Friday, September 18, 2026

Researchers to develop smarter, smaller microsensor for potentially dangerous gas emissions

Binghamton University professor Mohammad Younis to lead NSF project that could detect problems with lithium-ion batteries

Binghamton University

Mohammad Younis and PhD students — image:
Professor Mohammad Younis works with PhD students Hasan Albatayneh, left, and Basil Alattar at his lab in the Engineering and Science Building at the Innovative Technologies Complex at Binghamton University, State University of New York.
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Credit: Binghamton University, State University of New York

When developing new technologies, engineers do their best to keep it simple. Adding layers of complication can lead to more things that could potentially go wrong.

As a researcher who develops microelectromechanical systems (MEMS), Binghamton University, State University of New York Professor Mohammad Younis fits tiny mechanical devices into spaces no bigger than a microchip, so simplicity also helps to keep it small.

Younis — a faculty member at the Thomas J. Watson College of Engineering and Applied Science’s Department of Mechanical Engineering — recently received a $335,000 grant from the National Science Foundation to develop and test an ultrasensitive gas detection MEMS device with autonomous actuation.

The intention is to install the detector alongside lithium-ion batteries to pick up the faint traces of hydrogen or carbon dioxide that can be an early indicator of thermal runaway — a rare, uncontrolled, self-heating chain reaction where a rise in temperature increases the rate of heat generation, leading to extreme temperatures, fire, or explosion.

By improving the use of lithium-ion batteries, Younis will connect the research to wider Binghamton University initiatives such as the Upstate New York Energy Storage Engine, Battery-NY, the NorthEast Center for Chemical Energy Storage, and New Energy New York.

“I don't work on just sensors. I want the sensor to be part of a complete intelligent system,” he said. “In this case, it would be a sensor, an actuator, and the ability to make a decision based on one input or two inputs — all in the same MEMS device. I'm always intrigued about this idea that I can replace a complicated system of sensors, actuators, controllers, and decision units.”

Because the device is self-contained, it doesn’t need to transmit data for processing and activation. As Younis points out, that solves two problems: no need to expend energy to send its findings somewhere else, and no concerns about possible cybersecurity risks.

“We are overwhelming the network and the cloud with too much data,” he said. “Also, although sensors are cheap, it’s not free when you transmit so much data from them, and processing the data is not free.”

The hydrogen-detecting sensor that Younis has designed features a vibrating wire and works on the principle of thermal conductivity. When the gas is present, the wire cools and becomes stiffer, lowering the rate of vibration and triggering the alarm.

“The dynamical mechanism of this sensor is much more sensitive than a static mechanism, and I was among the first to do it using dynamics,” he said. “Most researchers do thermal conductivity, with just passive electrical current. As a mechanical engineer, my passion is always on dynamics.”

In addition, Younis is teaming up with Professor Roya Maboudian, the chair of the Chemical and Biomolecular Engineering Department at the University of California – Berkeley. Maboudian researches metal-organic frameworks, a class of polymers with porous structures that can be used for gas storage. By coating the MEMS device with MOFs, any increased mass when the polymers capture carbon dioxide can also trigger an alert.

When they applied for NSF funding for this sensor project two years ago, Younis and Maboudian both could claim “one degree of separation” from Nobel Prize winners — Distinguished Professor M. Stanley Whittingham at Binghamton (a pioneer in lithium-ion batteries) and Professor Omar Yaghi at Berkeley (a key developer of MOFs). Yaghi has since moved to Tsinghua University in China to lead an artificial intelligence laboratory for accelerating the discovery of new materials.

If this MEMS technology is developed successfully, it could be adapted for a wide variety of sensor needs.

“The application is not limited to gases. It can be magnetic, pressure, acceleration, or any other stimulus. I'm a mechanical engineer, so I don't have a loyal attachment to gases,” Younis said.

