Computers Math

A new joint study uncovers the remarkable potential of ultrafast lasers that could provide innovative solutions in 2D materials processing for many technology developers such as high-speed photodetectors, flexible electronics, biohybrids, and next-generation solar cells.
The manipulation of 2D materials, such as graphene and transition metal dichalcogenides (TMDs), is crucial for the advancement of next-generation electronic, photonic, quantum, and sensor technologies. These materials exhibit unique properties, including high electrical conductivity, mechanical flexibility, and tunable optical characteristics. Traditional processing methods, however, often lack the necessary precision and can introduce thermal damage. This is where ultrafast laser processing comes into play, offering unprecedented control over the material properties at the nanoscale.
Ultrafast lasers for modifying materials
Recent advancements in the field of light-matter interactions have paved the way for the transformative use of ultrafast laser processing in 2D materials. A new study by Aleksei Emelianov, Mika Pettersson from the University of Jyväskylä (Finland), and Ivan Bobrinetskiy from Biosense Institute (Serbia) explores the remarkable potential of ultrafast laser techniques in manipulating 2D layered materials and van der Waals (vdW) heterostructures toward novel applications.
“Ultrafast laser processing has emerged as a versatile technique for modifying materials and introducing novel functionalities. Unlike continuous-wave and long-pulsed optical methods, ultrafast lasers offer a solution for thermal heating issues. The nonlinear interactions between ultrafast laser pulses and the atomic lattice of 2D materials substantially influence their chemical and physical properties,” tells Postdoctoral Researcher Aleksei Emelianov from University of Jyväskylä.
A new tool for manipulating the properties of 2D materials
The researchers describe progress made over the past decade and primarily focus on the transformative role of ultrafast laser pulses in maskless green technology, enabling subtractive and additive processes that unveil ways for advanced devices. Utilizing the synergetic effect between the energy states within the atomic layers and ultrafast laser irradiation, it is feasible to achieve resolution down to several nanometers.

“Ultrafast light-matter interactions are being actively probed to study the unique optical properties of low-dimensional materials, says Aleksei Emelianov. In our research, we discovered that ultrafast laser processing has the potential to become a new technological tool for manipulating the properties of 2D materials,” he continues.
Reliable tools for advanced materials processing
Key advancements are discussed in functionalization, doping, atomic reconstruction, phase transformation, and 2D and 3D micro- and nanopatterning. The ability to manipulate 2D materials at such a fine scale opens up numerous possibilities for the development of novel photonic, electronic, and sensor applications. Potential applications include high-speed photodetectors, flexible electronics, biohybrids, and next-generation solar cells. The precision of ultrafast laser processing enables the creation of intricate micro- and nanoscale structures with potential utilization in telecommunications, medical diagnostics, and environmental monitoring.
“It is surprising how versatile ultrafast lasers are in tuning and modifying 2D materials. It is highly likely that lasers could provide innovative solutions in 2D materials processing for many technology developers,” adds Mika Pettersson.
This review represents a significant step forward in realizing the full potential of 2D and vdW materials, promising to drive new advancements in technology and industry.
“Still, there is a need for research on the physical basics of ultrafast interactions between lasers and 2D materials, says Ivan Bobrinetskiy. These should include not only interactions between the 2D material lattice and light but also involve the environment and substrates, which makes the physics of these processes more complicated but exciting at the same time,” he continues. More

A discovery by researchers at the University of British Columbia promises to improve care for patients with endometrial cancer, the most common gynecologic malignancy.
Using artificial intelligence (AI) to spot patterns across thousands of cancer cell images, the researchers have pinpointed a distinct subset of endometrial cancer that puts patients at much greater risk of recurrence and death, but would otherwise go unrecognized by traditional pathology and molecular diagnostics.
The findings, published today in Nature Communications, will help doctors identify patients with high-risk disease who could benefit from more comprehensive treatment.
“Endometrial cancer is a diverse disease, with some patients much more likely to see their cancer return than others,” said Dr. Jessica McAlpine, professor and Dr. Chew Wei Chair in Gynaecologic Oncology at UBC, and surgeon-scientist at BC Cancer and Vancouver General Hospital. “It’s so important that patients with high-risk disease are identified so we can intervene and hopefully prevent recurrence. This AI-based approach will help ensure no patient misses an opportunity for potentially lifesaving interventions.”
AI-powered precision medicine
The discovery builds on work by Dr. McAlpine and colleagues at B.C.’s Gynecologic Cancer Initiative — a multi-institutional collaboration between UBC, BC Cancer, Vancouver Coastal Health and BC Women’s Hospital — who in 2013 helped show that endometrial cancer can be classified into four subtypes based on the molecular characteristics of cancerous cells, with each posing a different level of risk to patients.
Dr. McAlpine and team then went on to develop an innovative molecular diagnostic tool, called ProMiSE, that can accurately discern between the subtypes. The tool is now used across B.C., parts of Canada and internationally to guide treatment decisions.

