Home Featured Articles List
Environmental Chemistry and Safety

Research team combines machine learning with environmental technology to achieve healthier soil

Scientists have environmental recycling processes that convert farm and forestry waste and polluted soil into useful energy and other useful materials. However, it has been very challenging to precisely control these processes. A research team is using machine learning to regulate the environmental processes, so they are more effective and predictable. Their work provides a technical path for achieving precise, intelligent, and sustainable remediation of polluted soil. Their research was published in the journal Environmental Chemistry and Safety on July 1, 2026. Co-pyrolysis technology is an environmental recycling process that heats waste products from farms and forests, along with polluted soil, in an oxygen-limited environment. This heating reaction converts the waste products and polluted soil into fuel and other useful materials. The challenge that scientists have faced with the co-pyrolysis technology lies with the differences in the raw materials. Because each batch of raw materials behaves a little differently, it is difficult for scientists to accurately control the co-pyrolysis process. The research team from Panzhihua University, Tsinghua University, and Harbin Institute of Technology has developed a theoretical framework that combines machine learning with the co-pyrolysis technology to provide a precise and sustainable solution to turn polluted soil to clean soil. The world produces over 2 billion tons of farm and forest waste every year. Most of this waste is not used in any way. At the same time, more than one-third of the world’s farmlands cannot be used for farming because the soil is polluted with toxic chemicals. Much of the plant waste is dumped in landfills or burned, which causes air pollution. Cleaning the polluted soil requires methods that harm the soil’s health and create new pollution. These two environmental problems are connected to each other. The co-pyrolysis technology offers a solution for farm and forest waste and polluted soil. This process “bakes” the plant waste and polluted soil under oxygen limited conditions.  The plant waste and polluted soil react better together than either of them would work on their own. The reaction that occurs cracks and destroys the organic pollutants and traps the heavy metals so they can no longer harm the environment. Finally, the process turns the waste and soil into a high-performance charcoal-like substance called biochar. While the co-pyrolysis technology offers a promising solution to these environmental problems, the process has been hard to control. Each batch of plant waste and polluted soil is different, making it hard to predict and control the exact repair effects they can achieve. The advances in machine learning technology in recent years give scientists the tools they need to better manage the co-pyrolysis process. Algorithms, such as deep learning, neural networks, and reinforcement learning, help scientists better understand the complex data related to the co-pyrolysis process. Machine learning tools let them to fix the polluted soil at different sizes, ranging from the molecular level all the way to the larger ecosystem. Using machine learning with the co-pyrolysis methods allows scientists to predict the connections across entire process. “Compared to traditional experience-oriented process development models, the core advantage of machine learning driven co-pyrolysis closed-loop design lies in its ability to model and predict high-dimensional nonlinear relationships across the entire chain of raw materials, processes, products, and ecological responses,” said Yuanchuan Ren, Panzhihua University, China. Looking to the future, the research team suggests that the co-pyrolysis closed-loop system that recycles waste could have potential for use beyond Earth. “The technical framework and engineering methods of the co-pyrolysis closed-loop system are expanding beyond the scope of Earth's environmental remediation, gradually extending to the utilization of in-situ resources in extraterrestrial celestial bodies and the construction of interstellar human settlements,” said Ren. The research team includes Yuanchuan Ren, Yuhang Lin, Xuejun Zhu, Hongbo Han, Shiyong Zhao, Renjie Huang, Tingfeng Su, Yan Guo, Fenghui Wu, Qiang Niu, Dandan Chen from Panzhihua University; Cheng Wang from Tsinghua University; and Nanqi Ren from Harbin Institute of Technology. This research is funded by Sichuan Science and Technology Program, the Panzhihua Key Laboratory of Chemical Resource Utilization Open Science Project, Panzhihua Association for Science and Technology Youth Science and Technology Talent Support Project, the Key Laboratory of Dry-hot Valley Characteristic Bio-Resources Development at University of Sichuan Province, and The College Students' Innovation and Entrepreneurship Training Program. DOI Link: https://doi.org/10.26599/ECS.2026.9600049  
Physical Sciences and Engineering
Polyoxometalates