Brain-inspired acoustic-optical system recognizes voice commands and drone trajectories

A self-powered triboelectric acoustic sensor and an oxide neuromorphic transistor combine sensing, memory and reservoir computing in a single multimodal platform

Tsinghua University Press

Acoustic-optical reservoir hardware integrates sound and light for neuromorphic recognition — image:
The acoustic-optical information reservoir system integrates a triboelectric acoustic sensor (TAS), a rectifier bridge and an indium-zinc-oxide photoelectronic neuromorphic transistor (IZO-PNT). Sound and light stimuli update synaptic weights, while the device maps voice commands and drone trajectory images into high-dimensional conductance states for classification.
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Credit: Si Yuan Zhou, Li Qiang Zhu* et al., Nano Research, Tsinghua University Press

As Internet of Things technologies continue to develop, human–machine interfaces are expected to perceive and process increasingly diverse environmental information. The human brain performs this task efficiently by integrating auditory and visual signals, whereas conventional artificial systems often use separate components for sensing, memory, and computation.

A research team at Ningbo University has developed an acoustic-optical information reservoir system (AOIRS) that integrates a triboelectric acoustic sensor (TAS) with an indium-zinc-oxide photoelectronic neuromorphic transistor (IZO-PNT). The system can perceive sound and light, reproduce synaptic-like memory behaviors, and process temporal information through reservoir computing.

The study, titled “Acoustic-optical information reservoir system based on triboelectric acoustic sensor tuned oxide photoelectronic neuromorphic transistor,” was published in Nano Research on June 29, 2026.

The TAS converts sound-induced mechanical vibrations into electrical signals through triboelectrification and electrostatic induction. It contains fluorinated ethylene-propylene and polyimide friction layers with copper-mesh electrodes. A three-dimensional acoustic coupling cavity was introduced to improve sound-wave collection compared with a planar device.

Under a sound wave signal of 110 dB and 200 Hz, the TAS generated an open-circuit voltage of ~20.4 V and a short-circuit current of ~3.8 μA. Its sensitivity reached ~1.1 V/dB, while the output power density reached ~30.3 mW/m² under a load resistance of 5 MΩ. The sensor maintained a stable response during a durability test of ~2000 s, and its open-circuit voltage decreased only slightly from ~20.4 V to ~19.2 V after ~170 days.

The TAS also captured voice commands with characteristic temporal and frequency information. The recorded voltage waveforms were similar to the original speech signals, while the corresponding frequency distributions remained highly similar below 2000 Hz.

After full-wave rectification, the acoustic signals were applied to the gate of the IZO-PNT. The transistor consists of an IZO channel and a chitosan-based solid-state electrolyte with a specific capacitance of ~5.1 μF/cm². Proton migration in the electrolyte and light-induced carrier processes in the IZO channel produced nonlinear conductance responses and short-term memory.

These properties allowed the device to reproduce synaptic functions, including excitatory postsynaptic current, paired-pulse facilitation, and spike-amplitude-, duration-, and number-dependent responses. The AOIRS also simulated learning–forgetting–relearning behavior. During three successive learning processes, the number of sound signals required to reach the same learning threshold decreased from 30 to 10 and then to 7, indicating that previous stimulation facilitated subsequent learning.

The device could be modulated by both sound and light. Under 20 consecutive sound wave signals of 90 dB, 200 Hz, and 0.5 s, the excitatory postsynaptic current peak increased from ~27.5 μA to ~51.9 μA as the optical power intensity increased from 0 to 57.2 mW/cm². Optical stimuli produced synaptic potentiation, while negatively rectified sound signals produced synaptic depression. The synaptic weights remained stable over repeated sound–light cycles.

The researchers then used the AOIRS as a physical reservoir. Its nonlinear dynamics and fading memory mapped temporal inputs into high-dimensional conductance states, which were classified by a multilayer perceptron.

For voice-command recognition, the team collected 270 samples covering nine commands spoken by 30 individuals. After 200 training epochs, the training and testing accuracies reached ~94.8% and ~89.8%, respectively.

