公开(公告)号：US20250140850A1

公开(公告)日：2025-05-01

申请号：US18735247

申请日：2024-06-06

Applicant: CENTRAL SOUTH UNIVERSITY

Inventor： Shangyong LIN ,  Shuang ZHOU

IPC: H01M4/58 ,  H01M4/02 ,  H01M4/04 ,  H01M4/1397 ,  H01M4/62 ,  H01M10/36

Abstract: The invention relates to a method and application of a multifunctional interface layer modified composite zinc cathode based on zinc blende in zinc metal batteries. Zinc blende powder is produced by crushing and ball milling, then mixed with a solvent and wet screened. The fine zinc blende is dried and mixed with a surfactant to obtain grafted fine powder. This modified powder is combined with a binder and organic solvent to form a slurry, which is coated on the zinc metal cathode. After drying, the modified composite zinc metal cathode is applied to aqueous zinc metal batteries. This method stabilizes the zinc cathode, isolates electrolyte corrosion, inhibits zinc dendrite growth, and addresses issues of dendrite formation, hydrogen evolution, and corrosion, thereby extending the battery's service life.

公开(公告)号：US20250166741A1

公开(公告)日：2025-05-22

申请号：US18827929

申请日：2024-09-09

Applicant: Central South University

Inventor： Chenyang ZHANG ,  Mingjun HAN ,  Dong FAN ,  Wei SUN ,  Jie LI ,  Yong PEI ,  Guixiang ZENG ,  Shangyong LIN ,  Hongliang ZHANG ,  Rong WANG

IPC: G16C20/70 ,  G16C20/50

Abstract: A machine learning (ML)-accelerated first-principles prediction method for a hydration structure of an acid radical anion is provided. The prediction method includes the following steps: S1: constructing and optimizing an anion hydration structure M_mH2O; S2: perturbing the optimized anion hydration structure to generate a training dataset; S3: conducting a ML force field training on the training dataset to establish ML models; S4: conducting a molecular dynamics simulation on the ML models, and identifying atomic structures with a force deviation within a preset range as candidate configurations; S5: merging a validated candidate configuration into a training set for a subsequent iteration to further refine and train the ML model until the model converges, thereby generating an accurate deep potential (DP) model; and S6: conducting a ML-accelerated deep potential molecular dynamics simulation on the DP model to ultimately acquire the hydration structure of the acid radical anion.