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Continuous monitoring of soil nitrate levels is essential for effective soil nutrient management. However, limited soil pore water at low soil water content levels hinders miniaturized soil sensor surfaces from efficiently interacting with nutrient ions. To address this, we introduce a nanofibrous mat designed to enhance nitrate detection by increasing connectivity between miniature sensors and the soil solution. Composed of polysulfone, polymethylmethacrylate, and polyvinyl alcohol, this mat is fabricated using electrospinning and electrospray methods to balance water absorbency, mechanical durability, and ease of manufacturing. When wrapped around an ion-selective electrode-based nitrate sensor, the mat improves access to soil pore water, acts as a filter, prevents direct sensor-soil particle contact, and reduces the impact of soil particle surface charges on sensor measurements. Continuous nitrate monitoring with both mat-wrapped and bare sensors was conducted in controlled and field environments. Linear regression analysis indicates that the mat-wrapped sensor has a stronger correlation with conventional salt extract methods for measuring soil nitrate levels. T-tests confirm statistically significant differences between sensor measurements and the salt extraction method. Additionally, Bland-Altman analysis reveals that mat-wrapping reduces mean bias and narrows the limits of agreement, demonstrating improved agreement with the conventional method. Notably, the mat-wrapped sensor performs consistently across varying soil moisture conditions. These findings suggest that the water-absorbing mat improves the ability of the sensor to monitor nitrate continuously by accommodating varying soil moisture levels over time, making the mat-wrapped soil nitrate sensor a viable improvement for in-field measurements of soil solution chemistry. 

• This paper studies the problem of structured 3D reconstruction using wireframes that consist of line segments and junctions, focusing on the computation of structured boundary geometries of scenes. Instead of leveraging matching-based solutions from 2D wireframes (or line segments) for 3D wireframe reconstruction as done in prior arts, we present NEAT, a rendering-distilling formulation using neural fields to represent 3D line segments with 2D observations, and bipartite matching for perceiving and dis- tilling of a sparse set of 3D global junctions. The proposed NEAT enjoys the joint optimization of the neural fields and the global junctions from scratch, using view-dependent 2D observations without precomputed cross-view feature matching. Comprehensive experiments on the DTU and BlendedMVS datasets demonstrate our NEAT’s superiority over state-of-the-art alternatives for 3D wireframe reconstruction. Moreover, the distilled 3D global junctions by NEAT, are a better initialization than SfM points, for the recently-emerged 3D Gaussian Splatting for high-fidelity novel view synthesis using about 20 times fewer initial 3D points. 

A plasmon-enhanced pyroelectric membrane was applied to control the current flow in a graphene transistor for light detection. The graphene transistor was built on a free-standing, 15-μm-thick PVDF membrane, which was doped using gold nanorods to facilitate its optical absorption. Under the resonant condition, the device exhibited a responsivity of 0.79 μA/mW.