High-performance porous copper (Cu)-based electrochemical sensors could potentially be achieved using GHz-burst femtosecond laser sintering, which allows enhanced control over the oxidation state and surface area. In this study, porous Cu-based electrodes were fabricated by GHz-burst femtosecond laser sintering and their electrochemical performances were evaluated in the non-enzymatic detection of D-glucose. Electrodes were produced under different intra-pulse numbers (Nint = 1, 2, 5, and 10), allowing control over both the extent of oxidation and the phase composition. At higher intra-pulse numbers, oxidation was effectively suppressed because of the reduced pulse intensity, whereas lower intra-pulse numbers promoted copper(I) oxide (Cu2O) formation through excessive heating. Electrochemical studies revealed that the Cu2O-containing electrode fabricated in the non-burst mode (Nint = 1) exhibited superior catalytic activity, demonstrating enhanced sensitivity toward D-glucose compared with that achieved using Cu-rich electrodes. These results confirm that GHz-burst femtosecond laser sintering provides precise control over the Cu phases and porosity, offering a promising strategy for developing efficient non-enzymatic glucose sensors.
In this work, a silica-based adsorbent incorporating two types of extractants was synthesized through vacuum-assisted impregnation. The synergistic adsorption behaviors of the prepared (TEHTDGA + TAMIA-EH)/SiO2–P composite toward 14 types of representative metal ions in simulated high-level liquid waste were systematically investigated under different contact durations, nitric acid concentrations, and solution temperatures, revealing the strong affinity of the (TEHTDGA + TAMIA-EH)/SiO2–P composite toward Pd(II), Ru(III), Re(VII), Mo(VI), and Zr(IV). Particularly, Pd(II) rapidly adsorbed on the composite, reaching equilibrium within 2h, with a removal efficiency exceeding 95
The estimation of nanoscale wear is crucial for comprehending the failure mechanisms of mechanical components, particularly in biomedical applications where wear-induced damage at the implant interface result in aseptic loosening and failure. Although recent wear models effectively estimate wear coefficients, they exhibit limitations in accounting for the inelastic deformation and diffusion at the interlayer. A comprehensive understanding of interlayer formation and its impact is essential for elucidating the interplay between mechanical and chemical effects in fretting wear behavior, which is vital for enhancing the longevity mechanical components and implants. This study aims to examine the cyclic wear behavior of Ti in contact with a HAp surface under normal load by integrating mechanical and chemical influences. Three loading cycles, including the approach-retraction process of Ti spheres on the HAp surface, were simulated using Molecular Dynamics (MD) simulations with a reactive force field and the charge equilibrium method. The predominance of charge migration and the interactive effects of mechanical diffusion and charge migration on the local wear behavior were examined by analyzing the influence of temperature and charge variation on the Ti wear rate. Heterogeneous cyclic wear behavior was observed, with severe wear activation during contact formation followed by reduced wear owing to the charged Ti interlayer. This finding underscores the role of the interlayer in enhancing the wear resistance, emphasizing the necessity of incorporating surface chemistry and mechanical deformation in predictive wear modeling.
This study presented a hybrid machine learning (ML) approach for predicting the small-strain shear modulus (Gmax) in granular soils that integrated AdaBoost, Decision Tree, and CatBoost models with the Gorilla Troops Optimization algorithm to improve predictive accuracy and model robustness. The approach addressed key limitations of conventional empirical models and standalone ML models in capturing complex parameter interactions across varying soil conditions. A database of 816 samples was compiled using four key soil parameters: void ratio, confining pressure, coefficient of uniformity, and particle shape descriptor. Among the developed models, CatBoost outperformed the other ML models and empirical correlations available in the literature for Gmax estimation, achieving coefficient of determination (R²) values of 0.986 (training) and 0.994 (testing) with minimal associated errors. To enhance model interpretability and transparency, Shapley additive explanations, partial dependence plots, and individual conditional expectation analyses were applied. The results showed that confining pressure was the most influential predictor, while the particle shape descriptor had the least effect on Gmax. The proposed approach provides a reliable, interpretable tool for engineers, supporting more accurate Gmax estimation and reducing uncertainty in geotechnical design.
Headache disorders cause work productivity and activity impairment (WPAI). There are two distinct types of burden caused by headache disorders: interictal burden measured by Migraine Interictal Burden Scale-4 (MIBS-4) and ictal burden measured by Headache Impact Test-6 (HIT-6). However, the impact of interictal burden on WPAI remains unclear. This study aimed to investigate whether MIBS-4 score (interictal burden) is associated with WPAI among individuals with headache disorders, in contrast to HIT-6 score (ictal burden). We conducted a school-based online survey of students’ parents in Tsubame City, Japan, in 2024. The questionnaire included age, sex, headache characteristics, MIBS-4 and HIT-6 scores, and overall work productivity impairment (OWPI) assessed using the WPAI questionnaire. A structural equation model (SEM) evaluated the effects of MIBS-4 and HIT-6 scores on OWPI. The headache diagnosis was solely based on the questionnaire. Among 5,227 households, 21.6
