Friday, April 03, 2026

 

Disinfectants influence microbes across hospital rooms



Hospital sink drains and airborne dust harbor disinfectant-tolerant bacteria




Northwestern University




Just because a topical antiseptic is swabbed on the skin doesn’t mean it stays on the skin.

In a new study, Northwestern University scientists studied how a powerful antiseptic, called chlorhexidine, affects bacteria in hospital environments. To prevent infections, hospitals heavily rely on chlorhexidine wipes to sterilize patients’ skin before procedures.

Through laboratory experiments, the researchers discovered that traces of chlorhexidine linger on surfaces much longer than previously known — long enough to help microbes build tolerance. By analyzing samples from a medical intensive care unit (MICU), the team also found chlorhexidine-tolerant bacteria spread throughout the hospital environment through touch — and, surprisingly, through the air.

The findings offer new insights into how disinfectants interact with microbes in indoor environments and could help inform strategies for preventing infection and antimicrobial resistance.

The study will be published on (Thursday) April 2 in the journal Environmental Science & Technology.

“Even though chlorhexidine is applied to patients’ skin, we saw evidence that it affects the microbes in the room all around the patients,” said Northwestern’s Erica M. Hartmann, who led the study. “Microbes and chemicals do not stay where we put them, and they can influence antimicrobial resistance. Our results suggest this is true for hospitals, but I have no reason to think there’s anything special about hospitals. I expect we would see the exact same thing if we looked at personal care products and microbes in homes, schools or anywhere else.”

An indoor microbiologist, Hartmann is a professor of civil and environmental engineering at Northwestern’s McCormick School of Engineering.

‘Keeping high-risk patients safe’

Widely used in healthcare since the 1950s, chlorhexidine is an important chemical for preventing infections in hospitals. Healthcare workers use products containing chlorhexidine in routine medical care, including the daily bathing of MICU patients, preparing skin before surgery or catheter insertion, sterilizing equipment and washing hands. It’s also commonly used in prescription mouthwashes for dental care and in veterinary clinics.

“Chlorhexidine is used in environments where patients are incredibly vulnerable, and physicians want to make sure microbial exposures are highly controlled,” Hartmann said. “It’s a well-regulated chemical and really important for keeping high-risk patients safe.”

But after chlorhexidine is applied to the skin, it appears to live a second life.

To track how chlorhexidine affects the environment, Hartmann and her team conducted a two-pronged study. First, the team designed laboratory experiments to simulate hospital cleaning. Then, they conducted an environmental survey inside a MICU.

Residue lingers for longer than 24 hours

In the laboratory, Hartmann’s team applied chlorhexidine to common materials — plastic, metal and laminate — often found in hospitals. Then, they cleaned those surfaces with chlorhexidine-free disinfectants typically used to sterilize hospital environments.

Even after these cleaning treatments, chlorhexidine residue lingered on surfaces after 24 hours. The residue levels were too low to kill bacteria but high enough to expose them to the chemical. In these conditions, surviving microbes can develop tolerance to the disinfectant.

To explore what happens under those sub-lethal conditions, the team exposed several clinically relevant bacteria, including Escherichia coli, to trace concentrations of chlorhexidine. Even after a full day of exposure, the microbes survived.

Sink drains are a hotspot

Next, Hartmann and her team conducted an environmental survey inside a MICU, collecting nearly 200 samples from hospital bed rails, keyboards, doorsills, light switches and sink drains. From those samples, they isolated more than 1,400 bacteria, and about 36% exhibited some level of tolerance to chlorhexidine.

While bacteria showed up all over the MICU, sink drains stood out as the biggest hotspot. Compared to dry surfaces, drains contained far higher levels of bacteria, including strains capable of tolerating much higher concentrations of chlorhexidine. According to Hartmann, hospital workers have long been concerned about sink drains because of the P-trap, the U-shaped pipe beneath the sink that traps a small amount of water to block sewer gas from escaping.

