❌

Normal view

NJIT Professor’s Polymer Proposal Wins $125,000 ACS Grant

11 August 2026 at 20:19

A new research project at the New Jersey Institute of Technology is taking a closer look at a strange but important behavior in rubber-like materials: their properties can depend on what happened to them before. With a $125,000 New Directions grant from the American Chemical Society Petroleum Research Fund, chemical and materials engineering professor Gennady Gor and his students will investigate how petroleum-derived polymers absorb fluids, swell, contract and change their elasticity over time. The work could improve predictions of how elastomers perform in everything from industrial seals to oil and gas equipment.

Elastomers are polymers capable of undergoing large, reversible deformations. Silicone, neoprene and natural or synthetic rubber are familiar examples. Their molecular chains can stretch, rearrange and return toward their original configuration, giving these materials their characteristic flexibility. Yet exposure to liquids or vapors can dramatically alter that behavior. Hydrocarbon molecules may enter the polymer network, pushing molecular chains apart and causing the material to expand. At the same time, the absorbed fluid can change the polymer’s stiffness, strength and ability to recover its original shape.

Gor’s project focuses on a phenomenon known as hysteresis, in which a material’s current state depends not only on its present conditions but also on the path it took to reach them. For an elastomer, that means the material may respond differently when it is swelling than when it is shrinking, even if its size and surrounding environment are identical. The difference can arise from molecular rearrangements, delayed relaxation or changes in the interactions between polymer chains and absorbed molecules. Such path-dependent behavior is central to understanding why materials sometimes fail to return fully to their previous mechanical state.

The question is especially relevant for petroleum-related technologies. Elastomers are widely used as O-rings, gaskets and seals because they can deform to fill microscopic gaps and maintain a hermetic barrier. In oil and gas environments, however, these components may encounter complex mixtures of hydrocarbons under pressure and at elevated temperatures. If a seal swells excessively, softens or loses its ability to recover after repeated exposure, it can become unreliable. A better understanding of hysteresis could help engineers select materials and operating conditions that reduce leakage and extend component lifetimes.

The new study represents a shift for Gor’s Laboratory for Materials Interfaces. His previous research has largely examined porous adsorbent materials, which collect molecules on their internal surfaces. Elastomers are different: they do not need pores to take up substantial amounts of fluid. Instead, molecules diffuse into the polymer matrix and interact with its flexible chains. The resulting process is more like a sponge absorbing liquid, although the molecular mechanisms are governed by diffusion, thermodynamics and the elasticity of the polymer network.

GΓΌnel Nabiyeva, a fourth-year doctoral student, will lead much of the experimental effort with undergraduate chemical engineering student Elitsa Mileva and second-year doctoral student Supun Rangana. The team has already begun collecting data on how rubber deforms in the presence of liquids and vapors. For Mileva, who has spent considerable time reviewing the scientific literature, the grant will provide an opportunity to move deeper into laboratory work. Nabiyeva describes the project as a new system for the research group because the laboratory has not previously studied elastomers in this way.

The researchers will expose polymer samples to controlled concentrations of hydrocarbon vapors and track how the materials respond. Their measurements will combine swelling, fluid uptake and elasticity rather than treating these properties separately. This approach is essential because a polymer can gain mass and volume while simultaneously becoming softer or, under some conditions, displaying a more complicated mechanical response. Different hydrocarbons may produce different results because their molecular size, chemical structure and compatibility with the polymer influence how quickly they enter the material and how strongly they interact with its chains.

Rangana will contribute expertise in ultrasonic sensing, which can measure subtle changes without physically contacting or damaging the sample. Ultrasonic waves travel through a material in ways that depend on its density, structure and mechanical properties. By analyzing changes in wave transmission or reflection, scientists can detect variations in stiffness and internal condition with high precision. Coupling these measurements to vapor exposure could reveal how quickly elasticity changes as molecules enter the polymer and whether the material follows the same path during desorption as it did during absorption.