Yet, challenges remain. The most prevalent molecular subtype, encompassing approximately 50 per cent of all cases, is largely a catch-all category for endometrial cancers lacking discernable molecular features.
“There are patients in this very large category who have extremely good outcomes, and others whose cancer outcomes are highly unfavourable. But until now, we have lacked the tools to identify those at-risk so that we can offer them appropriate treatment,” said Dr. McAlpine.
Dr. McAlpine turned to long-time collaborator and machine learning expert Dr. Ali Bashashati, an assistant professor of biomedical engineering and pathology and laboratory medicine at UBC, to try and further segment the category using advanced AI methods.
Dr. Bashashati and his team developed a deep learning AI model that analyzes images of tissue samples collected from patients. The AI was trained to differentiate between different subtypes, and after analyzing over 2,300 cancer tissue images, pinpointed the new subgroup that exhibited markedly inferior survival rates.
“The power of AI is that it can objectively look at large sets of images and identify patterns that elude human pathologists,” said Dr. Bashashati. “It’s finding the needle in the haystack. It tells us this group of cancers with these characteristics are the worst offenders and represent a higher risk for patients.”
Bringing the discovery to patients
The team is now exploring how the AI tool could be integrated into clinical practice alongside traditional molecular and pathology diagnostics, thanks to a grant from the Terry Fox Research Institute.

“The two work hand-in-hand, with AI providing an additional layer on top of the testing we’re already doing,” said Dr. McAlpine.
One benefit of the AI-based approach is that it’s cost-efficient and easy to deploy across geographies. The AI analyzes images that are routinely gathered by pathologists and healthcare providers, even at smaller hospital sites in rural and remote communities, and shared when seeking second opinions on a diagnosis.
The combined use of molecular and AI-based analysis could allow many patients to remain in their home communities for less intensive surgery, while ensuring those who need treatment at a larger cancer centre can do so.
“What is really compelling to us is the opportunity for greater equity and access,” said Dr. Bashashati. “The AI doesn’t care if you’re in a large urban centre or rural community, it would just be available, so our hope is that this could really transform how we diagnose and treat endometrial cancer for patients everywhere.” More

Balance can be impacted by various factors, including diseases such as Parkinson’s disease, acute and chronic injuries to the nervous system, and the natural aging process. Accurately assessing balance in patients is important to identify and manage conditions that affect coordination and stability. Balance assessments also play a key role in preventing falls, understanding movement disorders, and designing appropriate therapeutic interventions across age groups and medical conditions.
However, traditional methods used to assess balance often suffer from subjectivity, are not comprehensive enough and cannot be administered remotely. Moreover, these assessments rely on expensive, specialized equipment which may not be readily accessible in all clinical settings and depend on the clinician’s expertise, which can lead to variability in results. More objective and comprehensive assessment tools in balance evaluation are greatly needed.
Using wearable sensors and advanced machine learning algorithms, researchers from Florida Atlantic University’s College of Engineering and Computer Science have developed a novel approach that addresses a crucial gap in balance assessment and sets a new benchmark in the application of wearable technology and machine learning in health care. The approach is a significant advance in objective balance assessment, especially for remote monitoring in home-based or nursing care settings, potentially transforming balance disorder management.
For the study, researchers used the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB), widely used in health care to assess a person’s ability to maintain balance under different sensory conditions. Wearable sensors were placed on study participants’ ankle, lumbar (lower back), sternum, wrist and arm.
Researchers collected comprehensive motion data from the participants under four different sensory conditions of m-CTSIB: balance performance with eyes open and closed on a stable surface; and eyes open and closed on a foam surface. Each test condition lasted about 11 seconds without breaks to simulate continuous balance challenges and streamline the assessment process. Researchers used inertial measurement unit (IMU) sensors coupled with a specialized system to evaluate ground truth m-CTSIB balance scores for their analysis.
The data was then preprocessed and an extensive array of features was extracted for analysis. To estimate the m-CTSIB scores, researchers applied Multiple Linear Regression, Support Vector Regression and XGBOOST algorithms. The wearable sensor data served as the input for their machine-learning models, and the corresponding m-CTSIB scores from Falltrak II, one of the leading tools in fall prevention, acted as the ground truth labels for model training and validation. Multiple machine-learning models were then developed to estimate m-CTSIB scores from the wearable sensor data. Researchers also explored the most effective sensor placements to optimize balance analysis.
Results of the study, published in the journal Frontiers in Digital Health, underscore this approach’s high accuracy and strong correlation with ground truth balance scores, suggesting the method is effective and reliable in estimating balance. Data from lumbar and dominant ankle sensors demonstrated the highest performance in balance score estimation, highlighting the importance of strategic sensor placement for capturing relevant balance adjustments and movements.

