Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Nanotechnology
DFT and machine learning investigation of Au/Pt-decorated SnS2 monolayers for asthma and COPD diagnosis
Asthma and chronic obstructive pulmonary disease (COPD) are among the most prevalent chronic respiratory diseases worldwide, affecting hundreds of millions of people and contributing significantly to global morbidity and mortality. This work introduces a novel Au/Pt-decorated SnS2heterostructure for exhaled NO2detection, representing the new study to explore its role in lung disease diagnostics. It demonstrates ppb level NO2detection, a key biomarker for asthma and COPD, enabling early and differentiation of lung conditions by providing quantitative analysis of trace-level gases, which are often elevated in inflamed airways. While two-dimensional (2D) SnS2offers strong potential as a sensing platform, prior studies relied mainly on density functional theory (DFT) based gas sensing. Here, we present unprecedented integration of DFT and machine learning (ML) to investigate the gas sensing performance of pristine and Au/Pt-decorated SnS2monolayers. DFT analysis revealed enhanced adsorption and charge transfer upon noble-metal decoration, with Pt-SnS2showing optimal characteristics for asthma and COPD detection. Five ML models were trained on DFT and experimental-derived descriptors to rapidly predict the sensing behaviour of multiple gases, including NO2, among which XGBoost achievingR2= 0.9961. Both ML and DFT methods consistently identified NO2as the most sensitive analyte. This novel DFT-ML synergy not only validates fundamental adsorption mechanisms but also provides a scalable pathway for accelerated screening and design of high-performance gas sensors. Our findings establish a new prototype for integrating ML with first-principles simulations in the design of next-generation 2D material-based sensing devices.
24 July 2026
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Multidimensional additive manufacturing micro/nanorobots: from elaborate design to smart cargo delivery
Micro/nanorobots have attracted much attention because of their potential to perform complex tasks with high precision under controlled actuation within the human body, such as targeted cargo delivery, lesion exploration, and minimally invasive surgery. Among the various emerging fabrication technologies, multidimensional additive manufacturing (MAM) technology can enable the design and fabrication of complex structures with multifunctional characteristics. Compared with traditional manufacturing methods, MAM significantly reduces production complexity and time while enhancing design flexibility and customization. This review provides a comprehensive overview of MAM technologies for constructing micro/nanorobots, along with their applications and associated challenges in the biomedical field. In addition, emerging MAM approaches, including 4D, 5D, and 6D printing assisted by physical intelligence, machine learning, and artificial intelligence show great potential for designing and fabricating more sophisticated and intelligent micro/nanorobots, thereby advancing their clinical translation in the near future.
8 July 2026
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