Harbin Institute of Technology

About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.

Deeply understanding the Zn anode behaviour and corresponding improvement strategies in different aqueous Zn-based batteries

Abstract: Owing to the high capacity of the metallic Zn anode and intrinsically safe aqueous electrolyte, aqueous Zn-based batteries are advanced energy storage technology alternatives beyond lithium-ion batteries, providing a cost benefit, high safety, and competitive energy density. There has been a new wave of research interest across the family of Zn batteries, but fundamental understanding of the Zn electrode and its performance improvement still remain inconclusive. Based on the pH value of the electrolyte, Zn-based batteries can be divided into two types, with one adopting alkaline electrolyte and the other mild (including slightly acidic) electrolyte. As the behavior of the Zn electrode in these two distinctive systems is different, their requirements to yield excellent performance are different. In this Review, we present a comprehensive overview of the Zn electrode and its fundamentals in both systems. First, the differences and similarities of the Zn electrode in both systems are outlined. Specific attention is paid to the working principles and technical challenges. Then, Zn electrode issues and recently proposed strategies for each system are summarized and compared. Finally, a perspective on future research directions towards practical applications of aqueous Zn batteries is included.

Electroactive biofilms: Current status and future research needs

Abstract: Electroactive biofilms (EABFs) generated by electrochemically active microorganisms have many potential applications in bioenergy and chemicals production. Biofilm electroactivity can have a significant impact on the yield and efficiency of the conversion processes. This review assesses the effects of process and design parameters on the growth and activity of biofilms in bioelectrochemical systems (BESs). First we compare the role of planktonic and biofilm-forming microorganisms in BESs. The effect of physical, chemical, and electrochemical operating parameters such as flow rate, temperature, pH, ionic strength, substrate concentration and loading, external resistance, and redox potential on EABF attributes such as growth rate, exoelectrogen population, formation of extracellular polymeric substances, mediator synthesis, and rate of electron transfer are discussed. The relationship between electrochemical performance and operating parameters is also examined to identify gaps in assessment and the potential role of future modeling efforts. Similarly, we review what is currently known about the mechanisms that enable electroactive biofilms to transfer electrons and also the contribution of the electrical conductivity of the biofilms' exopolymeric components to BES performance. The current status of cathodic biofilms is also reviewed. Complementary approaches that use process control to optimize EABF composition and biomass density, while minimizing mass transfer effects and changes to system design parameters, are likely necessary to improve BES performance to a level needed for commercial consideration. Finally, future research needs that enable better understanding and optimization of the performance of EABFs are outlined.

Attention-over-Attention Neural Networks for Reading Comprehension

Abstract: Cloze-style reading comprehension is a representative problem in mining relationship between document and query. In this paper, we present a simple but novel model called attention-over-attention reader for better solving cloze-style reading comprehension task. The proposed model aims to place another attention mechanism over the document-level attention and induces “attended attention” for final answer predictions. One advantage of our model is that it is simpler than related works while giving excellent performance. In addition to the primary model, we also propose an N-best re-ranking strategy to double check the validity of the candidates and further improve the performance. Experimental results show that the proposed methods significantly outperform various state-of-the-art systems by a large margin in public datasets, such as CNN and Children’s Book Test.