“Wearable sensors offer a practical and cost-effective solution for capturing detailed movement data, which is essential for balance analysis,” said Behnaz Ghoraani, Ph.D., senior author, an associate professor, FAU Department of Electrical Engineering and Computer Science, co-director of the FAU Center for SMART Health, and a fellow, FAU Institute for Sensing and Embedded Network Systems Engineering (I-SENSE). “Positioned on areas like the lower back and lower limbs, these sensors provide insights into 3D movement dynamics, essential for applications such as fall risk assessment in diverse populations. Coupled with the evolution of machine learning, these sensor-derived datasets transform into objective, quantifiable balance metrics, using an array of machine learning techniques.”
Results provide important insights into the significance of specific movements, feature selection and sensor placement in estimating balance. Notably, the XGBOOST model, utilizing the lumbar sensor data, achieved outstanding results in both cross-validation methods and demonstrated a high correlation and a low mean absolute error, indicating consistent performance.
“Findings from this important research suggest that this novel method has the potential to revolutionize balance assessment practices, especially in situations where traditional methods are impractical or inaccessible,” said Stella Batalama, Ph.D., dean, FAU College of Engineering and Computer Science. “This approach is more accessible, cost-effective and capable of remote administration, which could have significant implications for health care, rehabilitation, sports science or other fields where balance assessment is important.”
The objectives of this study emerged from recognizing the need for advanced tools to capture the nuanced effects of different sensory inputs on balance.
“Traditional balance assessments often lack the granularity to dissect these influences comprehensively, leading to a gap in our understanding and management of balance impairments,” said Ghoraani. “Moreover, wearables support remote monitoring, enabling health care professionals to evaluate patients’ balance remotely, which is particularly useful in diverse health care scenarios.” More

An Oregon State University doctoral student and researchers at Adobe have created a new, cost-effective training technique for artificial intelligence systems that aims to make them less socially biased.
Eric Slyman of the OSU College of Engineering and the Adobe researchers call the novel method FairDeDup, an abbreviation for fair deduplication. Deduplication means removing redundant information from the data used to train AI systems, which lowers the high computing costs of the training.
Datasets gleaned from the internet often contain biases present in society, the researchers said. When those biases are codified in trained AI models, they can serve to perpetuate unfair ideas and behavior.
By understanding how deduplication affects bias prevalence, it’s possible to mitigate negative effects — such as an AI system automatically serving up only photos of white men if asked to show a picture of a CEO, doctor, etc. when the intended use case is to show diverse representations of people.
“We named it FairDeDup as a play on words for an earlier cost-effective method, SemDeDup, which we improved upon by incorporating fairness considerations,” Slyman said. “While prior work has shown that removing this redundant data can enable accurate AI training with fewer resources, we find that this process can also exacerbate the harmful social biases AI often learns.”
Slyman presented the FairDeDup algorithm last week in Seattle at the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
FairDeDup works by thinning the datasets of image captions collected from the web through a process known as pruning. Pruning refers to choosing a subset of the data that’s representative of the whole dataset, and if done in a content-aware manner, pruning allows for informed decisions about which parts of the data stay and which go.

