A high sensitive methane (CH₄) sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) using self-designed trapezoidal-head quartz tuning fork (QTF) and high power diode laser is reported for the first time in this paper. The trapezoidal-head QTF with low resonant frequency (<i>f</i> _<i>0</i> ) of ∼ 9 kHz, serves as the detection element, enabling longer energy accumulation times. A diode laser with an output power of 10 mW is utilized as the excitation source. A Raman fiber amplif…

Reducing noble metal dependence in oxygen evolution reaction (OER) catalysts is essential for achieving sustainable and scalable green hydrogen production. Bimetallic oxides, with their potential for high catalytic performance and reduced noble metal content, represent promising alternatives to traditional IrO 2 -based OER catalysts. However, optimizing these materials remains challenging due to the complex interplay of elemental selection, composition, and chemical ordering. In this study, we integrate density fu…

Abstract The low energy density and sluggish Na + diffusion in vanadium‐based polyanion cathodes have impeded further applications. Currently, a facile hydrothermal method to prepare an innovative Na 3 V 2 (PO 4 ) 3 /NaV(P 2 O 7 ) homogeneous cathode with columnar cluster morphology is proposed. In situ high‐temperature XRD reveals the reaction generation mechanism of the heterostructure, where konjac glucomannan (KGM) undergoes oxidative decomposition, resulting in a decrease in the oxygen content to generate (P …

In order to enhance the management and application of fault knowledge within intelligent production lines, thereby increasing the efficiency of fault diagnosis and ensuring the stable and reliable operation of these systems, we propose a fault diagnosis methodology that leverages knowledge graphs. First, we designed an ontology model for fault knowledge by integrating textual features from various components of the production line with expert insights. Second, we employed the ALBERT–BiLSTM–Attention–CRF model to a…

The rapid evolution of intelligent logistics has driven the need for efficient and adaptive warehouse scheduling solutions, particularly in dynamic and uncertain environments. Traditional scheduling methods, including rule-based heuristics and mathematical optimization models, struggle to handle real-time variability and computational complexity, limiting their practical deployment. These conventional approaches often fail to integrate real-time data and adapt to changing supply chain conditions, leading to ineffi…