Computers Math

Researchers at Linköping University, Sweden, have developed a stable high-conductivity polymer ink. The advance paves the way for innovative printed electronics with high energy efficiency. The results have been published in Nature Communications.
Electrically conducting polymers have made possible the development of flexible and lightweight electronic components such as organic biosensors, solar cells, light-emitting diodes, transistors, and batteries.
The electrical properties of the conducting polymers can be tuned using a method known as “doping.” In this method, various dopant molecules are added to the polymer to change its properties. Depending on the dopant, the doped polymer can conduct electricity by the motion of either negatively charged electrons (an “n-type” conductor), or positively charged holes (a “p-type” conductor). Today, the most commonly used conducting polymer is the p-type conductor PEDOT:PSS. PEDOT:PSS has several compelling features such as high electrical conductivity, excellent ambient stability, and most importantly, commercial availability as an aqueous dispersion. However, many electronic devices require a combination of p-types and n-types to function. At the moment, there is no n-type equivalent to PEDOT:PSS.
Researchers at Linköping University, together with colleagues in the US and South Korea, have now developed a conductive n-type polymer ink, stable in air and at high temperatures. This new polymer formulation is known as BBL:PEI.
“This is a major advance that makes the next generation of printed electronic devices possible. The lack of a suitable n-type polymer has been like walking on one leg when designing functional electronic devices. We can now provide the second leg,” says Simone Fabiano, senior lecturer in the Department of Science and Technology at Linköping University.
Chi-Yuan Yang is a postdoc at Linköping University and one of the principal authors of the article published in Nature Communications. He adds:
“Everything possible with PEDOT:PSS is also possible with our new polymer. The combination of PEDOT:PSS and BBL:PEI opens new possibilities for the development of stable and efficient electronic circuits,” says Chi-Yuan Yang.
The new n-type material comes in the form of ink with ethanol as the solvent. The ink can be deposited by simply spraying the solution onto a surface, making organic electronic devices easier and cheaper to manufacture. Also, the ink is more eco-friendly than many other n-type organic conductors currently under development, which instead contain harmful solvents. Simone Fabiano believes that the technology is ready for routine use.
“Large-scale production is already feasible, and we are thrilled to have come so far in a relatively short time. We expect BBL:PEI to have the same impact as PEDOT:PSS. At the same time, much remains to be done to adapt the ink to various technologies, and we need to learn more about the material,” says Simone Fabiano.
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A new mathematical model for the interaction of bacteria in the gut could help design new probiotics and specially tailored diets to prevent diseases. The research, from Chalmers University of Technology in Sweden, was recently published in the journal PNAS.
“Intestinal bacteria have an important role to play in health and the development of diseases, and our new mathematical model could be extremely helpful in these areas,” says Jens Nielsen, Professor of Systems Biology at Chalmers, who led the research.
The new paper describes how the mathematical model performed when making predictions relating to two earlier clinical studies, one involving Swedish infants, and the other adults in Finland with obesity.
The studies involved regular measurements of health indicators, which the researchers compared with the predictions made from their mathematical model — the model proved to be highly accurate in predicting multiple variables, including how a switch from liquid to solid food in the Swedish infants affected their intestinal bacterial composition.
They also measured how the obese adults’ intestinal bacteria changed after a move to a more restricted diet. Again, the model’s predictions proved to be reliably accurate.
“These are very encouraging results, which could enable computer-based design for a very complex system. Our model could therefore be used to for creating personalised healthy diets, with the possibility to predict how adding specific bacteria as novel probiotics could impact a patient’s health,” says Jens Nielsen. More

Engineered, autonomous machines combined with artificial intelligence have long been a staple of science fiction, and often in the role of villain like the Cylons in the “Battlestar Galactica” reboot, creatures composed of biological and engineered materials. But what if these autonomous soft machines were … helpful?
This is the vision of a team of Penn State and U.S. Air Force researchers, outlined in a recent paper in Nature Communications. These researchers produced a soft, mechanical metamaterial that can “think” about how forces are applied to it and respond via programmed reactions. This platform holds great potential for a variety of applications from medical treatments to improving the environment.
“We created soft, mechanical metamaterials with flexible, conductive polymer networks that can compute all digital logic computations,” said Ryan Harne, James F. Will Career Development Associate Professor, Penn State. “Our paper reports a way to create decision-making functionality in engineered materials in a way that could support future soft, autonomous engineered systems that are invested with the basic elements of lifeforms yet are programmed to perform helpful services for people. These could include helping maintain sustainable and robust infrastructure, monitoring of airborne and waterborne contaminants and pathogens, assisting with patient wound healing, and more.”
Human thought processes are based on logic, Harne notes, which is similar to Boolean logic from mathematics. This approach uses binary inputs to process binary control outputs — using only “on” and “off” sequences to represent all thought and cognition. The soft materials that the research team created “think” using the reconfiguration of the conductive polymer networks. Mechanical force, applied to the materials, connects, and reconnects the network.