The system was also tested using 1800 samples representing nine drone trajectories, including eight movement directions and a stationary state. Four successive 40 × 40 pixel images were converted into optical pulse trains and mapped into conductance states. After 200 epochs, the training and testing accuracies reached ~95.4% and ~93.6%, respectively.

The results demonstrate that sound and light information can be sensed, memorized, and processed through a shared neuromorphic hardware platform. The AOIRS shows potential for intelligent voice sensing, multimodal human–machine interaction, edge computing, and low-altitude applications such as drone trajectory recognition, formation control, and collision avoidance.

Other contributors include Wei Sheng Wang, Lin Feng Wu, Bo Bo Li, Wan Lin Zhang and Yu Fan Hu from the School of Physical Science and Technology at Ningbo University, and Wen Xiang Tao and Cong Shan Liu from the Center for Mechanics Plus Under Extreme Environments at Ningbo University.

This work was supported by the National Natural Science Foundation of China (U22A2075) and the Ningbo Key Scientific and Technological Project (2021Z116).

DOI Link:

https://doi.org/10.26599/NR.2026.94908978

About Nano Research

Nano Research is a peer-reviewed, open access, international and interdisciplinary research journal, sponsored by Tsinghua University and the Chinese Chemical Society, published by Tsinghua University Press on the platform SciOpen. It publishes original high-quality research and significant review articles on all aspects of nanoscience and nanotechnology, ranging from basic aspects of the science of nanoscale materials to practical applications of such materials. After 18 years of development, it has become one of the most influential academic journals in the nano field. Nano Research has published more than 1,000 papers every year from 2022, with its cumulative count surpassing 8,000 articles. In 2025 InCites Journal Citation Reports, its 2025 IF is 9.4 (8.3, 5 years), and it continues to be the Q1 area among the four subject classifications. Nano Research Award, established by Nano Research together with TUP and Springer Nature in 2013, and Nano Research Young Innovators (NR45) Awards, established by Nano Research in 2018, have become international academic awards with global influence.

Journal

Nano Research

DOI

10.26599/NR.2026.94908978

Article Title

Brain-inspired acoustic-optical system recognizes voice commands and drone trajectories

NSF awards $5M across three institutions to enhance data protection for users nationwide

Experts from San Diego State University, Arizona State University and University of Utah join forces to develop a new privacy-enhancing cybersecurity infrastructure

San Diego State University

Data Center image courtesy SDSU — image:
San Diego State Univerity's Joann Chen inside the data center at SDSU.
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Credit: Mandatory courtesy: San Diego State University.

Medical researchers at two different institutions may need to compare patient data to advance drug discovery. Cybersecurity professionals may want to discuss intelligence related to attacks in their networks to defend against adversaries. Privacy concerns and safeguards, as well as legal regulations, however, can prevent them from sharing and collaborating on sensitive information, including personally identifiable information or intellectual property.

With $5 million from the U.S. National Science Foundation Office of Advanced Cyberinfrastructure, researchers from San Diego State University, in partnership with Arizona State University and University of Utah, will develop a national community resource for computing and sharing private data.

Over the next five years, the team will deploy and support a novel cyberinfrastructure called Prototype Architecture for Research Advances using Privacy Enhancing Technologies (PARAPET), that could be used in a range of fields that require data analysis, including healthcare, internet security, science, finance and the social sciences, to protect sensitive information such as medical records, financial information and census statistics.

Using privacy-enhancing technologies, PARAPET is designed to be a more secure way to share and collaborate on sensitive data, safeguard data at rest and during computation, and train AI models without exposing private data.

“There are a lot of examples where privacy aspects preclude researchers from working on data together, and that's an impediment to science,” said Robert Beverly, director of SDSU’s Cybersecurity Center for Research and Education (CSCRE) and professor of computer science. “Researchers are entrusted with sensitive data, so we need to protect it. Trying to get these technologies into production promises to move the whole field forward.”

How to secure sharable data

Today, data can be encrypted for transmission and storage, but performing computations on encrypted data remains challenging. This project aims to make that process easier.