Spinning up a better understanding of molecules

One hundred years after the technique was recognized with the Nobel Prize in Chemistry, analytical ultracentrifugation (AUC) is still making waves in the scientific world. Theodor Svedberg developed the approach in the 1920s, using it to characterize tiny gold particles. Now, AUC helps researchers determine specific properties of individual molecules clustered together, such as those used in pharmaceuticals or industrial chemicals.     A team led by researchers at the University Akron recently published a review of the technology, cataloging use cases focused on how AUC can help solve challenges in characterizing molecular clusters. These clusters comprise clumps of proteins, sugars and other particles that scientists need to understand in deep detail to predict interactions, stability and more of the many diverse solutions encountered across a bevy of areas. They published their work on May 27 in Polyoxometalates. “Analytic ultracentrifugation is a powerful and information-rich technique for characterizing the molecular weight, size, shape, dispersity and association behavior of species in their native solution environment,” said corresponding author Tianbo Liu, professor in the Department of Polymer Science at the University of Akron. “With recent instrumentation and data analysis software, AUC enables more accurate and reliable characterization of diverse species in solution systems, including biomacromolecules, like proteins and carbohydrates; colloids, such as nanoparticle suspensions; surfactants assemblies such as micelle; and polymers, including synthetic polymers in solution and polymer-based nanoparticles in dispersion.” AUC works by spinning solution samples up to 60,000 rotations per minute, forcing the contents to sediment, or to disperse into concentrated gradients based on size and weight. The approach also integrates optical analytics to monitor and characterize the sedimentation of each molecule, taking AUC a step beyond traditional centrifugation, according to Liu. “This overcomes a key limitation of techniques such as light and X-ray scattering, which typically measure the collective properties of solute mixtures,” Liu said, explaining that the other techniques work by assessing how light or X-rays at a sample scatter. He noted that AUC also lacks the need for stationary phases or calibration standards like other techniques, such as chromatography, enabling direct, absolute determination of molecular properties. Another benefit of AUC is that the measurements are performed in solution, meaning surface interactions and system perturbations — and the artifacts they may result in — are minimized. To demonstrate the wide array of information AUC can glean, the researchers highlighted several examples. The first pointed to how AUC reveals the more complicated nature of hydration shells, or the water encapsulating various molecules. Rather than a homogenous layer of oxygen and hydrogen atoms, the water shifts in physical properties and behaviors through the shell, which also varies in thickness. The second example focused on determining the distance between components in molecular cluster solutions. According to the researchers, measuring intermolecular distances is key to understanding how charged macroions self-assemble in dilute solution and how changes in these distances correlate with transitions between different macroscopic phases. The third example detailed the interactions of molecular clusters with amino acids — the compounds that make up proteins. Understanding how amino acids interact with the surfaces of molecular clusters can help inform understanding of how the clusters will interact with biomolecules. Liu noted that these structures are governed by weak, noncovalent interactions between molecules. Although these forces are difficult to measure directly, AUC can reveal their effects by detecting subtle changes in molecular size, weight and shape in solution. “AUC is a powerful technique for investigating complex solution systems,” Liu said. “Owing to their well-defined and uniform size, shape and mass, molecular clusters are particularly well suited for AUC measurements. … AUC offers the distinct advantage of resolving different species prior to analysis, enabling direct determination of their individual concentrations.”  According to the researchers, there is a drawback, however. “The technique requires substantial expertise, experience and instrumentation resources,” Liu said. “As a result, the barrier to entry for new users can be relatively high, with a need for extensive training covering both experimental operation and data analysis.” Other contributors include Ruixin Li, Xiaohan Xu, Kexing Xiao and Bahareh Afsari, all with the University Akron; and Lake N. Paul, with BioAnalysis, LLC. The University of Akron supported this research. DOI Link: https://doi.org/10.26599/POM.2026.9140132  About Polyoxometalates Polyoxometalates (ISSN 2957-9821) is a peer-reviewed (single-blind), open-access and interdisciplinary journal, sponsored by Tsinghua University. Polyoxometalates publishes original high-quality research papers and significant review articles that focus on cutting-edge advancements in Polyoxometalates, and clusters of metals, metal oxides and chalcogenides. Rapid review to ensure quick publication is a key feature of Polyoxometalates. The journal is indexed by ESCI (IF 2025 = 10.4, Top 3), Scopus (CiteScore 2025 = 17.6, Top 3), Ei Compendex, CAS, and DOAJ. For details about Polyoxometalates, please visit: https://www.sciopen.com/journal/2957-9821.
Physical Sciences and Engineering
Energy Materials and Devices

ZnAl-layered double hydroxides template-induced formation of ZnO/ZnSe heterostructures on the surface of coal-tar-pitch derived carbon for high-efficiency sodium storage