The team hopes its results will fill a gap in the fundamental understanding of polymer hysteresis. Although hysteresis is well known in many areas of physics and engineering, relatively few studies have examined how history-dependent elasticity develops in swollen elastomers. The new measurements may help distinguish reversible molecular motions from more persistent structural changes and improve models used to predict polymer performance. Ultimately, the researchers aim to make elastomer behavior less mysterious and more reliable, helping engineers design seals and flexible components that remain dependable across repeated cycles of chemical exposure, swelling and recovery.

Subject of Research: Elastomers, petroleum-derived polymers, polymer swelling, elasticity and hysteresis

Article Title: Ultrasonic Experiments Aim to Reveal Why Rubber Remembers Its History

Web References: Gennady Gor’s NJIT profile; NJIT Laboratory for Materials Interfaces

Image Credits: Courtesy of New Jersey Institute of Technology

Keywords

Elastomers, polymers, hysteresis, rubber, polymer swelling, ultrasonic sensors, materials science, chemical engineering, petroleum research, NJIT

New AI chip mimics the human brain's capacity for split-second motor control β€” it solved problems using 10,000 times fewer calculations

11 August 2026 at 19:46

Scientists have developed a new type of artificial intelligence (AI) chip that mimics the human brain's aptitude for instinctive motor responses.

Modeled after the cerebellum, the part of the brain that helps coordinate balance and fine muscle control, the chip is designed to ignore routine information and respond only to unexpected events.

In simulated tests using electrocardiogram (ECG) data, the device flagged irregular heartbeats (arrhythmias) within one-fifth of a heartbeat and with 98% accuracy, according to the researchers β€” and did so twice as fast as conventional AI systems. The team published their findings July 10 in the journal Nature Communications.

The device could pave the way for highly responsive, low-power AI systems capable of spotting and reacting to unusual events without relying on the massive computing resources of data centers β€” from always-on health monitors to self-driving cars and autonomous robots.

A new approach to neuromorphic computing

Computer architecture inspired by the human brain is known as neuromorphic computing. Many researchers consider it key to developing more advanced and efficient AI systems, because it more closely mimics how neurons fire in the human brain.

Rather than processing all incoming information with equal intensity, the brain's biological circuits prioritize important signals and filter out routine background noise, helping it conserve energy.

Most neuromorphic approaches focus on the cerebrum, the largest part of the human brain and the central "thought center." In the new study, the scientists instead focused on the cerebellum, a smaller brain region responsible for coordination and instinctive motor skills β€” things we do without much conscious thought.

Neural circuits in the cerebellum contain competing excitatory and inhibitory signals that normally balance each other out. When something unexpected happens, the balance shifts and alerts the brain that it needs to react.

This makes the cerebellum a prime, untapped candidate for neuromorphic AI systems, said study co-author Mark Hersam, a professor of materials science and engineering at Northwestern University.

"The cerebellum is excellent at ignoring the expected and reserving its resources for reacting to the unexpected," Hersam said in a statement. "That approach ultimately translates into lower energy consumption."

The brain's action planning centers are in the frontal cortex (blue), with reciprocal connections to parietal cortex (yellow) and the cerebellum (gray), among others.

A map of the human brain, including the cerebellum. (Image credit: grayjay/Shutterstock)

Merging memory and compute

While current AI is exceptionally good at recognizing patterns, it spends enormous amounts of computing power continuously analyzing streams of data.

One of the constraints is the hardware itself. Processing information involves shuttling data back and forth between separate memory and processing components, resulting in a delay known as the von Neumann bottleneck.

This new device integrates memory and processing into a single component called a memtransistor (a portmanteau of "memory" and "transistor"), enabling it to move data much more quickly and efficiently.

The memtransistor is made from an atomically thin semiconductor called molybdenum disulfide, which forms a channel between two electrodes. One electrode makes direct contact with the semiconductor, while the other sits partly above it, separated by a thin insulating layer.