“Wherever there’s water, you will invariably have microbes,” Hartmann said. “Sink drains can be a reservoir for antimicrobial-resistant pathogens in hospitals. And the fear is that every time you run water, it generates aerosols. That has potential for re-exposures.”

Hitching a ride on airborne particles

In perhaps the most surprising finding, Hartmann and her team found bacteria with signs of chlorhexidine tolerance in samples collected from the top of doorsills.

“Our original hypothesis was that we’d find evidence of chlorhexidine in high-touch areas like light switches,” Hartmann said. “We included doorsills as a negative control.”

Because people rarely touch doorsills, the finding suggests bacteria might have hitched a ride on airborne particles, like dead skin cells. According to Hartmann, dust on doorsills can trap these particles circulating in the air.

“The point is not that we need to clean our doorsills,” she said. “The point is that we need to think about airflow pathways as a potential route of exposure or microbe transport within a built environment. Every time we walk around, we shed microbes, skin and chemicals that are on our skin. Some of that potentially floats around and deposits elsewhere in the room.”

Homes, offices do not need to be disinfected

While Hartmann emphasizes that chlorhexidine remains necessary and effective in clinical settings, she said the findings underscore the message that antimicrobial chemicals can have unintended consequences. Unless a person is actively sick or immune compromised, the environment around them does not need to be disinfected. To prevent antimicrobial resistance, Hartmann recommends using plain soap and water to clean our homes and offices.

“The MICU is an incredibly sensitive environment with incredibly vulnerable people,” she said. “But, elsewhere, we rarely need to disinfect. We don’t need to expose ourselves and our environments to these chemicals because those exposures are not necessarily benign.”

The study, “Hospital environments harbor chlorhexidine tolerant bacteria potentially linked to chlorhexidine persistence in the environment,” was supported by the Searle Leadership Fund.

 

Science confirms torpedo bat works as well as regular bat



Washington State University
Torpedo bat testing at WSU 

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WSU researchers determined that torpedo bats and traditional bats performed equally well regarding hitting power.

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Credit: WSU Voiland College of Engineering & Architecture





PULLMAN, Wash. — The New York Yankees took the baseball world by storm with the newly designed torpedo bat last year, but the revolutionary design has ended up being no better than a standard bat for hitting the ball out of the park.

In the first-ever laboratory experiments done on the bat, a research team determined that the torpedo bat and traditional bat perform equally well in hitting power with only a slight difference in the location of the bat’s sweet spot. The researchers, including Lloyd Smith from Washington State University, Alan Nathan from University of Illinois and Daniel Russell from Penn State University, will present their work at the upcoming International Sports Engineering Association conference set for June 1–4 on the Pullman campus.

“Wood is wood,” said Smith, professor in WSU’s School of Mechanical and Materials Engineering and director of the university’s Sports Science Laboratory.

Smith said he wasn’t exactly surprised at the findings. He has been studying bats for a couple of decades and has seen lots of excitement over the years — as when people debated ash versus maple bats, for instance. The news about the torpedo bats confirms his theory that wood material just doesn’t have a lot of pizazz.

“When it comes to baseball, there’s not a lot you can do with wood,” he said. “If your goal is to keep the game steady and consistent and not have a lot of change, wood bats are good.”

The torpedo bat went viral, receiving worldwide attention in spring 2025 when the New York Yankees used the new type of bat for the first time in a game and set a team record, hitting nine home runs in that game.

Although the torpedo bat can be the same weight as a standard bat, it has a slightly different shape. The diameter of a traditional bat tapers from the handle to the barrel and then gradually increases to the tip of the bat. Researchers who made the torpedo bat removed wood from the barrel tip and added it to the sweet spot, so that the diameter tapers down from the sweet spot to the tip.

For the study, the researchers created two maple bats that were duplicates of a standard Major League Baseball bat. Two additional maple bats were made with a torpedo shaped barrel that gave them the same swing weight as the standard bat. To measure the bats’ ball-bat coefficient of restitution, or how much energy the bat returns to the ball, the researchers fired baseballs from an air cannon at a stationary bat and then used light gates and cameras to measure the speed of the incoming and rebounding ball.

