Aurelia Butler

Depending on their location, cancer cells within a three-dimensional (3D) tumor structure can have different microenvironments. Cells in the core of the tumor receive less oxygen (hypoxia) and nutrients than those in the periphery. These varying conditions can drive differences in cell growth rates and drug sensitivities, highlighting the need to study 3D tumor models in lab settings. Until recently, conventional methods used to create such tumor spheroids were time-consuming, produced inconsistent results and involved high setup costs.
Investigators at Brigham and Women’s Hospital, a founding member of the Mass General Brigham healthcare system, developed a low-cost, high-throughput device that can reliably generate uniform tumor spheroids. The study describes how to assemble the ‘Do-It-Yourself (DIY)’ device from parts totaling less than $7, including a coin-vibrating motor commonly found in cell phones.
By vibrating a suspension of cancer cells flowing rapidly out of a fine nozzle, the team was able to create nearly 4000 equally sized droplets per minute. They found that cancer cells within the droplets aggregated to form tumor spheroids with hypoxic cores and exhibited proliferation markers typical of in vivo tumors.
The tumor spheroids also demonstrated clinically typical responses to chemotherapy, with cancer cells at the hypoxic core driving tumor survival and drug resistance. These findings, the authors suggest, could help overcome the limitations of traditional two-dimensional cancer cell cultures and provide insights for improved drug development.
“We developed a simple, DIY method for reliable preclinical testing of anti-cancer drugs,” said corresponding author Hae Lin Jang, PhD, of the Center for Engineered Therapeutics. “The cost of devices often acts as a barrier to cancer research. Low-cost, simple-to-operate systems like ours are essential to democratize cancer research and make science more accessible.”
First author Bumseok Namgung, PhD, of the Center for Engineered Therapeutics added, “Our simple and low-cost system facilitates the anti-cancer drug research by enabling high-throughput drug screening.” More

For over three decades, a highly accurate early diagnostic test for ovarian cancer has eluded physicians. Now, scientists in the Georgia Tech Integrated Cancer Research Center (ICRC) have combined machine learning with information on blood metabolites to develop a new test able to detect ovarian cancer with 93 percent accuracy among samples from the team’s study group.
John McDonald, professor emeritus in the School of Biological Sciences, founding director of the ICRC, and the study’s corresponding author, explains that the new test’s accuracy is better in detecting ovarian cancer than existing tests for women clinically classified as normal, with a particular improvement in detecting early-stage ovarian disease in that cohort.
The team’s results and methodologies are detailed in a new paper, “A Personalized Probabilistic Approach to Ovarian Cancer Diagnostics,” published in the March 2024 online issue of the medical journalGynecologic Oncology.Based on their computer models, the researchers have developed what they believe will be a more clinically useful approach to ovarian cancer diagnosis — whereby a patient’s individual metabolic profile can be used to assign a more accurate probability of the presence or absence of the disease.
“This personalized, probabilistic approach to cancer diagnostics is more clinically informative and accurate than traditional binary (yes/no) tests,” McDonald says. “It represents a promising new direction in the early detection of ovarian cancer, and perhaps other cancers as well.”
The study co-authors also include Dongjo Ban, a Bioinformatics Ph.D. student in McDonald’s lab; Research ScientistsStephen N. Housley,Lilya V. Matyunina, andL.DeEtte (Walker) McDonald; Regents’ ProfessorJeffrey Skolnick, who also serves as Mary and Maisie Gibson Chair in the School of Biological Sciences and Georgia Research Alliance Eminent Scholar in Computational Systems Biology; and two collaborating physicians: University of North Carolina Professor Victoria L. Bae-Jump and Ovarian Cancer Institute of Atlanta Founder and Chief Executive OfficerBenedict B. Benigno. Members of the research team are forming a startup to transfer and commercialize the technology, and plan to seek requisite trials and FDA approval for the test.
Silent killer
Ovarian cancer is often referred to as the silent killer because the disease is typically asymptomatic when it first arises — and is usually not detected until later stages of development, when it is difficult to treat.