Using a low voltage input into the materials, the research team created a way for the soft material to decide how to react according to the output voltage signal from the reconfigured conductive polymer network.
The type of logic that Harne and the team uses goes beyond pure mechanical logic, which is a way of using combinations of bistable switches — switches with two stable states — to represent the “0s” and “1s” of a binary number sequence. They found that when they used pure mechanical logic, the researchers ended up getting stuck because certain logical operations cannot be constructed. More

As artificial intelligence has attracted broad interest, researchers are focused on understanding how the brain accomplishes cognition so they can construct artificial systems with general intelligence comparable to humans’ intelligence.
Many have approached this challenge by using conventional silicon microelectronics in conjunction with light. However, the fabrication of silicon chips with electronic and photonic circuit elements is difficult for many physical and practical reasons related to the materials used for the components.
In Applied Physics Letters, by AIP Publishing, researchers at the National Institute of Standards and Technology propose an approach to large-scale artificial intelligence that focuses on integrating photonic components with superconducting electronics rather than semiconducting electronics.
“We argue that by operating at low temperature and using superconducting electronic circuits, single-photon detectors, and silicon light sources, we will open a path toward rich computational functionality and scalable fabrication,” said author Jeffrey Shainline.
Using light for communication in conjunction with complex electronic circuits for computation could enable artificial cognitive systems of scale and functionality beyond what can be achieved with either light or electronics alone.
“What surprised me most was that optoelectronic integration may be much easier when working at low temperatures and using superconductors than when working at room temperatures and using semiconductors,” said Shainline.
Superconducting photon detectors enable detection of a single photon, while semiconducting photon detectors require about 1,000 photons. So not only do silicon light sources work at 4 kelvins, but they also can be 1,000 times less bright than their room temperature counterparts and still communicate effectively.
Some applications, such as chips in cellphones, require working at room temperature, but the proposed technology would still have wide reaching applicability for advanced computing systems.
The researchers plan to explore more complex integration with other superconducting electronic circuits as well as demonstrate all the components that comprise artificial cognitive systems, including synapses and neurons.
Showing that the hardware can be manufactured in a scalable manner, so large systems can be realized at a reasonable cost, will also be important. Superconducting optoelectronic integration could also help create scalable quantum technologies based on superconducting or photonic qubits. Such quantum-neural hybrid systems may also lead to new ways of leveraging the strengths of quantum entanglement with spiking neurons.
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Fiber optic technology is the holy grail of high-speed, long-distance telecommunications. Still, with the continuing exponential growth of internet traffic, researchers are warning of a capacity crunch.
In AVS Quantum Science, by AIP Publishing, researchers from the National Institute of Standards and Technology and the University of Maryland show how quantum-enhanced receivers could play a critical role in addressing this challenge.
The scientists developed a method to enhance receivers based on quantum physics properties to dramatically increase network performance while significantly reducing the error bit rate (EBR) and energy consumption.
Fiber optic technology relies on receivers to detect optical signals and convert them into electrical signals. The conventional detection process, largely as a result of random light fluctuations, produces “shot noise,” which decreases detection ability and increases EBR.
To accommodate this problem, signals must continually be amplified as pulsating light becomes weaker along the optic cable, but there is a limit to maintaining adequate amplification when signals become barely perceptible.
Quantum-enhanced receivers that process up to two bits of classical information and can overcome the shot noise have been demonstrated to improve detection accuracy in laboratory environments. In these and other quantum receivers, a separate reference beam with a single-photon detection feedback is used so the reference pulse eventually cancels out the input signal to eliminate the shot noise.
The researchers’ enhanced receiver, however, can decode as many as four bits per pulse, because it does a better job in distinguishing among different input states.
To accomplish more efficient detection, they developed a modulation method and implemented a feedback algorithm that takes advantage of the exact times of single photon detection. Still, no single measurement is perfect, but the new “holistically” designed communication system yields increasingly more accurate results on average.
“We studied the theory of communications and the experimental techniques of quantum receivers to come up with a practical telecommunication protocol that takes maximal advantage of the quantum measurement,” author Sergey Polyakov said. “With our protocol, because we want the input signal to contain as few photons as possible, we maximize the chance that the reference pulse updates to the right state after the very first photon detection, so at the end of the measurement, the EBR is minimized.”
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Earthquake early warnings can be delivered successfully using a small network of off-the-shelf smartphones attached to building baseboards, according to a study conducted in Costa Rica last year.
In his presentation at the Seismological Society of America (SSA)’s 2021 Annual Meeting, Ben Brooks of the U.S. Geological Survey said the ASTUTI (Alerta Sismica Temprana Utilizando Teléfonos Inteligentes) network of more than 80 stations performed comparably to scientific-grade warning systems.
During six months’ of ASTUTI operation, there were 13 earthquakes that caused noticeable shaking in Costa Rica, including in the city of San Jose where the network was deployed. The system was able to detect and alert on five of these earthquakes, Brooks and his colleagues determined when they “replayed” the seismic events to test their network.
