Recently, the distributed single-fiber multi-parameter monitoring and sensing system developed by Professor Dong Yongkang's team from the School of Aerospace Engineering at Harbin Institute of Technology has been successfully applied to the monitoring and early warning of threat events in actual long-distance oil and gas pipelines. The review committee organized by the National Pipeline Network Group unanimously concluded that this achievement meets the requirements for on-site application.

Recently, a research article titled "Design of Strictly Orthogonal Biosensors for Maximizing Renewable Biofuel Overproduction" by the team of Professor Huo Yixin from the School of Life Sciences at Beijing Institute of Technology was published in the top-tier journal "Journal of Advanced Research" (Zone 1).

In the field of sensor testing and measurement, the prerequisite for accurately capturing signals is to create an "interference-free" environment. Vibration and noise reduction equipment must not only mitigate the interference from vibrations and noise but also eliminate its own impact on the testing system through "stealth design." This "stealth feature" has become the core competitiveness of high-end testing equipment.

Lurking in our water, blood, and environment, these notorious "forever chemicals" are notoriously difficult to detect, with some being toxic to humans. Researchers from the Pritzker School of Molecular Engineering at the University of Chicago (UChicago PME) and Argonne National Laboratory in the United States collaborated to develop a novel method for detecting trace amounts of per- and polyfluoroalkyl substances (PFAS) in water. They plan to share this approach through a portable handheld device, which employs unique probes to quantify the levels of PFAS, known as "forever chemicals.". Junhong Chen, a Crown Family Professor at the University of Chicago PME and the Chief Water Strategist at Argonne National Laboratory, stated, "Existing methods for measuring these pollutant levels may take weeks, requiring state-of-the-art equipment and specialized expertise." "Our new sensor device can measure these pollutants in just minutes." This technology, published in the journal *Nature Water*, can detect PFAS at concentrations as low as 250 parts per quadrillion (ppq), akin to finding a single grain of sand in an Olympic-standard swimming pool. This capability makes the test practical for monitoring two of the most toxic perfluorinated chemicals—perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS)—in drinking water. The U.S. Environmental Protection Agency (EPA) recently proposed limiting their concentrations to 4 parts per trillion. Andrew Ferguson, a professor of PME (Pritzker School of Molecular Engineering) at the University of Chicago, said, "Detecting and eliminating PFAS is an urgent environmental and public health challenge." "Computer simulations and machine learning have proven to be highly powerful tools for understanding how these molecules bind to molecular sensors and can guide experimental work to design more sensitive and selective molecular probes." Seth Darling, a senior scientist at Argonne National Laboratory and the University of Chicago, said, "Although PFAS typically exist at extremely low concentrations, they possess certain molecular characteristics that distinguish them from other substances dissolved in water. Our detector is designed to identify these features." Detection Challenge PFAS is a type of oil-resistant and waterproof chemical used in various consumer goods and industrial products, including non-stick cookware, fast food packaging, firefighting foam, raincoats, and stain-resistant carpets. These chemicals are often referred to as "forever chemicals" due to their incredible persistence, which prevents natural degradation and leads to their accumulation in the environment and the human body over time. In recent years, studies have linked PFAS to health issues, including cancer, thyroid problems, and weakened immune systems. In light of these findings, the U.S. Environmental Protection Agency has proposed new limits for perfluorooctane sulfonate and perfluorooctanoic acid. "The issue with implementing these restrictions is that detecting PFAS is highly challenging and time-consuming," Chen said. "Currently, you cannot simply collect water samples and test them at home." The gold standard for measuring PFAS levels is a costly laboratory test known as liquid chromatography/tandem mass spectrometry, which can separate compounds and provide information on each one. Researchers attempting to develop faster and cheaper PFAS testing methods face several challenges: first, the concentration of PFAS chemicals in water is typically much lower than that of dozens of other more common contaminants. Additionally, there are thousands of different PFAS chemicals, with only minor variations in their chemical structures, but significant differences in their health effects and regulatory limitations. Over the past fifteen years, Chen's team has been developing highly sensitive portable sensors on computer chips. This technology has already been utilized in lead sensors for tap water, and the research group suspects the same method could be applied to PFAS sensing. Their proposal to adapt the technology for PFAS detection became part of the Great Lakes Water Innovation Engine initiative by the National Science Foundation. Designed by AI The key point of the sensor is that if PFAS molecules adhere to the device, it will change the conductivity flowing through the surface of the silicon chip. But he and his colleagues must figure out how to make each sensor highly specific to a PFAS chemical substance, such as PFOS. To this end, Chen, Ferguson, Darling, and their team turned to machine learning to help select unique probes that could be placed on sensing devices and ideally only bind to the PFAS of interest. In 2021, they received the Discovery Challenge Award from the University of Chicago Data and Computing Center (CDAC) to support their use of artificial intelligence in designing PFAS detectors. In this case, machine learning is a tool that can quickly screen countless chemical probes and predict which probes are the best candidates to bind to each PFAS, "Chen said. In this new paper, the research team demonstrates that one of the predicted probes does selectively bind to perfluorooctane sulfonate, even though the levels of other common chemicals in tap water are much higher. When water containing perfluorooctane sulfonate flows through their equipment, this chemical substance will bind with new probes, thereby changing the conductivity of the chip. The degree of change in conductivity depends on the content of perfluorooctane sulfonate. In order to ensure the correct readings of the new device, the team collaborated with the US Environmental Protection Agency to confirm the concentration using approved liquid chromatography/tandem mass spectrometry and verify that its level is consistent with the level detected by the new device. The team further demonstrated that the sensor can maintain its accuracy even after multiple detections and washings, demonstrating the potential of real-time monitoring. Chen said, 'Our next step is to predict and synthesize new probes for other different PFAS chemicals, and demonstrate how to scale them up.'. ”From there, we can perceive many other possibilities in the same way - from chemicals in drinking water to antibiotics and viruses in wastewater The ultimate result may be that consumers can test their own water and make better choices about their environment and consumption. Collaborate with the forefront of AI era and open the door to more ordinary users! Whether you are a curious enthusiast of new technologies or a professional looking to improve your skills, there are courses and resources here that are suitable for you. Source: Sensor Expert Network

The team led by Suraj Shinde at Jeonbuk National University systematically reviewed the latest advancements in wearable sweat-sensing patches (WSPs) for personalized healthcare monitoring, offering a pathway to integrate WSPs into flexible human-machine interfaces, personalized healthcare solutions, and closed-loop systems.

In the field of industrial measurement, "compactness" and "precision" were once mutually exclusive, but the emergence of Senther Technology's 1001 series LVDT displacement sensors has broken this deadlock. This product series redefines installation possibilities with an astonishingly compact design—boasting an outer diameter of just 5.8mm and an inner diameter of 3.2mm, allowing it to be effortlessly embedded in tight spaces such as solenoid valve cores and robot joints, effectively addressing the industrial pain point of traditional sensors being "nowhere to be placed.".