McDonald explains that while the average five-year survival rate for late-stage ovarian cancer patients, even after treatment, is around 31 percent — but that if ovarian cancer is detected and treated early, the average five-year survival rate is more than 90 percent.
“Clearly, there is a tremendous need for an accurate early diagnostic test for this insidious disease,” McDonald says.
And although development of an early detection test for ovarian cancer has been vigorously pursued for more than three decades, the development of early, accurate diagnostic tests has proven elusive. Because cancer begins on the molecular level, McDonald explains, there are multiple possible pathways capable of leading to even the same cancer type.
“Because of this high-level molecular heterogeneity among patients, the identification of a single universal diagnostic biomarker of ovarian cancer has not been possible,” McDonald says. “For this reason, we opted to use a branch of artificial intelligence — machine learning — to develop an alternative probabilistic approach to the challenge of ovarian cancer diagnostics.”
Metabolic profiles
Georgia Tech co-author Dongjo Ban, whose thesis research contributed to the study, explains that “because end-point changes on the metabolic level are known to be reflective of underlying changes operating collectively on multiple molecular levels, we chose metabolic profiles as the backbone of our analysis.”
“The set of human metabolites is a collective measure of the health of cells,” adds co-author Jeffrey Skolnick, “and by not arbitrary choosing any subset in advance, one lets the artificial intelligence figure out which are the key players for a given individual.”

Mass spectrometry can identify the presence of metabolites in the blood by detecting their mass and charge signatures. However, Ban says, the precise chemical makeup of a metabolite requires much more extensive characterization.
Ban explains that because the precise chemical composition of less than seven percent of the metabolites circulating in human blood have, thus far, been chemically characterized, it is currently impossible to accurately pinpoint the specific molecular processes contributing to an individual’s metabolic profile.
However, the research team recognized that, even without knowing the precise chemical make-up of each individual metabolite, the mere presence of different metabolites in the blood of different individuals, as detected by mass spectrometry, can be incorporated as features in the building of accurate machine learning-based predictive models (similar to the use of individual facial features in the building of facial pattern recognition algorithms).
“Thousands of metabolites are known to be circulating in the human bloodstream, and they can be readily and accurately detected by mass spectrometry and combined with machine learning to establish an accurate ovarian cancer diagnostic,” Ban says.
A new probabilistic approach
The researchers developed their integrative approach by combining metabolomic profiles and machine learning-based classifiers to establish a diagnostic test with 93 percent accuracy when tested on 564 women from Georgia, North Carolina, Philadelphia and Western Canada. 431 of the study participants were active ovarian cancer patients, and while the remaining 133 women in the study did not have ovarian cancer.
Further studies have been initiated to study the possibility that the test is able to detect very early-stage disease in women displaying no clinical symptoms, McDonald says.
McDonald anticipates a clinical future where a person with a metabolic profile that falls within a score range that makes cancer highly unlikely would only require yearly monitoring. But someone with a metabolic score that lies in a range where a majority (say, 90%) have previously been diagnosed with ovarian cancer would likely be monitored more frequently — or perhaps immediately referred for advanced screening. More