Alerts for the system are triggered when shaking exceeds a certain threshold, equivalent to slightly less than what would be expected for a magnitude 5 earthquake, as measured by the accelerometers that are already built into the phones, Brooks said.
In simulations of the magnitude 7.6 Nicoya earthquake that took place in 2012 in Costa Rica, ASTUTI would have delivered its first alerts on average nine to 13 seconds after the event.
“The performance level over the six months is encouraging,” Brooks said. “Cascadia events in the Pacific Northwest are similar to the Costa Rican subduction zone, and latencies for ShakeAlert in Cascadia are about 10 seconds, so it’s comparable.”
ASTUTI demonstrates the possibilities of lower-cost earthquake early warning for regions that lack the scientific-grade network stations such as those behind ShakeAlert, he noted. More

In 2009, music producer Phil Spector was convicted for the 2003 murder of actress Lana Clarkson, who was shot in the face from a very short distance. He was dressed in white clothes, but no bloodstains were found on his clothing — even though significant backward blood spatter occurred.
How could his clothing remain clean if he was the shooter? This real-life forensic puzzle inspired University of Illinois at Chicago and Iowa State University researchers to explore the fluid physics involved.
In Physics of Fluids, from AIP Publishing, the researchers present theoretical results revealing an interaction of the incoming vortex ring of propellant muzzle gases with backward blood spatter.
A detailed analytical theory of such turbulent self-similar vortex rings was given by this group in earlier work and is linked mathematically to the theory of quantum oscillators.
“In our previous work, we determined the physical mechanism of backward spatter as an inevitable instability triggered by acceleration of a denser fluid, blood, toward a lighter fluid, air,” said Alexander Yarin, a distinguished professor at the University of Illinois at Chicago. “This is the so-called Rayleigh-Taylor instability, which is responsible for water dripping from a ceiling.”
Backward spatter droplets fly from the victim toward the shooter after being splashed by a penetrating bullet. So the researchers zeroed in on how these blood droplets interact with a turbulent vortex ring of muzzle gases moving from the shooter toward the victim.
They predict that backward blood spatter droplets can be entrained — incorporated and swept along within its flow — by the approaching turbulent vortex ring, even being turned around.
“This means that such droplets can even land behind the victim, along with the forward splatter being caused by a penetrated bullet,” said Yarin. “With a certain position of the shooter relative to the victim, it is possible for the shooter’s clothing to remain practically free of bloodstains.”
The physical understanding reached in this work will be helpful in forensic analysis of cases such as that of Clarkson’s murder.
“Presumably, many forensic puzzles of this type can be solved based on sound fluid mechanical principles,” said Yarin.
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A team headed by Dr. Phillip M. Maffettone (currently at National Synchrotron Light Source II in Upton, USA) and Professor Andrew Cooper from the Department of Chemistry and Materials Innovation Factory at the University of Liverpool joined forces with the Bochum-based group headed by Lars Banko and Professor Alfred Ludwig from the Chair of Materials Discovery and Interfaces and Yury Lysogorskiy from the Interdisciplinary Centre for Advanced Materials Simulation. The international team published their report in the journal Nature Computational Science from 19 April 2021.
Previously manual, time-consuming, error-prone
Efficient analysis of X-ray diffraction data (XRD) plays a crucial role in the discovery of new materials, for example for the energy systems of the future. It is used to analyse the crystal structures of new materials in order to find out, for which applications they might be suitable. XRD measurements have already been significantly accelerated in recent years through automation and provide large amounts of data when measuring material libraries. “However, XRD analysis techniques are still largely manual, time-consuming, error-prone and not scalable,” says Alfred Ludwig. “In order to discover and optimise new materials faster in the future using autonomous high-throughput experiments, new methods are required.”
In their publication, the team shows how artificial intelligence can be used to make XRD data analysis faster and more accurate. The solution is an AI agent called Crystallography Companion Agent (XCA), which collaborates with the scientists. XCA can perform autonomous phase identifications from XRD data while it is measured. The agent is suitable for both organic and inorganic material systems. This is enabled by the large-scale simulation of physically correct X-ray diffraction data that is used to train the algorithm.
Expert discussion is simulated
What is more, a unique feature of the agent that the team has adapted for the current task is that it overcomes the overconfidence of traditional neuronal networks: this is because such networks make a final decision even if the data doesn’t support a definite conclusion. Whereas a scientist would communicate their uncertainty and discuss results with other researchers. “This process of decision-making in the group is simulated by an ensemble of neural networks, similar to a vote among experts,” explains Lars Banko. In XCA, an ensemble of neural networks forms the expert panel, so to speak, which submits a recommendation to the researchers. “This is accomplished without manual, human-labelled data and is robust to many sources of experimental complexity,” says Banko.
XCA can also be expanded to other forms of characterisation such as spectroscopy. “By complementing recent advances in automation and autonomous experimentation, this development constitutes an important step in accelerating the discovery of new materials,” concludes Alfred Ludwig.
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