In this paper, the galvanic effect of pyrite and arsenopyrite during the leaching pretreatment of gold ores was determined with the use of electrochemical testing (open circuit potential, linear sweep voltammetry, Tafel, and electrochemical impedance spectroscopy (EIS)) and frontier orbit calculations. The results show that (i) the linear sweep voltammetry curve and Tafel curve of the galvanic pair are similar to those of arsenopyrite, (ii) the corrosion behavior of the galvanic pair is consistent with that of arsenopyrite, and (iii) the galvanic effect promotes the corrosion of arsenopyrite by simultaneously increasing the cathode and anode currents and reducing oxidation resistance. The frontier orbit calculation explains the principle of the galvanic effect of pyrite and arsenopyrite from the view of quantum mechanics.

Microplastics are a pervasive pollutant in aquatic ecosystems, raising critical environmental and public health concerns and driving the need for advanced detection technologies. Microscopic hyperspectral imaging (micro-HSI), known for its ability to simultaneously capture spatial and spectral information, has shown promise in microplastic analysis. However, its widespread application is hindered by limitations such as low signal-to-noise ratios (SNR) and reduced sensitivity to smaller microplastic particles. To address these challenges, this study investigates the use of Ag nanoarrays as reflective substrates for micro-HSI. The localized surface plasmon resonance (LSPR) effect of Ag nanoarrays enhances spectral resolution by suppressing background reflections and isolating microplastic reflection bands from interference. This improvement results in significantly increased SNR and more distinct spectral features. When analyzed using a 3D-2D convolutional neural network (3D-2D CNN), the integration of Ag nanoarrays improved classification accuracy from 90.17% to 98.98%. These enhancements were further validated through Support Vector Machine (SVM) analyses, demonstrating the robustness and reliability of the proposed approach. This study demonstrates the potential of combining Ag nanoarrays with 3D-2D CNN models to enhance micro-HSI performance, offering a novel and effective solution for precise microplastics detection and advancing chemical analysis, environmental monitoring, and related fields.

The primary interface between titanium alloy bone implants and bone is mainly composed of a titanium dioxide/hydroxyapatite interface. However, anatase phase titanium dioxide/hydroxyapatite interfaces have been less extensively studied. This paper employs density functional theory (DFT) to investigate the anatase/hydroxyapatite (HA) interface. Bonding behavior between the interface atoms is characterized using bond length, density of states (DOS), and electron density metrics. Based on this foundation, this paper proposes two types of cation-centered bonding networks: (1) a bonding network centered on Ca ions, and (2) a bonding network centered on Ti and P ions. Using the charge variations before and after the formation of surfaces and interfaces, this study characterizes the active and inert bonding sites. Calcium (Ca) is identified as the most valuable cationic active site at the interface. At the interface, the active sites in one phase precisely form bonds with the inactive sites in the other phase. Utilizing these insights, researchers and engineers can develop bone implants with improved mechanical properties and biocompatibility, leading to better patient outcomes in orthopedic and dental surgeries.

Introduction: Parkinson’s disease (PD) lacks reliable non-invasive diagnostic biomarkers. Urine is a promising biofluid for biomarker discovery, but the profound influence of shared environment and lifestyle represents a major confounder. Methods: To rigorously address this, we designed a pilot study using a unique matched-pair cohort: PD patients together with their healthy spouses. Untargeted LC–MS metabolomics was performed on urine samples from 15 carefully matched pairs. Differential features were identified using VIP > 1.0 and p < 0.05. A multi-model consensus strategy (RF, SVM, PLS-DA) was applied to prioritize robust candidates from 2,640 annotated metabolites, followed by filtering for pharmacological relevance and collinearity. Results: A preliminary five-metabolite panel (Cyanuric acid, Benzeneacetonitrile, 3-Formylsalicylic Acid, dADP, and ent-cassa-12,15-dien-2beta-ol) was defined. Despite the inherently small sample size of this niche cohort, the panel demonstrated promising internal discriminative performance (AUC > 0.95). Discussion: We emphasize that these results are exploratory. The primary contribution of this pilot study is not a validated diagnostic tool, but the demonstration of a carefully controlled design to isolate PD-specific metabolic signatures and the proposal of specific candidate biomarkers. This work establishes a critical proof-of-concept and prioritizes targets for essential future validation in larger, independent cohorts.