Soil nitrate nitrogen (NO₃⁻-N) is a key indicator of agricultural non-point source pollution. The ultraviolet (UV) dual-wavelength method is widely used for NO₃⁻-N detection, but interference from complex soil organic matter affects its accuracy. This study investigated how organic matter influences NO₃⁻-N detection by optimizing UV dual-wavelength combinations. Density functional theory (DFT) calculations showed slight spectral broadening of fulvic and humic acids in the presence of NO₃⁻-N under UV spectrum. Standard solutions and soil samples were used to compare the detection performance of different wavelength pairs. The findings indicated that the dual-wavelength combination of 235 nm/275 nm is optimal rather than 220 nm/275 nm for measuring soil samples at NO₃⁻-N concentrations exceeding 5 mg·L⁻¹. The 235/275 nm method gave an average calibration coefficient of 1.57. Compared to the national standard and flow analysis methods, the average relative errors were 19.7% and 22.3% (p < 0.001), respectively, indicating its suitability for practical soil applications. These results demonstrate the method’s potential for rapid and accurate NO₃⁻-N detection in real soil samples, supporting its application in environmental monitoring and agricultural management.

Plant wearable sensors (PWSs) can obtain weak plant physiological information under complex agricultural environment in real time and multiple points, offering powerful tools for smart agriculture. However, research on PWSs started relatively late and is strongly interdisciplinary, leaving their development and application still facing challenges. It is therefore necessary to review and trace the evolution of PWSs to clarify their technological trajectory, highlight current progress, and recognize remaining challenges. Herein, PWSs are systematically discussed in terms of their development roadmap, categories, materials, energy-supply technologies, fabrication techniques, and applications. Furthermore, multiscale modeling, such as density functional theory, finite element analysis, and machine learning respectively provides insights from the atomic scale in charge transfer and interfacial interactions, the macro-meso scale in mechanical adaptability and structural reliability, and efficient data analysis, thus advancing the rational design and practical deployment of PWSs. Finally, a systematically curated table, complemented by a comparative radar chart analysis, provides an overview of reported devices by summarizing their key attributes while elucidating strengths and trade-offs for specific applications. This review not only provides guidance for proposing future design strategies and research directions, but also serves as a roadmap for advanced PWSs and facilitating their translation from laboratory research to applications.

Dissociation of the representative Li ₂ S ₆ in DMSO is proposed, and light promotes the dissociation of the dominant into .

Foodborne disease presents a substantial challenge to researchers, as foliar water intake greatly influences pathogen internalization via stomata. Comprehending plant–pathogen interactions, especially under fluctuating humidity and temperature circumstances, is crucial for formulating ways to prevent pathogen ingress and diminish foodborne hazards. This study introduces a computational model utilizing neural networks to anticipate pathogen internalization via stomata, contrasting with previous research that emphasized biocontrol techniques. Computational modeling assesses the likelihood and duration of internalization for bacterial pathogens such as Salmonella enterica (S. enterica), considering various environmental factors including humidity and temperature. The estimated likelihood ranges from 0.6200 to 0.8820, while the internalization time varies from 4000 s to 5080 s, assessed at 50% and 100% humidity levels. The difference in internalization time, roughly 1042.73 s shorter at 100% humidity, correlates with a 26.2% increase in the likelihood of internalization, rising from 0.6200 to 0.8820. A neural network model has been developed to quantitatively predict these values, thereby enhancing the understanding of plant–microbe interactions. These methods will aid researchers in understanding plant–pathogen interactions, especially in environments characterized by varying humidity and temperature and are essential for formulating strategies to prevent pathogen ingress and tackle foodborne illnesses within a technologically advanced context.