The aim of this review is to gather current researches into sensors based on noble metal and semiconductor nanomaterials in biomedical detection and elucidate the basic principle and applications of different sorts of semiconductor nanomaterials, i.e., metal oxide NPs, transition metal dichalcogenides (TMDs), metal-organic frameworks (MOFs) and magnetic metal oxide (MMO) NPs. Based on the classifications of nano-biosensors, they can be summarized as electrochemical nano-biosensors, optical nano-biosensors, calorimetric nano-biosensors, and piezoelectric nano-biosensors, wherein, electrochemical and optical nano-biosensors acting as most popular study objects are focused on to excavate the great improvements in excellent sensitivity, selectivity and stability based on fabrication techniques.

In complex agricultural settings, accurately and rapidly identifying the growth and health conditions of strawberries remains a formidable challenge. Therefore, this study aims to develop a deep framework, Disease-YOLO (D-YOLO), based on the YOLOv8s model to monitor the health status of strawberries. Key innovations include (1) replacing the original backbone with MobileNetv3 to optimize computational efficiency; (2) implementing a Bidirectional Feature Pyramid Network for enhanced multi-scale feature fusion; (3) integrating Contextual Transformer attention modules in the neck network to improve lesion localization; and (4) adopting weighted intersection over union loss to address class imbalance. Evaluated on our custom strawberry disease dataset containing 1301 annotated images across three fruit development stages and five plant health states, D-YOLO achieved 89.6% mAP on the train set and 90.5% mAP on the test set while reducing parameters by 72.0% and floating-point operations by 75.1% compared to baseline YOLOv8s. The framework’s balanced performance and computational efficiency surpass conventional models including Faster R-CNN, RetinaNet, YOLOv5s, YOLOv6s, and YOLOv8s in comparative trials. Cross-domain validation on a maize disease dataset demonstrated D-YOLO’s superior generalization with 94.5% mAP, outperforming YOLOv8 by 0.6%. The framework’s balanced performance (89.6% training mAP) and computational efficiency surpass conventional models, including Faster R-CNN, RetinaNet, YOLOv5s, YOLOv6s, and YOLOv8s, in comparative trials. This lightweight solution enables precise, real-time crop health monitoring. The proposed architectural improvements provide a practical paradigm for intelligent disease detection in precision agriculture.

Sterile medical masks are essential in preventing infectious diseases. However, the ethylene oxide contained within these masks is a class I carcinogen. The standard method for measuring ethylene oxide is gas chromatography-mass spectrometry, which is not fit with the dynamic process of human inhalation. Thus, the amount of ethylene oxide volatilized from masks and inhaled by users is unknown. In this work, ethylene oxide was detected by using proton-transfer-reaction mass spectrometry, which can measure volatile quantities in milliseconds. We found that ethylene oxide was volatilized from masks during use. Within the first minute, the ethylene oxide concentration decreased by 84.65%, and then the rate of reduction gradually slowed. After 5 min, all ethylene oxide was effectively volatilized, and the average mass of ethylene oxide inhaled was 299.02 μg. We investigated three methods to reduce the concentration of ethylene oxide in masks before use: natural airing, shaking the mask, and blowing the mask with a hair dryer. The hair dryer method produced the best results: the ethylene oxide concentration decreased by 88.3% after only 10 s. The natural airing method was the least effective: the ethylene oxide concentration decreased by 60.7% even after 3 h.

Greenhouse gas emissions are changing the climate and affecting human activities. In cities, the anaerobic environment in sewage inspection wells produces CH₄, which is exchanged with the atmosphere and causes pollution. Moreover, although the number of inspection wells has increased with the development of cities, people have not paid enough attention to this potential source of CH₄ and ignored it in the statistics of greenhouse gas inventories. Conventional gas monitoring methods like gas chromatography are complex and expensive. Based on the portable TDLAS CH₄ sensor developed by our team, combined with a gas velocity analyser, we realised in situ continuous flux monitoring. We corrected the effect of temperature on the results according to the theory of infrared thermometry. We showed that the measurement results of the sensor fluctuate within the range of ±0.1 ppm of the measured standard gas concentration. We also verified its repeatability and ensured its reliability in field applications by comparing its results with the results of gas chromatography analysis. In addition to flux monitoring, based on the monitoring data of 56 inspection wells in the study area, the average concentration was estimated using the Bootstrap method, and combined with the average value of gas velocity, the average flux was estimated to be 2.19 × 10⁻⁶ mol/s, and the daily exchange mass was 3.03 g CH₄ d⁻¹. Combined with information such as the length of sewage pipes, we estimate that the annual CH₄ exchange mass in the city is about 5.49 × 10⁵ kg CH₄ yr⁻¹. This monitoring method will help us to understand climate change and improve greenhouse gas inventories.

Ca and Mg are key constituents in surface water that are essential nutrients and vital indicators of water hardness. Rapid on-site measurement of Ca and Mg concentrations in surface water is important. However, traditional laboratory detection methods are complex and time-consuming, and on-site detection is difficult. In this study, a portable surface water detection method was developed using laser-induced breakdown spectroscopy with a miniaturized spectrometer LIBS and a liquid jet device for sample introduction. The device enables the rapid online in situ measurement of elemental concentrations in the water. The limits of detection for the rapid on-site detection of Ca and Mg in surface water were 11.58 and 2.57 mg/L, respectively. We applied this method to assess the concentrations of Ca and Mg in authentic water samples collected from rivers and ponds. The recovery rates for Ca and Mg were 90.83–101.74% and 93.43–108.74%, respectively. This method is suitable for rapid, on-site, and highly sensitive monitoring of Ca and Mg concentrations in the environment.