Perfluorinated Amines: Accelerating Lithium Electrodeposition by Tailoring Interfacial Structure and Modulated Solvation for High-Performance Batteries.

Modulating interfacial electrochemistry represents a prevalent approach for mitigating lithium dendrite growth and enhancing battery performance. Nevertheless, while most additives exhibit inhibitory characteristics, the accelerating effects on interfacial electrochemistry have garnered limited attention. In this work, perfluoromorpholine (PFM) with facilitated kinetics is utilized to preferentially adsorb on the lithium metal interface. The PFM molecules disrupt the solvation structure of Li+ and enhance the migration of Li+. Combined with the benzotrifluoride, a synergistic acceleration-inhibition system is formed. The ab initio molecular dynamics (AIMD) and density functional theory (DFT) calculation of the loose outer solvation clusters and the key adsorption-deposition step supports the fast diffusion and stable interface electrochemistry with an accelerated filling mode with C─F and C─H groups. The approach induces the uniform lithium deposition. Excellent cycling performance is achieved in Li||Li symmetric cells, and even after 200 cycles in Li||NCM811 full cells, 80% of the capacity is retained. This work elucidates the accelerated electrochemical processes at the interface and expands the design strategies of acceleration fluorinated additives for lithium metal batteries.

Computational drug discovery.

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field.