Hefei University

The dissociative chemisorption of water on a Cu/Ni(111) bimetallic alloy surface was investigated using a combined neural-network potential energy surface and quantum dynamics approach. A full-dimensional (9D) PES was constructed and validated, enabling efficient seven-dimensional (7D) quantum wave packet calculations. Approximate 9D dissociation probabilities were obtained by site-averaging the 7D, site-specific results. The Cu monolayer is under 3.2% compressive strain, leading to a higher barrier height of 1.20eV on Cu/Ni(111) than on pure Cu(111) (1.08eV) and, consequently, to lower dissociation probabilities. The more reactive subsurface Ni atom induces a distinct site reactivity order (hcp > fcc > bridge > top). Strong mode specificity was observed, where vibrational excitations of the symmetric stretching, asymmetric stretching, and bending modes of H2O were found to be more efficacious than increasing the translational energy in promoting the reaction, with the asymmetric stretching mode providing the greatest enhancement and the bending mode the smallest. This mode-specific behavior aligns with earlier findings for water dissociation on pure Cu(111) and Ni(111) surfaces.

With the widespread application of automatic fire alarm systems in various types of buildings, the problem of fire false alarms has gradually become prominent, which not only causes resource waste, but also may reduce users’ trust in the alarm system, thereby affecting the efficiency of emergency response in actual fires. According to data from a certain fire cloud platform, 99.85% of the suspected fires predicted by its system are false alarms. Although existing models can recognize most fire accidents, the accuracy of fire alarm recognition is only 0.15%, due to loose judgment logic, which still requires a large amount of manpower to verify alarms. This article analyzes a large amount of false alarm data and explores the main causes of false alarms, including environmental interference, equipment failure, and improper human operation. By using a fire dynamics simulator (FDS) to establish fire simulation models under different data settings, horizontal and vertical multi-scene fire simulation data are obtained. The study combines simulation and platform data to form a fire and false alarm dataset using a one-dimensional convolutional neural network (1D-CNN) and deep neural network (DNN) deep learning techniques to learn the deductive rules of the fire scene, establish a two-stage judgment model, and gradually, accurately, judge the results. By quantifying the precision, recall, and F1 score of the model, a deep learning model designed to accurately identify genuine fire alarms while filtering out false ones is proposed that can significantly reduce the false alarm rate. The results indicate that the model can identify 1705 false alarms out of 2255 highly suspected true alarms identified by existing systems in multiple practical scenarios and eliminate 75.61% of false positive alarms. On the premise of ensuring an authenticity recognition rate greater than 98%, the accuracy of fire alarm recognition increased from 0.15% to 28.85%, which will significantly reduce the workload of staff verifying alerts, and has good practical value.

The missing first Shapiro step in microwave-irradiated Josephson junctions has been widely interpreted as a hallmark of Majorana bound states. However, conventional mechanisms like junction underdamping or Joule heating can produce similar signatures. Here, we demonstrate that the intrinsic non-linear current-voltage characteristic of low-to-moderate transparency junctions can also suppress the first step, accompanied by distinctive zigzag boundaries between the zeroth and first step at intermediate driving frequencies. Microwave measurements on Al/WTe2 junctions and numerical simulations of a non-linear resistively and capacitively shunted junction model reveal the first-step collapse induced by switching jumps of current, together with zigzag features absent in scenarios solely driven by finite $$\beta$$ or Joule heating. This zigzag signature, therefore, provides a crucial diagnostic tool, emphasizing the necessity of comprehensive analysis of microwave spectra before attributing the absence of the first Shapiro step to Majorana physics. The absence of odd Shapiro steps in microwave-irradiated Josephson junctions (JJs) is considered to be a possible indicator of 4π-periodic supercurrents that are induced by Majorana bound states. Here, by conducting measurements on Al/WTe2 JJs, the authors suggest that the missing first Shapiro step can instead arise from the intrinsic non-linearity of the current–voltage characteristics in low-to-moderate transparency junctions.