To secure shared information, researchers plan to equip PARAPET with specialized software and hardware tools that allow one user to send encrypted data that another user can work on without decrypting it, ensuring the data is never revealed to anyone but the original user. In addition, these techniques could help protect against future quantum computing attacks, which pose a heightened threat to cyber systems. PARAPET will provide an important national-scale testbed for the research community to explore these tools and techniques.

PARAPET also seeks to preventatively protect data, so that it remains inaccessible in the event that a device is compromised. In fact, the entire system will be secured by locking away encrypted data so only approved users on secured networks can access it.

“Our long-term goal is, even if our infrastructure got compromised and an attacker got a copy of encrypted data, they can't do much about it,” said Joann Chen, SDSU assistant professor of computer science.

Finally, with AI spreading into virtually every discipline, it is becoming increasingly more essential to protect data as models are built. In response, PARAPET hopes to protect users’ data while fine-tuning central AI models that can be used nationwide.

One way to do this is users can train their local model on their own device and share only the model updates with PARAPET, so it can use the information from the updates to train its larger model without accessing the raw data.

“You can see any kind of AI model as a giant math function. Basically, we take the output from that function and average all of them from different, local models,” Chen said.

Unlike systems that require organizations to transfer raw data to a central service, PARAPET will test methods that allow participating institutions to retain greater control over sensitive research data.

Beverly points out that even anonymizing data is not secure, as many hackers can conduct re-identification attacks by reverse engineering data to link it back to a person. With secure computing hardware utilizing technologies such as differential privacy, homomorphic encryption and federated learning, PARAPET seeks to provide a vastly more secure option for supporting high performance computing needs.

Manish Parashar, chief AI officer for the University of Utah and executive director of its Scientific Computing and Imaging Institute, will help manage PARAPET’s hardware and ensure the infrastructure is ready for deployment at a national scale.

“Data-driven and AI-enabled research is revolutionizing science, but progress in critical fields such as cybersecurity and public health is hampered by our inability to obtain, create, compute on, and share regulated data,” Parashar said. “PARAPET takes an important step toward solving this problem by leveraging advances in privacy-enhancing technologies. The University of Utah looks forward to contributing expertise and leadership in designing and operating the compliant, regulated environments that PARAPET needs to safely accelerate research with sensitive data.”

An expert on regulatory and policy compliance and director of Arizona State University’s Research Technology Office, Carolyn Ellis will ensure PARAPET meets the rapidly evolving requirements for technologies like this and will lead efforts to recruit users from across the nation to test it once a prototype is ready.

“Privacy-enhancing technologies have enormous potential to change how we conduct research with sensitive or regulated datasets,” said Ellis, a co-PI on the project. “We first need to understand how these new capabilities fit within institutional policies, research workflows, and the privacy expectations established through data-sharing agreements. The ASU team is well-positioned to investigate these new research data use cases.”

Through ASU’s leadership of the national Regulated Research Community of Practice, the team will bring a vital institutional perspective to the PARAPET project. This role bridges the gap between new tech capabilities and the practical realities of protecting sensitive research data.

“We’re building the infrastructure around individual privacy-enhancing technologies, bringing together specialized hardware, software, networking, security and data workflows into a prototype researchers can actually use,” said Mike Farley, SDSU chief technology research officer who oversees the on-campus data center where PARAPET’s servers will be housed. “PARAPET helps us understand what it takes to move these technologies from the lab into practical research cyberinfrastructure.”

This project will build upon SDSU’s growing body of interdisciplinary cyber initiatives and development of new cybersecurity and data privacy curricula under the CSCRE.

This project is made possible through U.S. National Science Foundation Office of Advanced Cyberinfrastructure grant #2537035.