Sodium-ion batteries are considered a promising technology for large-scale energy storage because sodium is abundant and widely distributed. A major challenge, however, is finding anode materials that combine high capacity, fast reaction kinetics and long cycling stability while remaining practical and low cost. A research team from Taiyuan University of Technology and Taiyuan University of Science and Technology has reported a strategy that addresses this challenge by using coal tar pitch, an industrial carbon-rich byproduct, as the carbon source for a new composite anode. Their work was published in Energy Materials and Devices on June 11, 2026. The team used a zinc-aluminum layered double hydroxide (ZnAl-LDH) as a template to induce the formation of ZnO/ZnSe heterostructures embedded in hierarchical porous carbon. During synthesis, the LDH template serves two functions: it guides the local structure of the active material and helps generate pores in the carbon framework. This produces intimately connected ZnO/ZnSe-carbon interfaces rather than simply mixing active particles with carbon. The heterostructure is important because ZnO and ZnSe can work together at their interface. The interfacial contact promotes charge transfer, while the mixed oxygen and selenium anion environment, the nanoscale dispersion of ZnO/ZnSe and the conductive carbon network help improve reaction kinetics and reduce the mechanical strain that usually occurs during repeated sodium insertion and extraction.  “Our goal was to turn an inexpensive industrial byproduct into a functional carbon host and then use interface engineering to overcome the kinetic limitations of metal selenide anodes,” said Jian Wang, the corresponding authors of this paper, professor in the College of Materials Science and Engineering at Taiyuan University of Technology. “The LDH template allowed us to build the active heterostructure and the porous carbon architecture in one integrated design.” Electrochemical tests showed that the composite anode achieved a reversible capacity of 637.5 mAh g−1 at 100 mA g−1. Under high-rate cycling, it retained 259.7 mAh g−1 after 1000 cycles at 5 A g−1. Kinetic analysis further showed that capacitive storage dominated the response, contributing 93.3% of the total charge storage at 1.2 mV s−1. This behavior explains the material’s strong rate capability. The team also assembled a full cell using an Na3V2(PO4)3 cathode to evaluate practical potential. The full cell retained 147.5 mAh g−1 after 100 cycles, indicating that the material design is not limited to half-cell testing. “This study shows that template-directed heterostructure engineering can be an effective route for developing advanced sodium-ion battery anodes from low-cost carbon resources,” the Dr. Wang said. “The next step is to further optimize electrode formulation and evaluate the material under more practical cell conditions.” Other contributors include the Yiming Liu, the professor in the College of Environmental Science and Engineering at Taiyuan University of Technology and deputy dean of School of Chemical Engineering and Technology at Taiyuan University of Science and Technology; Yibo Zhao also from the College of Environmental Science and Engineering at Taiyuan University of Technology and in Taiyuan, China. Peihua Li, Haochen Xie, Yalong Wang and Wanggang Zhang from the College of Materials Science and Engineering at Taiyuan University of Technology; Rufeng Tian and Xiaohong Li from the College of Chemistry and Chemical Engineering at Taiyuan University of Technology. This work was supported by the National Natural Science Foundation of China (Grant Nos. U25B20110, 22075197 and 22278290), the Shanxi Provincial Central Guidance Fund for Local Science and Technology Development Projects (Grant No. YDZJSX2024D022), and the Key Research and Development (R&D) Projects of Shanxi Province (Grant No. 202102040201003). DOI Link: https://doi.org/10.26599/EMD.2026.9370093
Physical Sciences and Engineering
Journal of Advanced Ceramics

Insight into the synergistic effect of rare-earth elements on the CMAS corrosion behavior in (RE1/4Tm1/4Yb1/4Lu1/4)2Si2O7 (RE = Gd, Ho and Sc) materials at 1300 °C

Rare-earth disilicates RE2Si2O7 have been widely acknowledged as state-of-the-art environmental barrier coating material in commercial applications due to its superior thermochemical stability and high temperature water vapor corrosion resistance. However, a primary limitation of RE2Si2O7 EBCs for high-performance aero-engine applications-particularly at sustained operating temperatures exceeding 1300 °C is their inadequate resistance to CMAS deposits. The advent of high-entropy or multicomponent design in materials engineering has brought new inspiration to address this bottleneck, and its effectiveness has been proven in recent research. In fact, all these benefits originate from the introduction of various rare-earth ions and their respective advantages in coupling. However, the correlation mechanism between the efficacy of rare-earth components and the final corrosion resistance is still unclear, and an exact guideline for component elements selection is still absent. Recently, a joint team of Professor Sun Luchao’s group from Institute of Metal Research, Chinese Academy of Sciences, and Professor Wang Jingyang’s group from Liaoning Academy of Materials confirmed the possibility of achieving tunable CMAS corrosion resistance in rare-earth disilicates through the synergistic effect of multiple rare-earth components through three novel multicomponent rare-earth disilicate EBC materials, and provided a criterion for designing multicomponent rare-earth silicates with enhanced CMAS corrosion resistance. This team published their work in Journal of Advanced Ceramics on July 28, 2026. “The findings in this work provide valuable insights for in-depth understanding of the synergistic effects among rare-earth components and the correlation mechanism of multicomponent rare-earth disilicates.” Said Jingyang Wang, Vice President of Liaoning Academy of Materials (LAM) and the director of Institute of Coating Technology for Hydrogen Gas Turbines in LAM (China). “In this study, three multicomponent (RE1/4Tm1/4Yb1/4Lu1/4)2Si2O7 (RE = Gd, Ho and Sc) materials were designed and exposed to CMAS at 1300 °C for durations of 1, 4, and 50 h. Mechanistic analysis reveals that the performance divergence primarily stems from distinct corrosion mechanisms: (Gd1/4Tm1/4Yb1/4Lu1/4)2Si2O7 and (Ho1/4Tm1/4Yb1/4Lu1/4)2Si2O7 predominantly undergo dissolution-reprecipitation processes, whereas (Sc1/4Tm1/4Yb1/4Lu1/4)2Si2O7 is dominated by intergranular corrosion penetration. Such mechanistic transition is attributable to compositional tuning of rare-earth elements (Gd and Ho to Sc) in the silicates.” Said Luchao Sun, a professor from Institute of Metal Research, Chinese Academy of Sciences (China). “In this study, elements such as Gd and Ho were proven to reduce the Ca/Si ratios by accelerating the precipitation of apatite phase during reaction. This process not only diminishes calcium in CMAS to reduce its corrosion aggressiveness but also retards CMAS diffusion through the formation of a dense product layer, collectively enhancing the corrosion resistance of disilicates. Thus, an optimal stoichiometric ratio between active (e.g., Gd and Ho) and inert (e.g., Yb and Lu) elements is essential to synergistically activate the precipitation for corrosion mitigation and the intrinsic resistance enhancement, thereby maximizing CMAS corrosion resistance in disilicate systems.” Said Luchao Sun. About Author Luchao Sun is currently a professor of advanced ceramics and composites division, Institute of Metal Research, Chinese Academy of Sciences. His main research interests cover theoretical and experimental investigations on advanced ceramics and composites for harsh environment applications and advanced materials for thermal/environmental barrier coatings. Jingyang Wang is currently the Vice President of Liaoning Academy of Materials (LAM) and the director of Institute of Coating Technology for Hydrogen Gas Turbines in LAM. His research covers fundamental research and engineering applications of structural ceramics, composite materials and high-temperature protective coatings for extreme service environments. Ziyu Wang is currently a doctor candidate in Shenyang National Laboratory for Materials Science, Institute of Matel Research. His research focuses on the composition design, preparation, and performance optimization of the environmental barrier coatings for aero-engines. Funding This work was supported by the National Natural Science Foundation of China (U21A2063); LiaoNing Revitalization Talents Program (XLYC2203090); International Partnership Program of the Chinese Academy of Sciences (172GJHZ2022094FN). About Journal of Advanced Ceramics Journal of Advanced Ceramics (JAC) is an international academic journal that presents the state-of-the-art results of theoretical and experimental studies on the processing, structure, and properties of advanced ceramics and ceramic-based composites. JAC is Fully Open Access, monthly published by Tsinghua University Press, and exclusively available via SciOpen. JAC’s 2024 IF is 16.6, ranking in Top 1 (1/34, Q1) among all journals in “Materials Science, Ceramics” category, and its 2024 CiteScore is 25.9 (5/130) in Scopus database. ResearchGate homepage: https://www.researchgate.net/journal/Journal-of-Advanced-Ceramics-2227-8508
Ceramics
Ocean