This asymmetry changes how electricity flows through the device, allowing it to switch between "excitatory" and "inhibitory" modes when the direction of the voltage is reversed, the researchers explained in the study.

The "output layer" of spiking neural networks

The device is designed to form the core of the output layer of a larger spiking neural network (SSN), Hersam explained in an email to Live Science.

SNNs are a type of neural network that processes information as a series of electrical spikes, mimicking how signals pass between neurons. For the study, the researchers measured how individual memtransistors responded to repeated electrical pulses, then used the results to simulate a network comprising multiple devices that could distinguish normal ECG patterns from arrhythmias.

They then fed the same ECG data into the cerebellum-inspired memtransistor network and a standard transformer model β€” the AI architecture that underpins large language models (LLMs) β€” and compared how quickly and efficiently each detected an arrhythmia. The cerebellum-inspired system was more than twice as fast and required around 10,000 times fewer computer calculations, according to the team.

Hersam said the results should translate into faster, more energy-efficient hardware, though he added that real-world performance would depend on the speed and size of the devices.

"We have not scaled memtransistors to the level of commercial Si [silicon] chips, but in principle, 2D materials and memtransistors can be scaled to comparable sizes and operating speeds," he told Live Science.

More efficient AI at the edge

Hersam said the new device was particularly suited to scenarios where quick inference was needed using minimal power. "One example could be edge computing, or in cases where access to the cloud is not available or is not desired due to the sensitivity of the data," he added.

A huge potential benefit of the technology is that it could slash AI's reliance on data centers. According to the International Energy Agency, global data center electricity demand could reach around 945 terawatt-hours by 2030 β€” slightly more than the entire electricity consumption of Japan β€” largely driven by AI. One terawatt-hour is equal to 1 trillion watt-hours β€” enough electricity to power a 60-watt lightbulb continuously for 1.9 million years.

A hypothetical future memtransistor-based network could enable robots, autonomous vehicles and cybersecurity systems to run continuously at low power by processing data locally and reacting only when they detect an unexpected, potentially hazardous event, the researchers said.

"The current AI deployed in cybersecurity and autonomous vehicles uses the massive computing power of data centers or an in-house facility of GPUs and CPUs," Hersam told Live Science. "In contrast, our approach promises low energy/power consumption by simplifying the net number of operations needed… [This] is not an incremental performance improvement, but a fundamentally different way of detecting anomalies."

The next stage of research will focus on mimicking the cerebellum's ability to adapt to predictability β€ͺ—‬ specifically, how the human brain stops treating events as unique or unexpected when they're encountered multiple times.

See how much you know about the most complex organ in the human body with our brain quiz!

Scientists Use Ordinary Sunlight to Create Quantum Entanglement

11 August 2026 at 13:57

Scientists have shown that sunlight can be used to create quantum entanglement, a surprising advance that could eventually help make quantum technologies more energy efficient. Quantum entanglement is a strange phenomenon in which two particles become connected so strongly that their properties remain linked, even when they are separated. It is an important part of […]

The post Scientists Use Ordinary Sunlight to Create Quantum Entanglement appeared first on Knowridge Science Report.

New 3D Printing Technique Creates Materials That Stretch or Shrink When Heated

11 August 2026 at 13:55

Scientists have developed a new 3D printing technique that can create soft materials programmed to either stretch or shrink when heated. The advance could eventually lead to more capable soft robots, artificial muscles, wearable devices and medical tools that change shape inside the body. The research was led by Professor Suk-kyun Ahn of Pusan National […]

The post New 3D Printing Technique Creates Materials That Stretch or Shrink When Heated appeared first on Knowridge Science Report.

3D printing method creates soft actuators with switchable heat-driven motion

10 August 2026 at 17:00
Soft materials that change shape in response to heat are attracting growing interest in applications ranging from soft robotics to wearable technologies and biomedical devices. However, conventional extrusion-based 3D printing limits how these materials move because molecules inside each printed filament align in only one direction, restricting every filament to a single actuation mode.

❌