The team found nearly identical performance for the torpedo and standard bats except that the sweet spot for the torpedo bat was a half inch farther from the bat tip than the standard bat.

“It was actually pretty phenomenal how close they were,” said Smith.

Of course, baseball players don’t care how much energy is lost — they just want to know how far the ball can be hit, said Smith.

In that case, the bats have the same swing weight, so that if a batter were blindfolded, he wouldn’t be able to tell the difference in how they felt. But because the ideal spot to hit the ball is closer in than on a standard bat, the torpedo bat will actually hit the ball a little bit slower than a standard bat would, he said.

“If you’re comparing both bats at the sweet spot, the ball hitting the torpedo bat is going to be traveling a little bit slower than the standard bat, so it will hit the ball not quite as fast or as far,” said Smith.

For some players who like to hit the ball closer in, the torpedo bat might be a better option for them, though, he added. And because the barrel is wider in a place where those batters do hit, they will be more likely to hit the ball more often — giving players a higher batting average.

In the first-ever laboratory experiments done on the bat, a research team determined that the torpedo bat and traditional bat perform equally well in hitting power with only a slight difference in the location of the bat’s sweet spot.

Credit

Photo courtesy of the Voiland College of Engineering and Architecture, WSU.

 

PediaBench: a comprehensive Chinese pediatric dataset for benchmarking large language models



Higher Education Press

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The dataset construction and evaluation process.

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Credit: HIGHER EDUCATION PRESS





Existing datasets for medical QA cannot comprehensively assess the proficiency of LLMs in pediatrics. To fill this problem, a research team led by Hui LI and Yanhao WANG published their new research on the benchmark of LLMs for pediatric QA in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.

The team introduced PediaBench, the first Chinese pediatric dataset encompassing 5 question types and 12 disease groups, and devised an integrated scoring scheme to thoroughly assess each LLM's proficiency across all types of questions in a unified manner. Finally validated the effectiveness of PediaBench with extensive experiments on 20 open-source and commercial LLMs.

In the research, they first introduced the construction process of the PediaBench dataset. The questions of PediaBench are collected from various public sources, including the Chinese national medical licensing examination, final exams of universities in medicine, pediatric disease diagnosis and treatment standards, and clinical guidelines. The questions are classified into five types: true-or-false (ToF), multiple choice (MC), pairing(PA), essay-type short answer (ES), and case analysis (CA). They use GLM to classify the questions into disease groups according to the International Classification of Diseases (ICD-11) standard issued by the WHO. Then they devise an integrated scoring criterion to evaluate the performance of each LLM. For ToF and MC questions, using accuracy as the basic measure. And assigning a weight to each question based on its difficulty level. For PA questions, using an equal weight of 3 and give a score of 1 for a partially correct result. And for ES and CA questions, using GPT-4o to score each LLM's answers. Finally, they assigned a fixed proportion to each type of question and calculated the integrated score.

The experimental results show that only a few LLMs achieve a passing score of at least 60. the high requirement for factuality in medical applications, there is still a significant gap when deploying LLMs as assistants for pediatricians.

 

Machine learning tracks methane emissions from orbit





Journal of Remote Sensing

Schematic illustration of the CH4Vision algorithm workflow and its application demonstration. 

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Schematic illustration of the CH4Vision algorithm workflow and its application demonstration.

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Credit: Journal of Remote Sensing





Methane is one of the most powerful greenhouse gases, yet quantifying its emissions remains difficult at large scales. A new framework, CH4Vision, addresses this problem by estimating methane flux directly from hyperspectral satellite imagery. By combining plume morphology, concentration patterns, and machine learning, the method improves both the accuracy and robustness of satellite-based methane monitoring. The approach offers a practical path toward large-area emissions surveillance and could strengthen efforts in climate mitigation and environmental governance.