Predicting underwater landslides to protect vital infrastructure

Underwater landslides are a destructive force, putting vital submarine infrastructure at serious risk and can even generate damaging tsunamis. By analyzing research completed from 2000 to 2025, scientists tracked this phenomenon to better understand what triggers the landslides, how they move and generate tsunamis, and the dangers they pose. After reviewing the data, researchers determined three recommendations for submarine landslide research going forward. The results of this review were published in Ocean on 6 March. “Given the rapid growth of global marine development, studying the mechanisms of triggering, movement, and disaster impacts behind submarine landslides has become increasingly urgent. More than 25% of global oil and gas production currently originates offshore, with projections indicating substantial growth in marine energy, including oil, gas, and wind energy, activities by 2040”, said Prof. Fawu Wang, a researcher at Tongji University in Shanghai, China. Marine environments are complex, and landslides are caused by interacting factors. They are most often caused by earthquakes, but longer-term processes like rapid sedimentation, material like magma and mud settling in the ocean, and erosion also contribute to submarine landslides. Shaking caused by earthquakes increases sliding force, reduces the strength of the soil, and starts liquefaction, which is when loosely packed sediments weaken. Even even small earthquakes can trigger large submarine landslides. Hydrodynamic forces are a more easily monitored cause of submarine landslides. “Waves, tides, bottom currents, and internal waves can trigger submarine landslides by increasing bottom shear stress, decreasing the shear strength of sediments, and causing dynamic changes in pore water pressure. These processes interact with the sediment structure, compromising its stability and potentially resulting in slope failure, particularly in regions with steep slopes or loose unconsolidated sediments”, said Prof. Wang. While scientists have been able to monitor the seafloor for the conditions leading to landslides, more research is needed to make the models more accurate. There have been multiple studies to better understand how submarine landslides move using different techniques. They have used experimental devices and numerical simulations and physical models, but both methods are limited by the complexity of marine conditions. Researchers suggested that future research should look at modeling frameworks that incorporate machine learning, observation, and numerical models. Finally, the review focused on research into how submarine landslides can cause a tsunami. Most tsunamis are caused by earthquakes, and those caused by submarine landslides are usually smaller in strength and scale. Even so, these tsunamis can be destructive, affecting coastal regions and engineering infrastructure that is right off the coast. Though these tsunamis are generally weaker, the initial wave can often be much higher than an earthquake-generated tsunami and cause significant damage. Researchers also focused on specific offshore infrastructure, which is only increasing because of renewable energy developments. “Submarine landslides can result in catastrophic consequences, including pipeline suspension and rupture, platform overturning, and erosion of wind turbine foundations. As human activities extend into deeper seas, interactions between submarine landslides and marine engineering infrastructure are becoming more frequent, highlighting the urgent need to systematically uncover the disaster mechanisms associated with these events”, said Prof. Wang. Looking ahead, researchers developed three conclusions that give guidance for future research into submarine landslides. First, they suggested that future research focus on understanding how different triggering factors interact and establishing metrics for predicting dangerous landslides. Second, they suggested improving numerical simulations to better reflect actual underwater environments. And third, they recommended improving modeling to incorporate machine learning and probabilistic analysis, along with incorporating coastal vulnerability for better tsunami risk assessment. Other contributors include Youqian Feng and Ye Chen of Tongji University and Kongming Yan of University of Cambridge. The Fundamental Research Funds of China for the Central Universities and the Interdisciplinary Collaborative Research Demonstration Project at Tongji University supported this research.   DOI Link: https://doi.org/10.26599/OCEAN.2025.9470014 About Ocean Ocean is an international peer-reviewed journal that offers open access and serves as a multidisciplinary platform for the state-of-the-art research and practice in the domains of ocean science, technology, and engineering. The journal is dedicated to publishing articles, reviews and perspectives in these areas, with the goal of promptly disseminating and promoting theoretical, numerical, site-based, and experimental advancements in the context of global sustainability.
Physical Sciences and Engineering
Computational Visual Media