New handheld device maps chemical composition using infrared light

Handheld scanner provides information comparable to much larger laboratory systems, could one day help surgeons assess tumors during surgery

Optica

Schematic of miniaturized system — image:
Researchers developed a handheld photothermal mid-infrared spectroscopic imaging (MIRSI) system that measures just 8 square inches. Probe light from a visible (red) diode laser and pump light from a modulated quantum cascade laser (QCL) are coupled into optical fibers and delivered to a flexible, handheld imager (blue dashed box). The beams are combined at a short-pass dichroic mirror and focused onto the sample using an off-axis parabolic mirror (OAP).
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Credit: Rohith Reddy, University of Houston

WASHINGTON — Researchers have developed a compact, handheld mid-infrared imaging spectrometer that can produce high-resolution chemical maps of a sample without using any stains or labels. With more development, the handheld device might provide a portable and easy-to-use way to map the molecular makeup of tissues and other samples.

“Ultimately, this technology could make it possible to assess tissue during cancer surgery,” said research team leader Rohith Reddy from the University of Houston. “After removing a suspected tumor, a surgeon could scan the freshly excised tissue to help determine whether it is malignant or whether cancer cells remain at the surgical margin. This complementary information would be available while the patient is still in the operating room instead of having to wait for results from laboratory testing.”

In Optica, Optica Publishing Group ’s journal for high-impact research, the researchers describe how they transformed a photothermal mid-infrared spectroscopic imaging (MIRSI) system, normally a benchtop instrument occupying more than 9 square feet, into a handheld probe measuring 8 by 8 inches. The probe holds the full optical head and connects by a flexible fiber tether to a compact base unit housing the lasers and control electronics. In side-by-side tests, it delivered image quality and chemical detail comparable to a state-of-the-art benchtop MIRSI system.

“Although the current platform is still a research prototype, it establishes a technical foundation for field-deployable, label-free chemical imaging,” said Reddy. “A handheld MIRSI device could be useful for clinical diagnosis, polymer manufacturing, pharmaceutical quality control, forensic analysis or any applications where chemical composition must be measured outside a specialized laboratory.”

Shrinking a bulky lab instrument

Photothermal mid-infrared spectroscopic imaging systems map the molecular composition of tissue or other samples, showing where different biochemical components are located. Because molecules absorb mid-infrared light at wavelengths determined by their molecular bonds, they produce characteristic spectra that can be used to distinguish proteins, lipids, nucleic acids and other components.

Unlike conventional infrared imaging, which typically uses infrared light itself to form an image, photothermal imaging detects tiny heat-induced changes caused by infrared absorption. Although this approach produces high-resolution chemical images, it typically requires a large laboratory-based instrument.

“Our initial goal was to determine whether a compact design could preserve the laboratory system's essential capabilities,” said Reddy. “The resulting platform was even closer in size and form to a clinically deployable device than we initially expected, providing a strong foundation for future clinical translation.”

Miniaturizing a photothermal MIRSI instrument is especially challenging because it requires visible and mid-infrared light to be focused onto the same point. These two wavelength ranges generally require different optical materials because materials that work well for visible light often absorb mid-infrared light, while those that work for mid-infrared light can introduce dispersion and other wavelength-dependent distortions that degrade the signal.

To create a compact and flexible MIRSI system, the researchers used chalcogenide optical fibers to deliver mid-infrared light directly from the laser, eliminating bulky free-space optics. They also replaced traditional lenses with mirrors. Because mirrors can reflect both visible and mid-infrared light, this allowed the two beams to share the same optical path without requiring a lens material that transmits both wavelengths. A final off-axis parabolic mirror was used to focus both beams onto the sample, and raster scanning was also introduced into the optical system.”

“Careful optical design and alignment helped to minimize the image distortions that mirrors can cause,” said Reddy. “We also designed the handheld scanner to connect to the light source through a fiber-optic cable, allowing it to be maneuvered easily around a sample.”

Validating the handheld system

The researchers evaluated their handheld system using biological samples, including human cervical and ovarian cancer tissues, human bone marrow biopsy tissue and mouse kidney tissue. To provide controlled tests of the system’s chemical specificity, they also characterized PMMA and polystyrene, polymers with distinct mid-infrared signatures. They then analyzed the same samples using a state-of-the-art benchtop chemical imaging system.