Rice University scientists have discovered a first-of-its-kind material, a 3D crystalline metal in which quantum correlations and the geometry of the crystal structure combine to frustrate the movement of electrons and lock them in place.
The find is detailed in a study published in Nature Physics. The paper also describes the theoretical design principle and experimental methodology that guided the research team to the material. One part copper, two parts vanadium and four parts sulfur, the alloy features a 3D pyrochlore lattice consisting of corner-sharing tetrahedra.
“We look for materials where there are potentially new states of matter or new exotic features that haven’t been discovered,” said study co-corresponding author Ming Yi, a Rice experimental physicist.
Quantum materials are a likely place to look, especially if they host strong electron interactions that give rise to quantum entanglement. Entanglement leads to strange electronic behaviors, including frustrating the movement of electrons to the point where they become locked in place.
“This quantum interference effect is analogous to waves rippling across the surface of a pond and meeting head-on,” Yi said. “The collision creates a standing wave that does not move. In the case of geometrically frustrated lattice materials, it’s the electronic wave functions that destructively interfere.”
Electron localization in metals and semimetals produces flat electronic bands, or flat bands. In recent years, physicists have found that the geometric arrangement of atoms in some 2D crystals, like Kagome lattices, can also produce flat bands. The new study provides empirical evidence of the effect in a 3D material.
Using an experimental technique called angle-resolved photoemission spectroscopy, or ARPES, Yi and study lead author Jianwei Huang, a postdoctoral researcher in her lab, detailed the band structure of the copper-vanadium-sulfur material and found it hosted a flat band that is unique in several ways.

“It turns out that both types of physics are important in this material,” Yi said. “The geometric frustration aspect was there, as theory had predicted. The pleasant surprise was that there were also correlation effects that produced the flat band at the Fermi level, where it can actively participate in determining the physical properties.”
In solid-state matter, electrons occupy quantum states that are divided in bands. These electronic bands can be imagined as rungs on a ladder, and electrostatic repulsion limits the number of electrons that can occupy each rung. Fermi level, an inherent property of materials and a crucial one for determining their band structure, refers to the energy level of the highest occupied position on the ladder.
Rice theoretical physicist and study co-corresponding author Qimiao Si, whose research group identified the copper-vanadium alloy and its pyrochlore crystal structure as being a possible host for combined frustration effects from geometry and strong electron interactions, likened the discovery to finding a new continent.
“It’s the very first work to really show not only this cooperation between geometric- and interaction-driven frustration, but also the next stage, which is getting electrons to be in the same space at the top of the (energy) ladder, where there’s a maximal chance of their reorganizing into interesting and potentially functional new phases,” Si said.
He said the predictive methodology or design principle that his research group used in the study may also prove useful to theorists who study quantum materials with other crystal lattice structures.
“The pyrochlore is not the only game in town,” Si said. “This is a new design principle that allows theorists to predictively identify materials in which flat bands arise due to strong electron correlations.”
Yi said there is also plenty of room for further experimental exploration of pyrochlore crystals.

“This is just the tip of the iceberg,” she said. “This is 3D, which is new, and just given how many surprising findings there have been on Kagome lattices, I’m envisioning that there could be equally or maybe even more exciting discoveries to be made in the pyrochlore materials.”
The research team included 10 Rice researchers from four laboratories. Physicist Pengcheng Dai’s research group produced the many samples needed for experimental verification, and Boris Yakobson’s research group in the Department of Materials Science and NanoEngineering performed first-principle calculations that quantified the flat-band effects produced by geometric frustration. ARPES experiments were conducted at Rice and at the SLAC National Accelerator Laboratory’s Stanford Synchrotron Radiation Lightsource in California and Brookhaven National Laboratory’s National Synchrotron Light Source II in New York, and the team included collaborators from SLAC, Brookhaven and the University of Washington.
The research used resources supported by a Department of Energy (DOE) contract to SLAC (DE-AC02-76SF00515) and was supported by grants from the Gordon and Betty Moore Foundation’s Emergent Phenomena in Quantum Systems Initiative (GBMF9470), the Robert A. Welch Foundation (C-2175, C-1411, C-1839), the DOE’s Office of Basic Energy Sciences (DE-SC0018197), the Air Force Office of Scientific Research (FA9550-21-1-0343, FA9550-21-1-0356), the National Science Foundation (2100741), the Office of Naval Research (ONR) (N00014-22-1-2753) and the ONR-managed Vannevar Bush Faculty Fellows program of the Department of Defense Basic Research Office (ONR-VB N00014-23-1-2870). More