Self-supervised 3D AI data training alleviates model learning bottleneck

Artificial intelligence (AI) is vastly superior to human analysis in many ways—particularly in processing, analyzing and synthesizing massive quantities of information at incredibly fast speeds—but AI model performance is only as good as the data used to train it. Much like humans, AI models need to be taught to perform specific tasks, and oftentimes, the more quality data used to train a model, the more proficient the model will be at recognizing patterns or processing data, for example. Currently, one of the biggest bottlenecks for AI model implementation is the training phase, where models are taught to perform specific tasks using data. Prior to training, large amounts of data often require humans to annotate or structure data into specific formats that algorithms require. As models get larger, the amount of labor and time required to transform or appropriately label data can grow exponentially, delaying model training and deployment. To address this issue, a group of researchers from Fudan University and Nanyang Technological University wrote a comprehensive literature review outlining how AI models can supervise their own learning using point cloud data, or raw, three-dimensional (3D) spatial coordinates that correlate to the surface of an object, greatly reducing or eliminating the amount of human labor required to annotate large datasets for model training. The team published their review on April 22 in the journal Computational Visual Media, published by Tsinghua University Press. “Just as self-supervised learning has already revolutionized how AI understands natural language and 2D images, it is now becoming the key to unlocking 3D data. 3D point clouds are essential for technologies like self-driving cars and robotics, but training AI to understand them traditionally required humans to manually label millions of 3D coordinates, creating a great bottleneck. This technology is attracting immense attention because it allows the AI to learn generic, powerful 3D representations directly from raw, unlabeled data, paving the way for the emergence of true 3D foundation models,” said Ben Fei, research fellow at the Chinese University of Hong Kong and first author of the review paper. One popular self-supervised AI learning approach uses deep neural networks (DNNs) and assigns various pretext tasks for the network to solve. The pretext task serves as a temporary, artificially created problem to train an AI model on unlabeled data. This task forces the model to understand the fundamental structure and patterns of the data, which can later be applied to downstream real-world applications. Several different pretext tasks have been proposed for self-supervised learning, including point cloud reconstruction, or the conversion of unstructured, 3D data into a usable digital 3D model; contrastive learning, which teaches models to understand data through comparison; and multi-modal learning, which integrates many different types of data into a single unified model.  Pretext tasks share two common properties: 1) The visual features of point clouds must be captured by DNNs to solve the pretext task, and 2) the supervisory signal is generated from the data itself, which results in self-supervision, by exploiting its structure. “The main takeaway is that effective 3D representation learning is closely tied to choosing the right pre-training tasks that leverage the data itself rather than human labels. For instance, these self-supervised approaches allow a single model to learn rich, generic geometric features from the data's own structure. This comprehensive understanding serves as a powerful prior that can be easily transferred to various applications, from indoor robotics to outdoor autonomous driving. Our paper provides a comprehensive, unified roadmap that categorizes these learning schemes, showing how to systematically build and adapt these frameworks toward capable 3D foundation models,” said Fei. Despite these advances, hurdles still exist for self-supervised 3D model training. “Our next step is to overcome the challenges unique to 3D representation learning, including optimizing the massive computing power required and establishing higher-quality, standardized 3D pre-training datasets. Our ultimate goal is to break down the barriers between different modalities. By successfully scaling up 3D foundation models and aligning them with large language models and 2D vision, we hope to provide the crucial spatial intelligence needed to achieve true Artificial General Intelligence,” said Fei. Jingyi Xu, Yixuan Li, Weidong Yang, Qingyuan Zhou, Liwen Liu and Tianyue Luo from the School of Computer Science at Fudan University in Shanghai, China; and Ying He from the College of Computing and Data Science at Nanyang Technological University in Singapore, Singapore also contributed to this research. This research was supported by the JC STEM Lab of AI for Science and Engineering, funded by The Hong Kong Jockey Club Charities Trust, the MTR Research Funding (MRF) Scheme (CHU-24003), the Research Grants Council of Hong Kong (CUHK14213224) and the Ministry of Education, Singapore, Academic Research Fund Grant (RT19/22).  
Computer Science
Friction

