In Situ Electrochemical Polymerization of Cathode Electrolyte Interphase Enabling High-Performance Lithium Metal Batteries.

Lithium metal batteries (LMBs) with high-voltage nickel-rich cathodes show great potential as energy storage devices due to their exceptional capacity and power density. However, the detrimental parasitic side reactions at the cathode electrolyte interface result in rapid capacity decay. Herein, a polymerizable electrolyte additive, pyrrole-1-propionic acid (PA), which can be in situ electrochemically polymerized on the cathode surface and involved in forming cathode electrolyte interphase (CEI) film during cycling is proposed. The formed CEI film prevents the formation of microcracks in LiNi0.8Co0.1Mn0.1O2 (NCM811) secondary particles and mitigates parasitic reactions. Additionally, the COO- anions of PA promote the acceleration of Li+ transport from cathode particles and increase charging rates. The Li||NCM811 batteries with PA in the electrolyte exhibit a high capacity retention of 83.83% after 200 cycles at 4.3 V, and maintain 80.88% capacity after 150 cycles at 4.6 V. This work provides an effective strategy for enhancing interface stability of high-voltage nickel-rich cathodes by forming stable CEI film.

Interlayer Polymerization to Construct a Fully Conjugated Covalent Organic Framework as a Metal-Free Oxygen Reduction Reaction Catalyst for Anion Exchange Membrane Fuel Cells.

Two-dimensional (2D) covalent organic frameworks (COFs) have a multilayer skeleton with a periodic π-conjugated molecular array, which can facilitate charge carrier transport within a COF layer. However, the lack of an effective charge carrier transmission pathway between 2D COF layers greatly limits their applications in electrocatalysis. Herein, by employing a side-chain polymerization strategy to form polythiophene along the nanochannels, a conjugated bridge is constructed between the COF layers. The as-synthesized fully conjugated COF (PTh-COF) exhibits high oxygen reduction reaction (ORR) activity with narrowed energy band gaps. Correspondingly, PTh-COF is tested as a metal-free cathode catalyst for anion exchange membrane fuel cells (AEMFCs) which showed a maximum power density of 176 mW cm-2 under a current density of 533 mA cm-2. The density functional theory (DFT) calculation reveals that interlayer conjugated polythiophene optimizes the electron cloud distribution, which therefore enhances the ORR performance. This work not only provides new insight into the construction of a fully conjugated covalent organic framework but also promotes the development of new metal-free ORR catalysts.

Unsupervised Learning-Guided Accelerated Discovery of Alkaline Anion Exchange Membranes for Fuel Cells.

Without insight into the correlation between the structure and properties, anion exchange membranes (AEMs) for fuel cells are developed usually using the empirical trial and error method or simulation methods. Here, a virtual module compound enumeration screening (V-MCES) approach, which does not require the establishment of expensive training databases and can search the chemical space containing more than 4.2×105 candidates was proposed. The accuracy of the V-MCES model was considerably improved when the model was combined with supervised learning for the feature selection of molecular descriptors. Techniques from V-MCES, correlating the molecular structures of the AEMs with the predicted chemical stability, generated a ranking list of potential high stability AEMs. Under the guidance of V-MCES, highly stable AEMs were synthesized. With understanding of AEM structure and performance by machine learning, AEM science may enter a new era of unprecedented levels of architectural design.

Machine Learning-Aided Design of Highly Conductive Anion Exchange Membranes for Fuel Cells and Water Electrolyzers.

Alkaline anion exchange membrane (AEM)-based fuel cells (AEMFCs) and water electrolyzers (AEMWEs) are vital for enabling the efficient and large-scale utilization of hydrogen energy. However, the performance of such energy devices is impeded by the relatively low conductivity of AEMs. The conventional trial-and-error approach to designing membrane structures has proven to be both inefficient and costly. To address this challenge, a fully connected neural network (FCNN) model is developed based on acid-catalyzed AEMs to analyze the relationship between structure and conductivity among 180,000 AEM variations. Under machine learning guidance, anilinium cation-type membranes are designed and synthesized. Molecular dynamics simulations and Mulliken charge population analysis validated that the presence of a large anilinium cation domain is a result of the inductive effect of N+ and benzene rings. The interconnected anilinium cation domains facilitated the formation of a continuous ion transport channel within the AEMs. Additionally, the incorporation of the benzyl electron-withdrawing group heightened the inductive effect, leading to high conductivity AEM variant as screened by the machine learning model. Furthermore, based on the highly active and low-cost monomers given by machine learning, the large-scale synthesis of anilinium-based AEMs confirms the potential for commercial applications.