Sensors that monitor infrastructure, such as bridges or buildings, or are used in medical devices, such as prostheses for the deaf, require a constant supply of power. The energy for this usually comes from batteries, which are replaced as soon as they are empty. This creates a huge waste problem. An EU study forecasts that in 2025, 78 million batteries will end up in the rubbish every day.
A new type of mechanical sensor, developed by researchers led by Marc Serra-​Garcia and ETH geophysics professor Johan Robertsson, could now provide a remedy. Its creators have already applied for a patent for their invention and have now presented the principle in the journal Advanced Functional Materials.
Certain sound waves cause the sensor to vibrate
“The sensor works purely mechanically and doesn’t require an external energy source. It simply utilises the vibrational energy contained in sound waves,” Robertsson says.
Whenever a certain word is spoken or a particular tone or noise is generated, the sound waves emitted — and only these — cause the sensor to vibrate. This energy is then sufficient to generate a tiny electrical pulse that switches on an electronic device that has been switched off.
The prototype that the researchers developed in Robertsson’s lab at the Switzerland Innovation Park Zurich in Dübendorf has already been patented. It can distinguish between the spoken words “three” and “four.” Because the word “four” has more sound energy that resonates with the sensor compared to the word “three,” it causes the sensor to vibrate, whereas “three” does not. That means the word “four” could switch on a device or trigger further processes. Nothing would happen with “three.”
Newer variants of the sensor should be able to distinguish between up to twelve different words, such as standard machine commands like “on,” “off,” “up” and “down.” Compared to the palm-​sized prototype, the new versions are also much smaller — about the size of a thumbnail — and the researchers are aiming to miniaturise them further.

Metamaterial without problematic substances
The sensor is what is known as a metamaterial: it’s not the material used that gives the sensor its special properties, but rather the structure. “Our sensor consists purely of silicone and contains neither toxic heavy metals nor any rare earths, as conventional electronic sensors do,” Serra-​Garcia says.
The sensor comprises dozens of identical or similarly structured plates that are connected to each other via tiny bars. These connecting bars act like springs. The researchers used computer modelling and algorithms to develop the special design of these microstructured plates and work out how to attach them to each other. It is the springs that determine whether or not a particular sound source sets the sensor in motion.
Monitoring infrastructure
Potential use cases for these battery-​free sensors include earthquake or building monitoring. They could, for example, register when a building develops a crack that has the right sound or wave energy.
There is also interest in battery-​free sensors for monitoring decommissioned oil wells. Gas can escape from leaks in boreholes, producing a characteristic hissing sound. Such a mechanical sensor could detect this hissing and trigger an alarm without constantly consuming electricity — making it far cheaper and requiring much less maintenance.
Sensor for medical implants
Serra-​Garcia also sees applications in medical devices, such as cochlear implants. These prostheses for the deaf require a permanent power supply for signal processing from batteries. Their power supply is located behind the ear, where there is no room for large battery packs. That means the wearers of such devices must replace the batteries every twelve hours. The novel sensors could also be used for the continuous measurement of eye pressure. “There isn’t enough space in the eye for a sensor with a battery,” he says.
“There’s a great deal of interest in zero-​energy sensors in industry, too,” Serra-​Garcia adds. He no longer works at ETH but at AMOLF, a public research institute in the Netherlands, where he and his team are refining the mechanical sensors. Their aim is to launch a solid prototype by 2027. “If we haven’t managed to attract anyone’s interest by then, we might found our own start-​up.” More

