proteins — where genetic alterations influence drug efficacy.

“Cancer is a network-based disease driven by many interconnected components, but previous machine learning models for predicting treatment resistance don’t always reflect this,” said Ideker. “Rather than focusing on a single gene or protein, our model evaluates the broader biochemical networks vital for cancer survival.”
After training their model, the researchers put it to the test in cervical cancer, in which roughly 35% of tumors persist after treatment. The model was able to accurately identify tumors that were susceptible to therapy, which were associated with improved patient outcomes. The model also effectively pinpointed tumors likely to resist treatment.
Further still, beyond forecasting treatment responses, the model helped shed light on its decision-making process by identifying the protein assemblies driving treatment resistance in cervical cancer. The researchers emphasize that this aspect of the model — the ability to interpret its reasoning — is key to the model’s success and also for building trustworthy AI systems.
“Unraveling an AI model’s decision-making process is crucial, sometimes as important as the prediction itself,” said Ideker. “Our model’s transparency is one of its strengths, first because it builds trust in the model, and second because each of these molecular assemblies we’ve identified becomes a potential new target for chemotherapy. We’re optimistic that our model will have broad applications in not only enhancing current cancer treatment, but also in pioneering new ones.” More

When you try a new restaurant or book a hotel, do you consider the online reviews? Do you submit online reviews yourself? Do you pay attention if they are filtered and moderated? Does that impact your own online review submissions?
A research team comprising of Rensselaer Polytechnic Institute’s T. Ravichandran, Ph.D., professor in the Lally School of Management, and Jason Kuruzovich, Ph.D., associate professor in the Lally School of Management; and Lianlian Jiang, Ph.D., assistant professor in the Bauer College of Business at the University of Houston, examined these questions in recently published research. In a world where businesses thrive or die by online reviews, it is important to consider the implications of a platform’s review moderation policies, the transparency of those policies, and how that affects the reviews that are submitted.
“In 2010, Yelp debuted a video to help users understand how its review filter works and why it was necessary,” said Jiang. “Then, Yelp added a section to display filtered reviews. Previously, Yelp did not disclose information about its review filter. This change presented the perfect opportunity to examine the effect of policy transparency on submitted reviews.”
Ravichandran and team compared reviews of over 1,000 restaurants on Yelp to those same restaurants on TripAdvisor, whose practices remained unchanged and was not transparent about its review filter. They used a difference-in-difference (DID) approach. They found that the number of reviews submitted to Yelp decreased. Those that were submitted were increasingly negative and shorter in length compared to TripAdvisor. Also, the more positive a review, the shorter it was.
“Platforms are pressured to have content guidelines and take measures to prevent fraud and ensure that reviews are legitimate and helpful,” said Ravichandran. “However, most platforms are not transparent about their policies, leading consumers to suspect that reviews are manipulated to increase profit under the guise of filtering fraudulent content.”
Platforms use sophisticated software to flag and filter reviews. Once a review is flagged, it is filtered out and not displayed, and it is not factored into the overall rating for a business.
“Whether or not to be transparent about review filters is a critical decision for platforms with many considerations,” said Kuruzovich.
Users may put in less time and effort into their reviews if they suspect that they have a significant chance of being filtered, or they may do the opposite to make their reviews less likely to be filtered. Since most fake reviews are overly positive, users may assume that positive reviews are most likely to be filtered and act accordingly. However, with a transparent policy, those who submit fake reviews may be incentivized to change their ways.
“Review moderation transparency comes at a cost for platforms,” said Ravichandran. “Users reduce their contribution investment, or the amount of time and effort that they put into their reviews. This, in turn, affects the quality and characteristics of reviews. Although transparency helps to position a platform as unbiased toward advertisers, the resultant decrease in the number of reviews submitted impacts the platform’s usefulness to consumers.”
“This research informs businesses on best practices and consumer behavior in the digital world,” said Chanaka Edirisinghe, Ph.D., acting dean of the Lally School of Management. “Online reviews pose great opportunity for firms, but also raise complex questions. Platforms must earn the trust of users without sacrificing engagement.” More