Methane is the second most important anthropogenic greenhouse gas after carbon dioxide, with a global warming potential roughly 28–34 times greater over a 100-year timescale. Major sources include fossil-fuel production, agriculture, livestock, and landfill waste. In recent years, hyperspectral satellite remote sensing has become an important means of detecting methane emissions because it can identify methane absorption features and reveal plume structures from orbit. However, converting these observations into reliable emission estimates remains a major challenge. Widely used approaches such as the integrated mass enhancement (IME) method depend on simplified assumptions about the relationship between plume mass and wind speed. In practice, these assumptions often fail to capture the complex interaction between atmospheric transport and plume shape, leading to substantial uncertainty in methane flux estimates.

To address this limitation, researchers from East China Normal University and collaborating institutions recently reported (DOI: 10.34133/remotesensing.1013) a new methane-monitoring framework called CH4Vision in the Journal of Remote Sensing (published February 27, 2026). Using hyperspectral observations from the GaoFen-5 satellite, CH4Vision estimates methane emission flux by analyzing both plume morphology and concentration distributions. Rather than relying on a simple linear relationship, the framework uses machine learning to infer emission rates from a richer set of plume characteristics, with the goal of improving the reliability of satellite-based methane quantification.

The core idea of CH4Vision is that methane plumes contain spatial information that reflects source strength. Instead of focusing mainly on plume area and wind speed, as in conventional IME-based methods, the framework extracts a broad set of descriptors that characterize plume geometry and concentration structure. These include plume area, perimeter, aspect ratio, gradient-related features, and concentration statistics. CH4Vision then integrates these variables into a random-forest regression model, allowing it to capture nonlinear relationships between plume structure and emission intensity.

To build the framework, the researchers first generated a large training dataset using atmospheric large-eddy simulations. These simulations produced thousands of methane plume scenarios under different emission rates, wind conditions, and turbulence regimes. The simulated plumes were then embedded into hyperspectral scenes acquired by the Advanced Hyperspectral Imager on GaoFen-5. Methane enhancement concentrations were retrieved using an improved algorithm known as SSRMF, which reduces background noise and false detections by reconstructing pixel-level reference spectra and preserving spatial continuity across plume structures. From the resulting plume maps, the team extracted morphological and concentration-based features and used them to train a random-forest regression model containing 500 decision trees to estimate methane flux directly from plume characteristics and wind information.

The model performed well in evaluation tests. Compared with the IME method, CH4Vision increased the coefficient of determination (R2) by 3–9% and reduced estimation errors by 14–36.5%. It also showed greater resilience to uncertainty in wind speed and methane retrieval, which is particularly important for operational monitoring under real observational conditions. These results suggest that incorporating plume morphology into the estimation process can substantially improve methane flux quantification from satellite imagery.

The framework was further tested in both controlled and real-world settings. In controlled methane release experiments conducted in Arizona, CH4Vision predicted emission rates within about ±100 kg per hour of the true values. When applied to satellite observations over Shanxi Province in China, the method detected hundreds of emission sources and indicated that traditional IME-based approaches substantially underestimated emissions from strong sources. These findings support the practical value of CH4Vision for identifying and quantifying methane emissions in complex real environments.

Overall, CH4Vision provides a powerful new tool for monitoring methane emissions from space. By improving the accuracy and robustness of satellite-based flux estimates, the framework could help researchers better constrain global methane budgets and identify major emission hotspots. It may also support environmental regulation, climate policy, and energy-sector oversight. Because the method emphasizes plume morphology rather than gas-specific assumptions, it may be adaptable in the future to other atmospheric pollutants detectable by hyperspectral satellites.

###

References

DOI

10.34133/remotesensing.1013

Original Source URL

https://doi.org/10.34133/remotesensing.1013

Funding information

This work is supported by the National Natural Science Foundation of China (Grant No. 425B2007), the International Research Center of Big Data for Sustainable Development Goals (CBAS2022GSP07), and the Fundamental Research Funds for the Central Universities.

About Journal of Remote Sensing

The Journal of Remote Sensingan online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.