Global warming continues to pose a threat to human society and the ecological systems, and carbon dioxide accounts for the largest proportion of the greenhouse gases that dominate climate warming. To combat climate change and move towards the goal of carbon neutrality, researchers from The Hong Kong Polytechnic University (PolyU) have developed a durable, highly selective and energy-efficient carbon dioxide (CO2) electroreduction system that can convert CO2 into ethylene for industrial purposes to provide an effective solution for reducing CO2 emissions. This research was recently published in Nature Energy and won a Gold Medal at the 48th International Exhibition of Inventions Geneva in Switzerland.
Ethylene (C2H4) is one of the most in-demand chemicals globally and is mainly used in the manufacture of polymers such as polyethylene, which, in turn, can be used to make plastics and chemical fibres commonly used in daily life. However, it is still mostly obtained from petrochemical sources and the production process involves the creation of a very significant carbon footprint.
Led by Prof. Daniel LAU, Chair Professor of Nanomaterials and Head of the Department of Applied Physics, the research team adopted the method of electrocatalytic CO2 reduction — using green electricity to convert carbon dioxide into ethylene, providing a more environmentally friendly alternative and stable ethylene production. The research team is working to promote this emerging technology to bring it closer to mass production, closing the carbon loop and ultimately achieving carbon neutrality.
Prof. Lau’s innovation is to dispense with the alkali-metal electrolyte and use pure water as a metal-free anolyte to prevent carbonate formation and salt deposition. The research team denotes their design the APMA system, where A stands for anion-exchange membrane (AEM), P represents the proton-exchange membrane (PEM), and MA indicates the resulting membrane assembly.
When an alkali-metal-free cell stack containing the APMA and a copper electrocatalyst was constructed, it produced ethylene with a high specificity of 50%. It was also able to operate for over 1,000 hours at an industrial-level current of 10A — a very significant increase in lifespan over existing systems, meaning the system can be easily expanded to an industrial scale.
Further tests showed that the formation of carbonates and salts was suppressed, while there was no loss of CO2 or electrolyte. This is crucial, as previous cells using bipolar membranes instead of APMA suffered from electrolyte loss due to the diffusion of alkali-metal ions from the anolyte. The formation of hydrogen in competition with ethylene, another problem affecting earlier systems that used acidic cathode environments, was also minimised.
Another key feature of the process is the specialised electrocatalyst. Copper is used to catalyse a wide range of reactions across the chemical industry. However, the specific catalyst used by the research team took advantage of some distinctive features. The millions of nano-scale copper spheres had richly textured surfaces, with steps, stacking faults and grain boundaries. These “defects” — relative to an ideal metal structure — provided a favourable environment for the reaction to proceed.
Prof. Lau said, “We will work on further improvements to enhance the product selectivity and seek for collaboration opportunities with the industry. It is clear that this APMA cell design underpins a transition to green production of ethylene and other valuable chemicals and can contribute to reducing carbon emissions and achieving the goal of carbon neutrality.”
This innovative PolyU project was a collaboration with researchers from the University of Oxford, the National Synchrotron Radiation Research Centre of Taiwan and Jiangsu University. More

Researchers have developed a robotic sensor that incorporates artificial intelligence techniques to read braille at speeds roughly double that of most human readers.
The research team, from the University of Cambridge, used machine learning algorithms to teach a robotic sensor to quickly slide over lines of braille text. The robot was able to read the braille at 315 words per minute at close to 90% accuracy.
Although the robot braille reader was not developed as an assistive technology, the researchers say the high sensitivity required to read braille makes it an ideal test in the development of robot hands or prosthetics with comparable sensitivity to human fingertips. The results are reported in the journal IEEE Robotics and Automation Letters.
Human fingertips are remarkably sensitive and help us gather information about the world around us. Our fingertips can detect tiny changes in the texture of a material or help us know how much force to use when grasping an object: for example, picking up an egg without breaking it or a bowling ball without dropping it.
Reproducing that level of sensitivity in a robotic hand, in an energy-efficient way, is a big engineering challenge. In Professor Fumiya Iida’s lab in Cambridge’s Department of Engineering, researchers are developing solutions to this and other skills that humans find easy, but robots find difficult.
“The softness of human fingertips is one of the reasons we’re able to grip things with the right amount of pressure,” said Parth Potdar from Cambridge’s Department of Engineering and an undergraduate at Pembroke College, the paper’s first author. “For robotics, softness is a useful characteristic, but you also need lots of sensor information, and it’s tricky to have both at once, especially when dealing with flexible or deformable surfaces.”
Braille is an ideal test for a robot ‘fingertip’ as reading it requires high sensitivity, since the dots in each representative letter pattern are so close together. The researchers used an off-the-shelf sensor to develop a robotic braille reader that more accurately replicates human reading behaviour.

