Enzymatic Activation and Continuous Electrochemical Production of Methane from Dilute CO2 Sources with a Self-Healing Capsule.

Converting dilute CO2 source into value-added chemicals and fuels is a promising route to reduce fossil fuel consumption and greenhouse gas emission, but integrating electrocatalysis with CO2 capture still faced marked challenges. Herein, we show that a self-healing metal-organic macrocycle functionalized as an electrochemical catalyst to selectively produce methane from flue gas and air with the lowest applied potential so far (0.06 V vs reversible hydrogen electrode, RHE) through an enzymatic activation fashion. The capsule emulates the enzyme' pocket to abstract one in situ-formed CO2-adduct molecule with the commercial amino alcohols, forming an easy-to-reduce substrate-involving clathrate to combine the CO2 capture with electroreduction for a thorough CO2 reduction. We find that the self-healing system exhibited enzymatic kinetics for the first time with the Michaelis-Menten mechanism in the electrochemical reduction of CO2 and maintained a methane Faraday efficiency (FE) of 74.24% with a selectivity of over 99% for continuous operation over 200 h. A consecutive working lab at 50 mA·cm-2, in an eleven-for-one (10 h working and 1 h healing) electrolysis manner, gives a methane turnover number (TON) of more than 10,000 within 100 h. The integrated electrolysis with CO2 capture facilitates the thorough reduction of flue gas (ca. 13.0% of CO2) and first time of air (ca. 400 ppm of CO2 to 42.7 mL CH4 from 1.0 m3 air). The new self-healing strategy of molecular electrocatalyst with an enzymatic activation manner and anodic shifting of the applied potentials provided a departure from the existing electrochemical catalytic techniques.

BSI-MVS: multi-view stereo network with bidirectional semantic information.

The basic principle of multi-view stereo (MVS) is to perform 3D reconstruction by extracting depth information from multiple views. Most current SOTA MVS networks are based on Vision Transformer, which usually means expensive computational complexity. To reduce computational complexity and improve depth map accuracy, we propose a MVS network with Bidirectional Semantic Information (BSI-MVS). Firstly, we design a Multi-Level Spatial Pyramid module to generate multiple layers of feature map for extracting multi-scale information. Then we propose a 2D Bidirectional-LSTM module to capture bidirectional semantic information at different time steps in the horizontal and vertical directions, which contains abundant depth information. Finally, cost volumes are built based on various levels of feature maps to optimize the final depth map. We experiment on the DTU and BlendedMVS datasets. The result shows that our network, in terms of overall metrics, surpasses TransMVSNet, CasMVSNet, CVP-MVSNet, and AACVP-MVSNet respectively by 17.84%, 36.42%, 14.96%, and 4.86%, which also shows a noticeable performance enhancement in objective metrics and visualizations.