The multifaceted science of where rubber meets road

Increasing pavement skid resistance on a London highway by 50% reduced the total number of accidents over four years by 45%, according to the United Kingdom Transport and Road Research Laboratory. The United States National Transportation Safety Board and Federal Highway Administration stated that poor pavement skid resistance is a major cause of driving accidents. These are just two sources cited by an international team of researchers who recently reviewed more than 3,000 studies on tire-road friction and found that while tire-road friction theory and simulations have been well developed across the research scales — rubber-pavement scale, the tire-road scale and the vehicle scale — the scales themselves remain largely unconnected. According to the researchers, who published their work on Feb. 10 in Friction, that disconnect is a major hindrance in advancing the field, and ultimately, road safety for all.  “The concept of pavement skid resistance pertains to the capacity of road surfaces to provide adequate friction during diverse vehicular operations, including braking, accelerating and cornering,” said co-corresponding author Yuchuan Du, professor, Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, China. “It is critical for preventing skidding incidents and ensuring driving safety, serving as a significant indicator for road performance evaluation and maintenance decisions.” Tire-road friction has been studied for more than half a century, Du said, explaining that early studies focused on improving the materials and structure of the tire. However, researchers later learned that simply improving tires did not reduce the proportion of skidding accidents, so more researchers in the field focused on the skid resistance of the road itself. “From the perspective of road researchers, the primary concern is the role that the pavement itself plays within the tire-rubber friction system, which is referred to as the pavement skid resistance,” Du said. “This is an inherent characteristic of the pavement within the tire-road friction system, and understanding the tire-road friction system is fundamental for evaluating the skid resistance of asphalt pavements.” From their analysis of the field’s scientific literature, the researchers found that studies typically fell into one of four research paradigms: experimental science, theoretical science, computational science and data science. They also found that the research can be categorized into the rubber-pavement scale, the tire-road scale and the vehicle scale. Across the paradigms and scales, the team found that experimental studies were limited by specific variables that have made reproducibility difficult, and that computational studies were limited by their ability to integrate friction mechanisms, making them difficult to interpret properly. “The synergistic development of the four research paradigms can promote and advance the understanding and application of tire-road friction mechanisms,” Du said. The researchers developed a multiscale architecture for tire-road friction research to clarify the different concepts and boundaries as they feed into two coefficients: rubber-pavement friction and tire-pavement adhesion. Both feed into determining the skidding risk of the vehicle, along with other variables. The two coefficients are determined by pavement factors, such as texture and aggregate; rubber-related material properties, like density; environmental factors, including temperature and humidity; tire-related physical conditions, such as pattern and pressure; and vehicle-related operation conditions, like load and velocity. “With the accumulation of data, data-based research has gradually become the primary research paradigm for evaluating pavement skid resistance,” Du said. “However, the integration of data with theoretical research remains a question that requires further exploration. Leveraging theory to enhance the predictive capabilities of data models and utilizing data mining to reveal the underlying mechanisms and patterns of pavement skid resistance are areas that warrant deeper investigation.” Collaborators include Zihang Weng and co-corresponding author Zhen Leng, Department of Civil and Environmental Engineering, Hong Kong Polytechnical University, China; Chenglong Liu and Difei Wu, Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, China; and Bryan T. Adey, Institute of Construction and Infrastructure Management, Swiss Federal Institute of Technology, Switzerland. Weng is also affiliated with Tongji University and Hong Kong Polytechnic University Shenzhen Research Institute. The National Natural Science Foundation of China, the Shanghai Science and Technology Innovation Action Plan and the Carbon Neutrality Funding Scheme of PolyU supported this research.
Engineering
Aging Research

How we age: 3D organization of DNA offers clues to the underlying mechanisms of aging Reveals connections between aging and disease

