Organic redox-active flow batteries enabled by aqueous ionic liquid electrolytes

The supporting electrolytes play a critical role in the electrochemical and thermal stability, solubility, and electrochemical reversibility of redox species. The utilization of aqueous electrolytes is promising for achieving techno-economic targets via advancement in energy density. Herein the attractive “Water-in-Ionic Liquid” aqueous electrolytes system using imidazolium chloride (EMImCl or BMImCl) are explored to extend the temperature stability window and the solubility of organic redox-active species. Water crystallization at subzero temperatures was effectively suppressed in the proposed aqueous electrolytes. The broadening of the electrochemical stability window of the supporting electrolytes enabled the studies of redox metal complexes over a broad temperature range. The organic redox-active species, such as nitroxyl radicals, bipyridinium, and quinones, achieved high solubility and enhanced electrochemical reversibility in the studied aqueous electrolytes with different molalities. The electrochemical performance has been studied with different concentrations of organic species, demonstrating good capacity retention and power density.Die Leitelektrolyte spielen eine entscheidende Rolle für die elektrochemische und thermische Stabilität, Löslichkeit und elektrochemische Reversibilität von Redoxspezies. Die Verwendung von wässrigen Elektrolyten ist ein primärer Weg, um technoökonomische Ziele durch Verbesserung der Energiedichte zu erreichen. Hier werden attraktive wässrige Elektrolyte unter Verwendung von ionischen Imidazoliumchlorid (EMImCl oder BMImCl) Flüssigkeiten untersucht, um das Temperaturstabilitätsfenster und die Löslichkeit organischer redoxaktiver Spezies zu verbessern. Die Wasserkristallisation bei Temperaturen unter Null wurde in dem vorgeschlagenen wässrigen Elektrolyten wirksam unterdrückt. Die Verbreiterung des elektrochemischen Stabilitätsfensters der Leitelektrolyte ermöglichte die Untersuchung von Redoxmetallkomplexen über einen weiten Temperaturbereich. Die organischen redoxaktiven Spezies wie Nitroxylradikale, Bipyridinium und Chinone erreichten eine hohe Löslichkeit und eine verbesserte elektrochemische Reversibilität wässriger Elektrolyte mit unterschiedlicher Molalität. Die elektrochemische Leistung wurde bei verschiedenen Konzentrationen organischer Spezies untersucht

Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants

Causal discovery with latent variables is a crucial but challenging task. Despite the emergence of numerous methods aimed at addressing this challenge, they are not fully identified to the structure that two observed variables are influenced by one latent variable and there might be a directed edge in between. Interestingly, we notice that this structure can be identified through the utilization of higher-order cumulants. By leveraging the higher-order cumulants of non-Gaussian data, we provide an analytical solution for estimating the causal coefficients or their ratios. With the estimated (ratios of) causal coefficients, we propose a novel approach to identify the existence of a causal edge between two observed variables subject to latent variable influence. In case when such a causal edge exits, we introduce an asymmetry criterion to determine the causal direction. The experimental results demonstrate the effectiveness of our proposed method.Comment: Accepted by AAAI 202

A Survey on Causal Reinforcement Learning

While Reinforcement Learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged insights from the causality literature recently, bringing forth flourishing works to unify the merits of causality and address well the challenges from RL. As such, it is of great necessity and significance to collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL methods, and investigate the potential functionality from causality toward RL. In particular, we divide existing CRL approaches into two categories according to whether their causality-based information is given in advance or not. We further analyze each category in terms of the formalization of different models, ranging from the Markov Decision Process (MDP), Partially Observed Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment Regime (DTR). Moreover, we summarize the evaluation matrices and open sources while we discuss emerging applications, along with promising prospects for the future development of CRL.Comment: 29 pages, 20 figure