 

Machine learning designs cheaper and rust-proof steel for 3D printing




International Journal of Extreme Manufacturing

Laser 3D printing the AI-designed rust-proof ultra-high strength and ductility steel 

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A close-up of a laser-directed energy deposition (LDED) system fabricating the novel AI-designed ultra-high strength steel, which achieves a rare balance of strength and ductility, excellent corrosion resistance, and requires only 6 hours of single-step heat treatment at low cost.

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Credit: By Yating Luo, Tao Zhu, Cunliang Pan, Xu Ben, Xudong An, Xiaoming Wang and Hongmei Zhu*






A machine-learning strategy has generated a new class of ultra-high strength and ductility steel for 3D printing that costs less, resists rust, and requires only a fraction of the usual processing time.

In International Journal of Extreme Manufacturing, a new study demonstrates that integrating artificial intelligence with the fundamental physical and chemical properties of elements can rapidly identify optimal alloy recipes. The resulting metal achieves a rare balance of extreme strength and ductility, solving a persistent bottleneck in heavy manufacturing and aerospace engineering.

Currently, producing ultra-high strength and ductility steels through 3D printing requires heavy doses of expensive elements like cobalt, molybdenum, or high levels of nickel. Even with these premium ingredients, the printed parts must undergo complex, multi-step heat treatments in industrial furnaces to reach their final strength, and they often remain highly vulnerable to corrosion in harsh environments.

To bypass this trial-and-error chemistry, a research team from the University of South China and Purdue University turned to an "interpretable machine learning" model. Instead of treating the AI as a black box that simply guesses combinations, the team fed the algorithm 81 fundamental physicochemical features of various elements, such as their atomic radius, electron behavior, and how fast sound travels through them.

Predicting properties

The algorithm calculated that a specific blend of iron and chromium, mixed with precise, small amounts of cheaper elements like silicon, copper, and aluminum, would form the ideal internal structure. After 3D printing the metal Fe-15Cr-3.2Ni-0.8Mn-0.6Cu-0.56Si-0.4Al-0.16C (wt.%) using a laser-directed energy deposition technique, the researchers baked it in a single-step tempering process at 480°C for just six hours.

The physical testing matched the algorithm's predictions. The resulting steel withstood stresses of 1,713 MPa and stretched by 15.5% before breaking. This represents an approximately 30% increase in strength over the metal's raw, printed state, accompanied by a doubling of its ductility.

The team investigated the metal's internal architecture to understand the mechanics behind this performance. They found that the short heat treatment forced the metal to grow a dense network of nanoscale particles, including copper and nickel-aluminum.

When physical stress is applied to the metal, these tiny particles act as roadblocks that pin down structural defects and stop them from spreading, drastically increasing the force required to break the part. Simultaneously, microscopic pockets of a softer phase, known as austenite, act as shock absorbers by changing their crystalline shape to soak up energy, a phenomenon that prevents the steel from snapping under tension.

Rust resistance

The AI-designed recipe also solved the rust problem inherent to many high-strength alloys. In typical steels, the formation of carbides drains chromium from the surrounding metal, creating vulnerable, chromium-depleted zones where corrosion takes hold. The researchers found that the nanoscale copper particles in their new steel effectively expelled chromium during their formation, forcing it to remain evenly distributed throughout the surrounding matrix. In salt-water tests, the new alloy degraded at a rate of just 0.105 millimeters per year, significantly outperforming standard commercial stainless steels like AISI 420.

While the interpretable machine learning approach successfully cut costs and processing times, the researchers note that the methodology relies on datasets that are highly specific to certain manufacturing techniques. Because different 3D printing methods heat and cool metals at drastically different rates, data from one fabrication process is often incompatible with another.

In future work, researchers will need to re-screen these fundamental physical features when applying the AI to entirely new material classes. However, the study provides a clear blueprint for moving away from slow and empirical testing, offering a rapid pathway to design custom, high-performance components.


International Journal of Extreme Manufacturing (IJEM, IF: 21.3) is dedicated to publishing the best advanced manufacturing research with extreme dimensions to address both the fundamental scientific challenges and significant engineering needs.

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