“There are existing robotic braille readers, but they only read one letter at a time, which is not how humans read,” said co-author David Hardman, also from the Department of Engineering. “Existing robotic braille readers work in a static way: they touch one letter pattern, read it, pull up from the surface, move over, lower onto the next letter pattern, and so on. We want something that’s more realistic and far more efficient.”
The robotic sensor the researchers used has a camera in its ‘fingertip’, and reads by using a combination of the information from the camera and the sensors. “This is a hard problem for roboticists as there’s a lot of image processing that needs to be done to remove motion blur, which is time and energy-consuming,” said Potdar.
The team developed machine learning algorithms so the robotic reader would be able to ‘deblur’ the images before the sensor attempted to recognise the letters. They trained the algorithm on a set of sharp images of braille with fake blur applied. After the algorithm had learned to deblur the letters, they used a computer vision model to detect and classify each character.
Once the algorithms were incorporated, the researchers tested their reader by sliding it quickly along rows of braille characters. The robotic braille reader could read at 315 words per minute at 87% accuracy, which is twice as fast and about as accurate as a human Braille reader.
“Considering that we used fake blur the train the algorithm, it was surprising how accurate it was at reading braille,” said Hardman. “We found a nice trade-off between speed and accuracy, which is also the case with human readers.”
“Braille reading speed is a great way to measure the dynamic performance of tactile sensing systems, so our findings could be applicable beyond braille, for applications like detecting surface textures or slippage in robotic manipulation,” said Potdar.
In future, the researchers are hoping to scale the technology to the size of a humanoid hand or skin. The research was supported in part by the Samsung Global Research Outreach Program. More

For decades scientists have been trying to solve Feynman’s Sprinkler Problem: How does a sprinkler running in reverse — in which the water flows into the device rather than out of it — work? Through a series of experiments, a team of mathematicians has figured out how flowing fluids exert forces and move structures, thereby revealing the answer to this long-standing mystery.
“Our study solves the problem by combining precision lab experiments with mathematical modeling that explains how a reverse sprinkler operates,” explains Leif Ristroph, an associate professor at New York University’s Courant Institute of Mathematical Sciences and the senior author of the paper, appears in the journal Physical Review Letters. “We found that the reverse sprinkler spins in the ‘reverse’ or opposite direction when taking in water as it does when ejecting it, and the cause is subtle and surprising.”
“The regular or ‘forward’ sprinkler is similar to a rocket, since it propels itself by shooting out jets,” adds Ristroph. “But the reverse sprinkler is mysterious since the water being sucked in doesn’t look at all like jets. We discovered that the secret is hidden inside the sprinkler, where there are indeed jets that explain the observed motions.”
The research answers one of the oldest and most difficult problems in the physics of fluids. And while Ristroph recognizes there is modest utility in understanding the workings of a reverse sprinkler — “There is no need to ‘unwater’ lawns,” he says — the findings teach us about the underlying physics and whether we can improve the methods needed to engineer devices that use flowing fluids to control motions and forces.
“We now have a much better understanding about situations in which fluid flow through structures can induce motion,” notes Brennan Sprinkle, an assistant professor at Colorado School of Mines and one of the paper’s co-authors. “We think these methods we used in our experiments will be useful for many practical applications involving devices that respond to flowing air or water.”
The Feynman sprinkler problem is typically framed as a thought experiment about a type of lawn sprinkler that spins when fluid, such as water, is expelled out of its S-shaped tubes or “arms.” The question asks what happens if fluid is sucked in through the arms: Does the device rotate, in what direction, and why?
The problem is associated with pioneers in physics, from Ernst Mach, who posed the problem in the 1880s, to the Nobel laureate Richard Feynman, who worked on and popularized it from the 1960s through 1980s. It has since spawned numerous studies that debate the outcome and the underlying physics — and to this day it is presented as an open problem in physics and in fluid mechanics textbooks.