“FairDeDup removes redundant data while incorporating controllable, human-defined dimensions of diversity to mitigate biases,” Slyman said. “Our approach enables AI training that is not only cost-effective and accurate but also more fair.”
In addition to occupation, race and gender, other biases perpetuated during training can include those related to age, geography and culture.
“By addressing biases during dataset pruning, we can create AI systems that are more socially just,” Slyman said. “Our work doesn’t force AI into following our own prescribed notion of fairness but rather creates a pathway to nudge AI to act fairly when contextualized within some settings and user bases in which it’s deployed. We let people define what is fair in their setting instead of the internet or other large-scale datasets deciding that.”
Collaborating with Slyman were Stefan Lee, an assistant professor in the OSU College of Engineering, and Scott Cohen and Kushal Kafle of Adobe. More

In a study published in Nature Communications June 19, a team of scientists led by Rice University’s Qimiao Si predicts the existence of flat electronic bands at the Fermi level, a finding that could enable new forms of quantum computing and electronic devices.
Quantum materials are governed by the rules of quantum mechanics, where electrons occupy unique energy states. These states form a ladder with the highest rung called the Fermi energy.
Electrons, being charged, repel each other and move in correlated ways. Si’s team found that electron interactions can create new flat bands at the Fermi level, enhancing their importance.
“Most flat bands are located far from the Fermi energy, which limits their impact on the material’s properties,” said Si, the Harry C. and Olga K. Wiess Professor of Physics and Astronomy at Rice.
Typically, a particle’s energy changes with its momentum. But in quantum mechanics, electrons can exhibit quantum interference, where their energy remains flat even when their momentum changes. These are known as flat bands.
“Flat electronic bands can enhance electron interactions, potentially creating new quantum phases and unusual low-energy behaviors,” Si said.
These bands are especially sought after in transition metal ions called d-electron materials with specific crystal lattices, where they often show unique properties, Si said.

The team’s findings suggest new ways to design these, which could inspire new applications for these materials in quantum bits, qubits and spintronics. Their research shows that electron interactions can link immobile and mobile electron states.
Using a theoretical model, the researchers demonstrated that these interactions can create a new type of Kondo effect, where immobile particles gain mobility by interacting with mobile electrons at the Fermi energy. The Kondo effect describes the scattering of conduction electrons in a metal due to magnetic impurities, resulting in a characteristic change in electrical resistivity with temperature.
“Quantum interference can enable the Kondo effect, allowing us to make significant progress,” said Lei Chen, a Ph.D. student at Rice.
A key attribute of the flat bands is their topology, Chen said. “The flat bands pinned to the Fermi energy provide a means to realize new quantum states of matter,” he said.
The team’s research reveals that this includes anyons and Weyl fermions, or massless quasiparticles and fermions that carry an electric charge. The researchers found that anyons are promising agents for qubits, and materials that host Weyl fermions may find applications in spin-based electronics.
The study also highlights the potential for these materials to be very responsive to external signals and capable of advanced quantum control. The results indicate that the flat bands could lead to strongly correlated topological semimetals at relatively low temperatures potentially operating at high temperatures or even room temperature.
“Our work provides the theoretical foundation for utilizing flat bands in strongly interacting settings to design and control novel quantum materials that operate beyond the realm of low temperatures,” Si said.
Contributors to this research include Fang Xie and Shouvik Sur, Rice postdoctoral associates of physics and astronomy; Haoyu Hu, Rice alumnus and postdoctoral fellow at Donostia International Physics Center; Silke Paschen, physicist at the Vienna University of Technology; and Jennifer Cano, theoretical physicist at Stony Brook University and the Flatiron Institute. More

