Leaf functional traits—chlorophyll (CHL), carotenoid (CAR), equivalent water thickness (EWT), nitrogen (N), and leaf mass per area (LMA)—are essential for monitoring crop health and ecosystem productivity.

A tandem neural network capable of inferring key physical parameters of semiconductor materials from simple transistor measurements has been developed, as reported by researchers from Institute of Science Tokyo. While conventional approaches for this type of analysis require hours or even days, the proposed system produces results in under one millisecond with near-perfect accuracy.

Objective: Cardiovascular diseases (CVDs), particularly coronary artery disease (CAD) and heart failure (HF), are leading causes of global mortality. Small-molecule metabolites serve as promising biomarkers for CAD and HF management, yet their low abundance in complex biological matrices poses significant detection challenges. Laser desorption/ionization mass spectrometry (LDI-MS) is a powerful analytical tool, but conventional organic matrices suffer from strong background interference and poor reproducibility. This review systematically summarizes recent advances in metal-based nanomaterials as alternative LDI-MS matrices for small-molecule biomarker detection in CAD and HF, focusing on composition and structural design strategies.

Methods: The authors reviewed and classified metal-based nanomaterials by composition (single-component systems including noble metals and metal oxides; multi-component systems including noble metal-based, metal oxide-based, and MOF-based composites) and structural design (size/morphology control, porous, core-shell, and other structures). Enhancement mechanisms and analytical performance were analyzed, and clinical applications in CAD (stable CAD, acute coronary syndrome, and myocardial infarction) and HF were reviewed.

Results: Compositional engineering enables synergistic effects including localized surface plasmon resonance, enhanced photothermal conversion, efficient charge transfer, and selective analyte enrichment. Structural design optimizes electromagnetic field distribution and provides abundant active sites. In clinical applications, nanomaterial-assisted LDI-MS combined with machine learning achieves excellent diagnostic performance for CAD and HF, including stable CAD screening, ACS subtyping, MI classification, and HF differentiation.

Conclusions: Metal-based nanomaterials significantly enhance LDI-MS performance for metabolite detection through rational design. The technology shows promise for cardiovascular metabolic profiling, but challenges remain regarding fundamental mechanisms, complex synthesis, and clinical translation. Standardized protocols, multicenter validation, and interdisciplinary collaboration are needed to advance clinical implementation.

Green hydrogen production through seawater electrolysis is a promising strategy, although challenges such as sluggish oxygen evolution reaction (OER) kinetics and chlorine (Cl⁻) corrosion hinder its practical applicability. A novel fluorine (F)-doped cobalt (Co) and iron (Fe) layered metal hydroxide (F-CoFe LMH-8) is developed as a robust bifunctional catalyst achieving 81.23 and 265.5 mV at 10 mA cm⁻² for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), respectively. Theoretical and experimental studies demonstrate that the F-doping modulates the electronic structure, effectively tuning Fe sites toward a high-spin configuration that optimizes binding energies and induces a chlorophobic effect that repel corrosive (Cl⁻) ions. Notably, the F-CoFe LMH-8( +|| −) bifunctional catalyst integrated anion exchange membrane water electrolyzer (AEMWE) exhibited outstanding performance for continuous H₂ production, achieves a current density of 1.2 A cm⁻² in 1 M KOH, 1.02 A cm⁻² in 1 M KOH + 0.5 M NaCl, and 1 A cm⁻² in 1 M KOH in seawater at 2.3 V. Furthermore, a long short-term memory-based machine learning model was employed to forecast and predict the stability of F-CoFe LMH-8. This approach provides a comprehensive pathway for heuristic design of durable, chlorophobic, and advanced electrocatalyst for seawater-based AEMWE and large-scale hydrogen production.