In setting out to solve the reverse sprinkler problem, Ristroph, Sprinkle, and their co-authors, Kaizhe Wang, an NYU doctoral student at the time of the study, and Mingxuan Zuo, an NYU graduate student, custom manufactured sprinkler devices and immersed them in water in an apparatus that pushes in or pulls out water at controllable rates. To let the device spin freely in response to the flow, the researchers built a new type of ultra-low-friction rotary bearing. They also designed the sprinkler in a way that enabled them to observe and measure how the water flows outside, inside, and through it.
“This has never been done before and was key to solving the problem,” Ristroph explains.
To better observe the reverse sprinkler process, the researchers added dyes and microparticles in the water, illuminated with lasers, and captured the flows using high-speed cameras.
The results showed that a reverse sprinkler rotates much more slowly than does a conventional one — about 50 times slower — but the mechanisms are fundamentally similar. A conventional forward sprinkler acts like a rotating version of a rocket powered by water jetting out of the arms. A reverse sprinkler acts as an “inside-out rocket,” with its jets shooting inside the chamber where the arms meet. The researchers found that the two internal jets collide but they do not meet exactly head on, and their math model showed how this subtle effect produces forces that rotate the sprinkler in reverse.
The team sees the breakthrough as potentially beneficial to harnessing climate-friendly energy sources.
“There are ample and sustainable sources of energy flowing around us — wind in our atmosphere as well as waves and currents in our oceans and rivers,” says Ristroph. “Figuring out how to harvest this energy is a major challenge and will require us to better understand the physics of fluids.”
The work was supported by a grant from the National Science Foundation (DMS-1646339). More

Artificial intelligence using neural networks performs calculations digitally with the help of microelectronic chips. Physicists at Leipzig University have now created a type of neural network that works not with electricity but with so-called active colloidal particles. In their publication in the journal Nature Communications, the researchers describe how these microparticles can be used as a physical system for artificial intelligence and the prediction of time series.
“Our neural network belongs to the field of physical reservoir computing, which uses the dynamics of physical processes, such as water surfaces, bacteria or octopus tentacle models, to make calculations,” says Professor Frank Cichos, whose research group developed the network with the support of ScaDS.AI. As one of five new AI centres in Germany, since 2019 the research centre with sites in Leipzig and Dresden has been funded as part of the German government’s AI Strategy and supported by the Federal Ministry of Education and Research and the Free State of Saxony.
“In our realization, we use synthetic self-propelled particles that are only a few micrometres in size,” explains Cichos. “We show that these can be used for calculations and at the same time present a method that suppresses the influence of disruptive effects, such as noise, in the movement of the colloidal particles.” Colloidal particles are particles that are finely dispersed in their dispersion medium (solid, gas or liquid).
For their experiments, the physicists developed tiny units made of plastic and gold nanoparticles, in which one particle rotates around another, driven by a laser. These units have certain physical properties that make them interesting for reservoir computing. “Each of these units can process information, and many units make up the so-called reservoir. We change the rotational motion of the particles in the reservoir using an input signal. The resulting rotation contains the outcome of a calculation,” explains Dr Xiangzun Wang. “Like many neural networks, the system needs to be trained to perform a particular calculation.”
The researchers were particularly interested in noise. “Because our system contains extremely small particles in water, the reservoir is subject to strong noise, similar to the noise that all molecules in a brain are subject to,” says Professor Cichos. “This noise, Brownian motion, severely disrupts the functioning of the reservoir computer and usually requires a very large reservoir to remedy. In our work, we have found that using past states of the reservoir can improve computer performance, allowing smaller reservoirs to be used for certain computations under noisy conditions.”
Cichos adds that this has not only contributed to the field of information processing with active matter, but has also yielded a method that can optimise reservoir computation by reducing noise. More