Electrochemical conversion of nitrate (NO 3 − ) into ammonia (NH 3 ) recycles nitrogen and offers a route to the production of NH 3 , which is more valuable than dinitrogen gas. However, today's development of NO 3 − electroreduction remains hindered by the lack of a mechanistic picture of how catalyst structure may be tuned to enhance catalytic activity. Here we demonstrate enhanced NO 3 − reduction reaction (NO 3 − RR) performance on Cu 50 Ni 50 alloy catalysts, including a 0.12 V upshift in the half-wave potential and a 6-fold increase in activity compared to those obtained with pure Cu at 0 V vs reversible hydrogen electrode (RHE). Ni alloying enables tuning of the Cu d-band center and modulates the adsorption energies of intermediates such as *NO 3 − , *NO 2 , and *NH 2 . Using density functional theory calculations, we identify a NO 3 − RR-to-NH 3 pathway and offer an adsorption energy−activity relationship for the CuNi alloy system. This correlation between catalyst electronic structure and NO 3 − RR activity offers a design platform for further development of NO 3 − RR catalysts.

The current industrial ammonia synthesis relies on Haber–Bosch process that is initiated by the dissociative mechanism, in which the adsorbed N2 dissociates directly, and thus is limited by Brønsted–Evans–Polanyi (BEP) relation. Here we propose a new strategy that an anchored Fe3 cluster on the θ-Al2O3(010) surface as a heterogeneous catalyst for ammonia synthesis from first-principles theoretical study and microkinetic analysis. We have studied the whole catalytic mechanism for conversion of N2 to NH3 on Fe3/θ-Al2O3(010), and find that an associative mechanism, in which the adsorbed N2 is first hydrogenated to NNH, dominates over the dissociative mechanism, which we attribute to the large spin polarization, low oxidation state of iron, and multi-step redox capability of Fe3 cluster. The associative mechanism liberates the turnover frequency (TOF) for ammonia production from the limitation due to the BEP relation, and the calculated TOF on Fe3/θ-Al2O3(010) is comparable to Ru B5 site.

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Summary Signal transmission loss of using wireless sensors for structural health monitoring is a usual case, which undermines the reliability of the sensors for monitoring the structural conditions. The measured vibration data with a high data loss ratio can hardly be used for the analysis, that is, modal identification, as it will lead to significant errors in the results. This paper proposes a novel approach based on convolutional neural networks for recovering the lost vibration data for structural health monitoring. The used network is a fully feed‐forward convolutional neural network with bottleneck architecture and skip connection, which constructs the nonlinear relationships between the incomplete signal with data loss measured from the sensors with the transmission loss and the complete true signal. The trained network extracts the robust higher representation features of the measured incomplete signals using the compression layers and expands those features gradually throughout the reconstruction layers to recover and obtain the complete true signals. The long‐term vibration data from Dowling Hall Footbridge are employed to validate the effectiveness and robustness of the proposed approach for the lost data recovery. Two case studies are conducted to validate the recovery accuracy for single‐channel and multiple‐channel cases, respectively. The effect of sampling rate on the recovery accuracy is also investigated. The proposed approach exhibits the outstanding capability of lost data recovery, even when the signals have severe data loss ratios up to 90%. To further demonstrate the reliability of the recovered signals for data analysis, modal identification results by using the recovered signals with different data loss ratios show a very good agreement with those obtained from the complete true data.

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