Toward High-Performance Hydrogenation at Room Temperature Through Tailoring Nickel Catalysts Stable in Aqueous Solution

The development of highly active, reusable catalysts for aqueous-phase reactions is challenging. Herein, metallic nickel is encapsulated in a nitrogen-doped carbon-silica composite (SiO2@Ni@NC) as a catalyst for the selective hydrogenation of vanillin in aqueous media. The constructed catalyst achieved 99.8% vanillin conversion and 100% 4-hydroxymethyl-2-methoxyphenol selectivity at room temperature. Based on combined scanning transmission electron microscopy, X-ray photoelectron spectroscopy, and Raman analyses, the satisfactory catalytic performance is attributed to the composite structure consisting of an active metal, carbon, and silica. The hydrophilic silica core promoted dispersion of the catalyst in aqueous media. Moreover, the external hydrophobic NC layer has multiple functions, including preventing oxidation or leaching of the internal metal, acting as a reducing agent to reduce the internal metal, regulating the active-site microenvironment by enriching the concentrations of H2 and organic reactants, and modifying the electronic structure of the active metal via metal-support interactions. Density functional theory calculations indicated that NC facilitates vanillin adsorption and hydrogen dissociation to promote aqueous-phase hydrogenation. This study provides an efficient strategy for constructing encapsulated Ni-based amphiphilic catalysts to upgrade biomass-derived compounds.

Oxygen Vacancies on NH4 V4 O10 Accelerate Ion and Charge Transfer in Aqueous Zinc-Ion Batteries

Vanadium-based compounds are identified as promising cathode materials for aqueous zinc ion batteries due to their high specific capacity. However, the low intrinsic conductivity and sluggish Zn2+ diffusion kinetics seriously impede their further practical application. Here, oxygen vacancies on NH4 V4 O10 is reported as a high-performing cathode material for aqueous zinc ion batteries via a facile hydrothermal strategy. The introduction of oxygen vacancy accelerates the ion and charge transfer kinetics, reduces the diffusion barrier of zinc ions, and establishes a stable crystal structure during zinc ion (de-intercalation). As a result, the oxygen vacancy enriched NH4 V4 O10 exhibits a high specific capacity of ≈499 mA h g-1 at 0.2 A g-1 , an excellent rate capability of 296 mA h g-1 at 10 A g-1 and the specific capacity cycling stability with 95.1% retention at 5 A g-1 for 4000 cycles, superior to the NVO sample (186.4 mAh g-1 at 5 A g-1 , 66% capacity retention).

Temperature Compensation of Wind Tunnel Balance Signal Detection System Based on IGWO-ELM

The wind tunnel balance signal detection system is widely employed in aerospace applications for the accurate and automated measurement of aerodynamic forces and moments. However, measurement errors arise under different environmental temperature. This paper addresses the issue of measurement accuracy under different temperature conditions by proposing a temperature compensation method based on an improved gray wolf optimization (IGWO) algorithm and optimized extreme learning machine (ELM). The IGWO algorithm is enhanced by improving the initial population position, convergence factor, and iteration weights of the gray wolf optimization algorithm. Subsequently, the IGWO algorithm is employed to determine the optimal network parameters for the ELM. The calibration decoupling experiment and high-low temperature experiment are designed and carried out. On this basis, ELM, GWO-ELM, PSO-ELM, GWO-RBFNN and IGWO-ELM are used for temperature compensation experiments. The experimental results show that IGWO-ELM has a good temperature compensation effect, reducing the measurement error from 20%FS to within 0.04%FS. Consequently, the accuracy and stability of the wind tunnel balance signal detection system under different temperature environments are enhanced.

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy

Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer utilizing near-infrared spectroscopy (NIRS), thereby providing a technical foundation for the selection, screening, and breeding of rice germplasm cultivars characterized by a low cellulose content in the bran layer. To ensure the accuracy of the NIR spectroscopic analysis, the potassium dichromate oxidation (PDO) method was improved and then used as a reference method. Using 141 samples of rice bran layer (rice bran without germ), near-infrared diffuse reflectance (NIRdr) spectra, near-infrared diffuse transmittance (NIRdt) spectra, and fusion spectra of NIRdr and NIRdt were used to establish cellulose quantitative analysis models, followed by a comparative evaluation of these models' predictive performance. Results indicate that the optimized PDO method demonstrates superior precision compared to the original PDO method. Upon examining the established models, their predictive capabilities were ranked in the following order: the fusion model outperforms the NIRdt model, which in turn surpasses the NIRdr model. Of all the fusion models developed, the model exhibiting the highest predictive accuracy utilized fusion spectra (NIRdr-NIRdt (1st der)) derived from preprocessed (first derivative) diffuse reflectance and transmittance spectra. This model achieved an external predictive R2p of 0.903 and an RMSEP of 0.213%. Using this specific model, the rice mutant O2 was successfully identified, which displayed a cellulose content in the bran layer of 3.28%, representing a 0.86% decrease compared to the wild type (W7). The utilization of NIRS enables quantitative analysis of the cellulose content within the rice bran layer, thereby providing essential technical support for the selection of rice varieties characterized by lower cellulose content in the bran layer.

Structural Design and Simulation of a Multi-Channel and Dual Working Condition Wafer Defect Inspection Prototype

Detecting and classifying defects on unpatterned wafers is a key part of wafer front-end inspection. Defect inspection schemes vary depending on the type and location of the defects. In this paper, the structure of the prototype is designed to meet the requirements of wafer surface and edge defect inspection. This prototype has four inspection channels: scattering, reflection, phase, and contour, with two working conditions: surface and edge inspection. The key structure of the prototype was simulated using Ansys. The simulation results show that the maximum deformation of the optical detection subsystem is 19.5 μm and the fundamental frequency of the prototype is 96.9 Hz; thus, these results meet the requirements of optical performance stability and structural design. The experimental results show that the prototype meets the requirements of the inspection sensitivity better than 200 nm equivalent PSL spherical defects.

Self-Assembled Bifunctional Copper Hydroxide/Gold-Ordered Nanoarray Composites for Fast, Sensitive, and Recyclable SERS Detection of Hazardous Benzene Vapors

Volatile organic compounds (VOCs), particularly monoaromatic hydrocarbon compounds (MACHs), pose a potential risk to the atmospheric environment and human health. Therefore, the progressive development of efficient detection methodologies is a pertinent need, which is still a challenge at present. In this study, we present a rapid and sensitive method to detect trace amounts of MACHs using a bifunctional SERS composite substrate. We prepared an Au/SiO₂ enhanced layer and a porous Cu(OH)₂ adsorption layer via microfluidic-assisted gas-liquid interface self-assembly. The composite substrate effectively monitored changes in benzaldehyde using time-varying SERS spectra, and track-specifically identified various VOCs such as benzene, xylene, styrene, and nitrobenzene. In general, the substrate exhibited a rapid response time of 20 s to gaseous benzaldehyde, with a minimum detection concentration of less than 500 ppt. Further experimental assessments revealed an optimum Cu(OH)₂ thickness of the surrounding adsorption layer of 150 nm, which can achieve an efficient SERS response to MACHs. Furthermore, the recoverable and reusable property of the composite substrate highlights its practicality. This study presents a straightforward and efficient approach for detecting trace gaseous VOCs using SERS, with significant implications in the designing of SERS substrates for detecting other VOCs.