Pulsed electrolysis of carbon dioxide by large-scale solid oxide electrolytic cells for intermittent renewable energy storage

CO_(2) electrolysis with solid oxide electrolytic cells(SOECs)using intermittently available renewable energy has potential applications for carbon neutrality and energy storage.In this study,a pulsed current strategy is used to replicate intermittent energy availability,and the stability and conversion rate of the cyclic operation by a large-scale flat-tube SOEC are studied.One hundred cycles under pulsed current ranging from -100 to -300 mA/cm^(2) with a total operating time of about 800 h were carried out.The results show that after 100 cycles,the cell voltage attenuates by 0.041%/cycle in the high current stage of−300 mA/cm^(2),indicating that the lifetime of the cell can reach up to about 500 cycles.The total CO_(2) conversion rate reached 52%,which is close to the theoretical value of 54.3% at -300 mA/cm^(2),and the calculated efficiency approached 98.2%,assuming heat recycling.This study illustrates the significant advantages of SOEC in efficient electrochemical energy conversion,carbon emission mitigation,and seasonal energy storage.

CoLM^(2)S:Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information

Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.

Dynamic-Change Laws of the Porosity and Permeability of Low-to Medium-Rank Coals under Heating and Pressurization Treatments in the Eastern Junggar Basin,China

Deep coalbed methane exists in high-temperature and high-pressure reservoirs. To elucidate the dynamic-change laws of the deep coal reservoir porosity and permeability characteristics in the process of coalbed methane production, based on three pieces of low- to medium-rank coal samples in the eastern Junggar Basin, Xinjiang, we analyse their mercury-injection pore structures. We measured the porosity and permeability of the coal samples at various temperatures and confining pressures by high-temperature and confining pressure testing. The results show that the porosity of a coal sample decreases exponentially with increasing effective stress. With increasing temperature, the initial porosity increases for two pieces of relatively low-rank coal samples. The increased rate of porosity decreases with increasing confining pressure. With increasing temperature, the initial porosity of a relatively high-rank coal sample decreases, and the rate of change of the porosity become faster. An exponential relationship exists between the porosity and permeability. With increasing coal rank, the initial porosity and permeability decrease. The change rate of the permeability decreases with increasing porosity.