A research team has examined how aging and diseases are connected by exploring the aging-related remodeling of chromatin architecture. This study of chromatin architecture, the three-dimensional organization of DNA, gives scientists a better understanding of aging and its underlying mechanisms. Their review is published in the journal Aging Research on May 8, 2026. With senescence, or the biological process of aging, an irreversible and systemic process occurs, characterized by declining cellular functions, loss of tissue homeostasis, and deterioration of overall physiological capacity. Scientists know that aging is a primary risk factor for a variety of chronic non-communicable diseases, such as Alzheimer’s disease, Parkinson’s disease, cardiovascular disease, metabolic syndrome, and cancer. In their review, the team synthesizes the recent studies that explore how multiscale chromatin reconfiguration influences gene regulation and cellular identity in senescence. They summarize the representative disease settings and implicated structural layers. The team further discusses the major technical challenges. These include the challenge of accurately capturing the biological and genetic differences between individual cells, the limitations of fixed-cell assays for capturing chromatin dynamics, and the difficulties in robust multi-omics integration.  The team proposes future directions for leveraging single-cell and spatiotemporal three-dimensional genomics to dissect the mechanisms linking senescence to aging and to inform the development of new therapeutic treatments. Because the global population is rapidly aging, scientists are looking to unravel the mechanisms of aging. “Understanding the mechanisms of aging and its intrinsic connections with disease holds profound social and biomedical implications,” said Professor Zhenyu Ju from Jinan University.  The research team notes that there are two related concepts that are the driving forces behind the aging process: cellular senescence and organismal aging. Organismal aging describes the process by which multicellular organisms decline in function over time. This decline involves multiple organ systems and includes the disruption of tissue homeostasis, along with the cumulative risk of age-associated diseases.  By contrast, cellular senescence describes the stable, irreversible cellular state where damaged or old cells permanently stop dividing but do not die. This cellular state is induced by specific stresses. Current studies show that cellular senescence represents one of the key cellular foundations of organismal aging and many age-related diseases. Cells undergo many multifaceted changes as they become senescent. Scientists have learned that progressive reorganization of higher-order chromatin structure is a pivotal regulatory layer. Chromatin is a mixture of DNA and proteins inside cells. They provide an organized storage system that folds the long strands of DNA so they fit inside the cells. This reorganization refers to the changes that occur over times as the DNA is folded into the nucleus.  The chromatin’s three-dimensional architecture goes through profound changes and dynamic remodeling as cellular senescence occurs and the cells age. Studies are showing that these architectural changes are closely related to key senescence-related changes that occur when the cells permanently stop dividing but remain active. These changes include genomic instability, transcriptional dysregulation, stem cell functional decline, and chronic inflammatory signaling. Scientists have recently begun to map patterns of three-dimensional genome remodeling and connect them to diseases and medical conditions that occur with aging. These range from Alzheimer’s disease to blood diseases to cancer.  “In recent years, advances in three-dimensional genome technologies have provided valuable tools for understanding the dynamic chromatin changes underlying aging and disease,” said Ju. Looking ahead, the team notes that the integration of single-cell technologies with dynamic three-dimensional genome mapping may enable the construction of systematic spatiotemporal models of chromatin evolution during aging. “This approach could not only help delineate causal relationships between chromatin remodeling and aging or disease phenotypes but also advance three-dimensional genomics from theoretical studies to clinical applications, offering novel strategies for aging intervention, cancer therapy, and the prevention of complex diseases,” said Ju. The research team includes Weicong Chen, Qiang Zhan, Feng Xiao, Zhenyu Ju, and Zhiyang Chen from the Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Aging and Regenerative Medicine, Department of Developmental & Regenerative Medicine, College of Life Science and Technology, Jinan University, Guangzhou, China. The research is funded by the National Key R&D Program of China, the National Natural Science Foundation of China, and the innovation team project of universities in Guangdong province. DOI Link:https://doi.org/10.26599/AGR.2025.9340072
Life Sciences and Medicine
Lifeline Emergency and Safety

Seismic performance of high-rise segmented rocking truss steel frame with multiple tuned mass dampers

