Porous carbon materials for CO_(2)capture,storage and electrochemical conversion

Continuous accumulation and emission into the atmosphere of anthropogenic carbon dioxide(CO_(2)),a major greenhouse gas,has been recognized as a primary contributor to climate change associated with the global warming and acidification of oceans.This has led to drastic changes in the natural ecosystem,and hence an unhealthy ecological environment for human society.Thus,the effective mitigation of the ever increasing CO_(2)emission has been recognized as the most important global challenge.To achieve zero carbon footprint,novel materials and approaches are required for potentially reducing the CO_(2)release,while our current fossil-fuel-based energy must be replaced by renewable energy free from emissions.In this paper,porous carbons with hierarchical pore structures are promising for CO_(2)adsorption and electrochemical CO_(2)reduction owing to their high specific surface area,excellent catalytic performance,low cost and long-term stability.Since efficient gas-phased(electro)catalysis involves the access of reactants to active sites at the gas-liquid-solid triple phase,the hierarchical porous carbon materials possess multiple advantages for various CO_(2)-related applications with enhanced volumetric and gravimetric activities(e.g.,CO_(2)uptake and current density)for practical operations.Recent studies have demonstrated that porous carbon materials exhibited notable activities as CO_(2)adsorbents and provided facile conducting pathways and mass diffusion channels for efficient electrochemical CO_(2)reduction even under the high current operation conditions.Herein,we summarize recent advances in porous carbon materials for CO_(2)capture,storage,and electrochemical conversion.Prospectives and challenges on the rational design of porous carbon materials for scalable and practical CO_(2)capture and conversion are also discussed.

Language Model Using Differentiable Neural Computer Based on Forget Gate-Based Memory Deallocation

A differentiable neural computer(DNC)is analogous to the Von Neumann machine with a neural network controller that interacts with an external memory through an attention mechanism.Such DNC’s offer a generalized method for task-specific deep learning models and have demonstrated reliability with reasoning problems.In this study,we apply a DNC to a language model(LM)task.The LM task is one of the reasoning problems,because it can predict the next word using the previous word sequence.However,memory deallocation is a problem in DNCs as some information unrelated to the input sequence is not allocated and remains in the external memory,which degrades performance.Therefore,we propose a forget gatebased memory deallocation(FMD)method,which searches for the minimum value of elements in a forget gate-based retention vector.The forget gatebased retention vector indicates the retention degree of information stored in each external memory address.In experiments,we applied our proposed NTM architecture to LM tasks as a task-specific example and to rescoring for speech recognition as a general-purpose example.For LM tasks,we evaluated DNC using the Penn Treebank and enwik8 LM tasks.Although it does not yield SOTA results in LM tasks,the FMD method exhibits relatively improved performance compared with DNC in terms of bits-per-character.For the speech recognition rescoring tasks,FMD again showed a relative improvement using the LibriSpeech data in terms of word error rate.