The researchers found that the spectra and images acquired with the handheld system exhibited comparable chemical contrast and imaging performance to the benchtop chemical imaging system, demonstrating that miniaturization successfully preserved the technology’s core capabilities.

The system resolves features as small as 2 µm, five times finer than direct infrared detection allows, and matches or exceeds current benchtop instruments on both spectral and spatial performance. Spectra of biological tissue agreed with reference FTIR measurements at a cosine similarity of 0.935, and chemical images correlated with the benchtop system at r = 0.90.

The researchers are now working to broaden the system’s mid-infrared spectral bandwidth – currently 1150 to 1400 cm−1 – to provide more complete molecular signatures and improve the system’s ability to distinguish different biochemical constituents. They also plan to increase the imaging speed, which would help reduce motion artifacts and make true freehand operation more practical for clinicians and technicians. They note that before clinical use, the system’s repeatability, safety and diagnostic performance must also be thoroughly evaluated under realistic clinical conditions.

Photothermal MIRSI images of cervical cancer (rows A and B) and ovarian cancer (rows C and D) acquired the handheld instrument (Column III) closely match the corresponding data from a benchtop system (Column IV). Column I shows H&E-stained histology images and Column II confocal scanning images of adjacent tissue sections that provide morphological contrast but lack biochemical specificity. Scale bars are 200 𝜇m.

Credit

Rohith Reddy, University of Houston

DOI

10.1364/OPTICA.600562

Method of Research

Experimental study

Subject of Research

Human tissue samples

Article Title

New handheld device maps chemical composition using infrared light

Article Publication Date

17-Sep-2026

Commentary calls for consistent evaluation of potential links between COVID-19, vaccination and cancer

“Given this evidence, however, published in highly regarded scientific journals, if we are to consider if COVID-19 is contributing to cancers, should we not also consider if COVID-19 vaccines are contributing to cancers?”

Impact Journals LLC

Are both COVID-19 and COVID-19 vaccines associated with cancer? — image:
*Oncotarget* (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science.
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Credit: Copyright © 2026 Rapamycin Press LLC dba Impact Journals Oncotarget ® is a registered trademark of Rapamycin Press LLC Impact Journals ® is a registered trademark of Rapamycin Press LLC RAPAMYCIN PRESS ® is a registered trademark of Rapamycin Press LLC

BUFFALO, NY – September 17, 2026 – A new commentary was published in Volume 17 of Oncotarget on September 11, 2026, titled “Are both COVID-19 and COVID-19 vaccines associated with cancer?.”

The paper was authored by Raphael Lataster, an independent researcher at the University of Sydney, and responds to a recent Oncotarget review by Charlotte Kuperwasser and Wafik S. El-Deiry examining published reports concerning cancer following COVID-19 vaccination or infection.

Lataster highlights research concerning potential associations between COVID-19 and cancer and discusses mechanisms proposed in the literature. He argues that similar mechanisms reported in connection with COVID-19 vaccination warrant consideration and further study.

The commentary also highlights differences in how evidence concerning COVID-19 vaccines and cancer has been evaluated in the scientific literature. He calls for the same evidentiary standards to be applied when considering potential links between COVID-19 infection, vaccination, and cancer.

“Such inquiries should proceed without preconceptions, applying the same evidentiary standards regardless of whether findings implicate infection, vaccination, or both.”

The commentary does not establish a causal relationship between COVID-19 vaccination and cancer. Rather, it calls for potential associations and biological mechanisms to be investigated without predetermined conclusions and with appropriate consideration of alternative explanations and the limitations of observational evidence.

DOI: https://doi.org/10.18632/oncotarget.28918

Correspondence to: Raphael Lataster – okaythennews@gmail.com (ORCID: https://orcid.org/0000-0002-6670-1702)

Keywords: cancer, COVID-19, COVID-19 vaccines, double standards

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