Computers have come so far in terms of their power and potential, rivaling and even eclipsing human brains in their ability to store and crunch data, make predictions and communicate. But there is one domain where human brains continue to dominate: energy efficiency.
“The most efficient computers are still approximately four orders of magnitude — that’s 10,000 times — higher in energy requirements compared to the human brain for specific tasks such as image processing and recognition, although they outperform the brain in tasks like mathematical calculations,” said UC Santa Barbara electrical and computer engineering Professor Kaustav Banerjee, a world expert in the realm of nanoelectronics. “Making computers more energy efficient is crucial because the worldwide energy consumption by on-chip electronics stands at #4 in the global rankings of nation-wise energy consumption, and it is increasing exponentially each year, fueled by applications such as artificial intelligence.” Additionally, he said, the problem of energy inefficient computing is particularly pressing in the context of global warming, “highlighting the urgent need to develop more energy-efficient computing technologies.”
Neuromorphic (NM) computing has emerged as a promising way to bridge the energy efficiency gap. By mimicking the structure and operations of the human brain, where processing occurs in parallel across an array of low power-consuming neurons, it may be possible to approach brain-like energy efficiency. In a paper published in thejournal Nature Communications, Banerjee and co-workers Arnab Pal, Zichun Chai, Junkai Jiang and Wei Cao, in collaboration with researchers Vivek De and Mike Davies from Intel Labs propose such an ultra-energy efficient platform, using 2D transition metal dichalcogenide (TMD)-based tunnel-field-effect transistors (TFETs). Their platform, the researchers say, can bring the energy requirements to within two orders of magnitude (about 100 times) with respect to the human brain.
Leakage currents and subthreshold swing
The concept of neuromorphic computing has been around for decades, though the research around it has intensified only relatively recently. Advances in circuitry that enable smaller, denser arrays of transistors, and therefore more processing and functionality for less power consumption are just scratching the surface of what can be done to enable brain-inspired computing. Add to that an appetite generated by its many potential applications, such as AI and the Internet-of-Things, and it’s clear that expanding the options for a hardware platform for neuromorphic computing must be addressed in order to move forward.
Enter the team’s 2D tunnel-transistors. Emerging out of Banerjee’s longstandingresearch efforts to develop high-performance, low-power consumption transistors to meet the growing hunger for processing without a matching increase in power requirement, these atomically thin, nanoscale transistors are responsive at low voltages, and as the foundation of the researchers’ NM platform, can mimic the highly energy efficient operations of the human brain. In addition to lower off-state currents, the 2D TFETs also have a low subthreshold swing (SS), a parameter that describes how effectively a transistor can switch from off to on. According to Banerjee, a lower SS means a lower operating voltage, and faster and more efficient switching.
“Neuromorphic computing architectures are designed to operate with very sparse firing circuits,” said lead author Arnab Pal, “meaning they mimic how neurons in the brain fire only when necessary.” In contrast to the more conventional von Neumann architecture of today’s computers, in which data is processed sequentially, memory and processing components are separated and which continuously draw power throughout the entire operation, an event-driven system such as a NM computer fires up only when there is input to process, and memory and processing are distributed across an array of transistors. Companies like Intel and IBM have developed brain-inspired platforms, deploying billions of interconnected transistors and generating significant energy savings.

However, there’s still room for energy efficiency improvement, according to the researchers.
“In these systems, most of the energy is lost through leakage currents when the transistors are off, rather than during their active state,” Banerjee explained. A ubiquitous phenomenon in the world of electronics, leakage currents are small amounts of electricity that flow through a circuit even when it is in the off state (but still connected to power). According to the paper, current NM chips use traditional metal-oxide-semiconductor field-effect transistors (MOSFETs) which have a high on-state current, but also high off-state leakage. “Since the power efficiency of these chips is constrained by the off-state leakage, our approach — using tunneling transistors with much lower off-state current — can greatly improve power efficiency,” Banerjee said.
When integrated into a neuromorphic circuit, which emulates the firing and reset of neurons, the TFETs proved themselves more energy efficient than state-of-the-art MOSFETs, particularly the FinFETs (a MOSFET design that incorporates vertical “fins” as a way to provide better control of switching and leakage). TFETs are still in the experimental stage, however the performance and energy efficiency of neuromorphic circuits based on them makes them a promising candidate for the next generation of brain-inspired computing.
According to co-authors Vivek De (Intel Fellow) and Mike Davies (Director of Intel’s Neuromorphic Computing Lab), “Once realized, this platform can bring the energy consumption in chips to within two orders of magnitude with respect to the human brain — not accounting for the interface circuitry and memory storage elements. This represents a significant improvement from what is achievable today.”
Eventually, one can realize three-dimensional versions of these 2D-TFET based neuromorphic circuits to provide even closer emulation of the human brain, added Banerjee, widely recognized as one of the key visionaries behind 3D integrated circuits that are now witnessing wide scale commercial proliferation. More