High-rise buildings in seismically active regions face a critical engineering challenge: traditional seismic design approaches often struggle to control complex vibration patterns that can lead to catastrophic damage during major earthquakes. But the traditional rocking truss mainly controls the damage distribution of the structure according to the first-order vibration mode, which has limited applicability to high-rise structures. A research team led by Professor Zhi-Qian Dong at Dalian University of Technology has proposed a solution: a steel frame-segmented rocking truss-MTMD damping system. The system divides rocking structures into multi-segment trusses along the vertical direction, allowing deformation along multiple mode shapes while arranging MTMDs based on system vibration characteristics without increasing frame lateral stiffness. The research focused on a 35-story steel frame structure with a total height of 140 meters, analyzing single-segment, double-segment, and triple-segment rocking truss configurations under severe earthquake conditions. The team investigated critical parameters including segmentation number and position, lateral stiffness ratios between the frame and rocking truss, MTMD mass ratios and placement locations, and the effects of velocity-dependent dampers and self-centering energy-dissipative braces. The findings reveal that the segmented rocking truss system offers several distinct advantages over conventional designs. The segmentation effectively reduces the maximum bending moment in the middle of the rocking truss. By introducing hinged joints between segments, the system allows each rocking truss segment to deform along multiple mode shapes, significantly reducing internal forces while maintaining structural integrity. The integration of multiple tuned mass dampers arranged according to the system's vibration mode represents another innovation which can reduce the structure's maximum inter-story drift ratio by 7% to 29%. The researchers found that placing MTMDs at the top of the structure—where deformation of the first three vibration modes is largest—proves more effective than locating them at segment joints. With a mass ratio of just 1%, the MTMDs achieved an 8.5% reduction in maximum inter-story drift under certain ground motions, while a 4% mass ratio delivered average vibration reduction rates of 19.5% at the top. Additional energy dissipation is provided by velocity-dependent dampers, which the study found particularly effective. "Compared with self-centering energy dissipation braces, velocity-dependent dampers provide no additional stiffness and can dissipate seismic energy effectively," the researchers reported. Installation of these dampers reduced maximum inter-story drift ratios by 53% to 59% and decreased roof displacements by 24% to 37% under various earthquake scenarios. The research also explored variable stiffness designs for segmented rocking trusses, demonstrating that upper segments can use smaller cross-sections than lower segments without compromising seismic performance. This approach significantly reduces overall structural weight and steel consumption while maintaining effective lateral deformation control. The system's self-centering energy-dissipative braces, installed at each rocking truss segment base, further reduce residual structural deformation after earthquakes, facilitating faster post-earthquake recovery. This system is particularly well-suited for high-rise structures where higher-order modes play a significant role. Through incremental dynamic analysis (IDA) and vulnerability analysis, the results show the seismic performance of controlled structures is significantly better than that of uncontrolled structures. The segmented structural design effectively minimizes the impact of higher-order modes on internal forces and stress concentrations in the sway wall structure. Each sway wall segment can be designed and constructed independently based on its specific location and structural characteristics, making maintenance more convenient and flexible compared to a monolithic sway wall. The research team expects this innovative system to provide a practical and efficient solution for high-rise building seismic design, particularly in regions with high seismic risk. Future work will focus on optimizing the integration of these various damping mechanisms and further refining the vulnerability analysis of relevant influencing factors. The ultimate goal is to develop standardized design methodologies that can be readily implemented in engineering practice, making tall buildings more resilient against earthquakes while maintaining economic feasibility. Other contributors to this research include Hao Wu, Yi-Zhang Chai, Qing-Tao Meng from Dalian University of Technology, Hui-Dong Liu from China Construction Third Engineering Bureau Group, and Yi Zhao from Guangxi University and Rice University. This research was supported by the Open Project Program of Guangdong Provincial Key Laboratory of Intelligent Disaster Prevention and Emergency Technologies for Urban Lifeline Engineering, and the Opening Funds of State Key Laboratory of Building Safety and Built Environment & National Engineering Research Center of Building Technology. See the article: Seismic performance of high-rise segmented rocking truss steel frame with multiple tuned mass dampers
Physical Sciences and Engineering
iEnergy

Vortex-induced triboelectric nanogenerator for multidirectional wind energy harvesting enables efficient wind energy harvesting under low wind speed and high humidity

“We propose a vortex-induced vibration-based triboelectric nanogenerator (VIV-TENG) to enable efficient wind energy harvesting under low wind speed and complex environmental conditions,” says Prof. Chuyan Zhang from China University of Geosciences (Beijing). They published their study on May 13, 2026, in iEnergy. A new strategy for distributed wind energy harvesting With the growing demand for clean energy, harvesting distributed wind energy in urban environments has attracted increasing attention. However, conventional wind turbines are often limited by their large size, high cost, and strict installation requirements, making them unsuitable for small-scale and decentralized applications. Triboelectric nanogenerators (TENGs), pioneered by Wang Zhonglin, provide a promising alternative due to their advantages in low-frequency energy harvesting. Nevertheless, existing wind-driven TENG designs still face challenges such as low efficiency at low wind speeds, poor adaptability to multidirectional airflow, mechanical wear, and performance degradation in humid environments.  Vortex-induced vibration enables stable energy conversion To overcome these limitations, the research team developed a VIV-TENG that utilizes vortex-induced vibration instead of conventional rotational or fluttering mechanisms. When airflow passes through the device, periodic vortex shedding induces oscillations of the central axis, which drives multiple TENG units to generate electricity. “The vortex-induced vibration mechanism allows the device to operate without rotational components, reducing mechanical wear while maintaining stable output under multidirectional airflow,” the research team explains. The device features a symmetric structure with multiple TENG units arranged around a central axis, enabling efficient wind energy collection from various directions. In addition, an encapsulated design improves its environmental robustness.  Efficient output under low wind speed and high humidity The experimental results demonstrate that the VIV-TENG achieves stable output performance over a wide range of wind speeds. At a wind speed of 3.5 m/s, the device reaches a peak open-circuit voltage of 82.9 V and a short-circuit current of 13 μA. Notably, the device can operate at a low start-up wind speed of 0.9 m/s. It can also charge a 47 μF capacitor to 2 V within one minute. In addition, the device maintains stable performance in humid environments. The maximum average output power reaches 49.5 μW at 45% relative humidity and remains at 45.5 μW even at 85% humidity, demonstrating environmental adaptability. Toward self-powered systems in urban environments The device shows potential for practical applications. It can power multiple LEDs, drive small electronic devices such as clocks, and support self-powered sensing systems. The results suggest that the proposed device could serve as a feasible approach for distributed wind energy harvesting and self-powered systems. This study provides a possible pathway for utilizing low-speed wind energy in complex environments and indicates the potential of TENG-based technologies in urban energy systems. See the article: Vortex-driven triboelectric nanogenerator for multidirectional wind energy collection with humidity resistance
Information Sciences
Total 185