Researchers have found a way to bind engineered skin tissue to the complex forms of humanoid robots. This brings with it potential benefits to robotic platforms such as increased mobility, self-healing abilities, embedded sensing capabilities and an increasingly lifelike appearance. Taking inspiration from human skin ligaments, the team, led by Professor Shoji Takeuchi of the University of Tokyo, included special perforations in a robot face, which helped a layer of skin take hold. Their research could be useful in the cosmetics industry and to help train plastic surgeons.
Takeuchi is a pioneer in the field of biohybrid robotics, where biology and mechanical engineering meet. So far, his lab, the Biohybrid Systems Laboratory, has created mini robots that walk using biological muscle tissue, 3D printed lab-grown meat, engineered skin that can heal, and more. It was during research on the last of these items that Takeuchi felt the need to take the idea of robotic skin further to improve its properties and capabilities.
“During previous research on a finger-shaped robot covered in engineered skin tissue we grew in our lab, I felt the need for better adhesion between the robotic features and the subcutaneous structure of the skin,” said Takeuchi. “By mimicking human skin-ligament structures and by using specially made V-shaped perforations in solid materials, we found a way to bind skin to complex structures. The natural flexibility of the skin and the strong method of adhesion mean the skin can move with the mechanical components of the robot without tearing or peeling away.”
Previous methods to attach skin tissue to solid surfaces involved things like mini anchors or hooks, but these limited the kinds of surfaces that could receive skin coatings and could cause damage during motion. By carefully engineering small perforations instead, essentially any shape of surface can have skin applied to it. The trick the team employed was to use a special collagen gel for adhesion, which is naturally viscous so difficult to feed into the minuscule perforations. But using a common technique for plastic adhesion called plasma treatment, they managed to coax the collagen into the fine structures of the perforations while also holding the skin close to the surface in question.
“Manipulating soft, wet biological tissues during the development process is much harder than people outside the field might think. For instance, if sterility is not maintained, bacteria can enter and the tissue will die,” said Takeuchi. “However, now that we can do this, living skin can bring a range of new abilities to robots. Self-healing is a big deal — some chemical-based materials can be made to heal themselves, but they require triggers such as heat, pressure or other signals, and they also do not proliferate like cells. Biological skin repairs minor lacerations as ours does, and nerves and other skin organs can be added for use in sensing and so on.”
This research was not just made to prove a point, though. Takeuchi and his lab have a goal in mind for this application that could help in several areas of medical research. The idea of an organ-on-a-chip is not especially new, and finds use in things like drug development, but something like a face-on-a-chip could be useful in research into skin aging, cosmetics, surgical procedures, plastic surgery and more. Also, if sensors can be embedded, robots may be endowed with better environmental awareness and improved interactive capabilities.
“In this study, we managed to replicate human appearance to some extent by creating a face with the same surface material and structure as humans,” said Takeuchi. “Additionally, through this research, we identified new challenges, such as the necessity for surface wrinkles and a thicker epidermis to achieve a more humanlike appearance. We believe that creating a thicker and more realistic skin can be achieved by incorporating sweat glands, sebaceous glands, pores, blood vessels, fat and nerves. Of course, movement is also a crucial factor, not just the material, so another important challenge is creating humanlike expressions by integrating sophisticated actuators, or muscles, inside the robot. Creating robots that can heal themselves, sense their environment more accurately and perform tasks with humanlike dexterity is incredibly motivating.” More

Every so often, a food product is recalled because of some sort of contamination. For consumers of such products, a recall can trigger doubt in the safety and reliability of what they eat and drink. In many cases, a recall will come too late to keep some people from getting ill.
In spite of the food industry’s efforts to fight pathogens, products are still contaminated and people still get sick. Much of the problem stems from the tools available to screen for harmful pathogens, which are often not effective enough at protecting the public.
In AIP Advances, by AIP Publishing, researchers from Guangdong University of Technology and Pudong New District People’s Hospital developed a new method for detecting foodborne pathogens that is faster, cheaper, and more effective than existing methods. The researchers hope their technique can improve screening processes and keep contaminated food out of the hands of consumers.
Even with the best detection method, finding contaminating pathogens is not an easy task.
“Detecting these pathogens is challenging, due to their diverse nature and the various environments in which they can thrive,” said author Silu Feng. “Additionally, low concentrations of pathogens in large food samples, the presence of similar non-pathogenic organisms, and the complex nature of different food types make accurate and rapid detection difficult.”
Existing detection methods do exist, such as cell culture and DNA sequencing, but are challenging to employ at large scales. Not every batch of food can be thoroughly tested, so some contaminants inevitably slip through.
“Overall, these methods face limitations such as lengthy result times, the need for specialized equipment and trained personnel, and challenges in detecting multiple pathogens simultaneously, highlighting the need for improved detection techniques,” said Feng.

The study’s authors decided to take a different approach, designing a microfluidic chip that uses light to detect multiple types of pathogens simultaneously. Their chip is created using 3D printing, making it easy to fabricate in large amounts and modify to target specific pathogens.
The chip is split into four sections, each of which is tailored to detect a specific pathogen. If that pathogen is present in the sample, it will bind to a detection surface and change its optical properties. This arrangement let the researchers detect several common bacteria, such as E. coli, salmonella, listeria, and S. aureus, quickly and at very low concentrations.
“This method can quickly and effectively detect multiple different pathogens, and the detection results are easy to interpret, significantly improving detection efficiency,” said Feng.
The team plans to continue developing their device to make it even more applicable for food screening. More