Click:   The Last Update Time:--
中文

Faculty Profile

  • Home
  • Scientific Research
    • Research Fields
    • Paper Publications
    • Patents
    • Published Books
    • Research Projects
  • Teaching Achievements
    • Teaching Resources
    • Teaching Information
    • Teaching Achievement
  • Awards and Honours
  • Enrollment Information
  • Student Information
  • My Album
  • Blog

lww

  • Professor
  • Supervisor of Doctorate Candidates
  • Supervisor of Master's Candidates
  • Name :Wenwu Li
  • Name (Pinyin):lww
  • School/Department:材料科学与化学工程学院
  • Degree:Doctoral Degree in Engineering
  • Professional Title:Professor
  • Status:Employed
  • Teacher College:College of Material Science and Chemical Engineering
  • Discipline:Materialogy
    Chemical Engineering

Ph.D.Supervisor

MA Supervisor

Contact Information
  • PostalAddress:18f45ebbe89aa3e8f7b94a843a65c870de0eab0dc07a7e0c15c13fe5930a354a69eb2b8ad25340befe6877d1c25eafea3ddac479bf7f1ea24587566887009683d4c0cc185af0b7214db0133aca89b85661251b917602ea7ac4600d640c726724ef532f356c1e32baf35ee303ba73e5b62caad8e933fb0a0177a687b4121d0544
  • Telephone:53b27574b3e92bea19739d9a6438143847a27d714ed799173c7bac5b1a1c5680072e8969eeb7eeed2bd16a98c4a2275093a21ce3c67033d42a72fcbff313a8adbf8869dcd016fb1f9fb4ef6590d4aec63dd329368a32f1eed6d0a51752baace98139bd1bca614e0b9e49f9369cb5e04a79018d33a15784160fca62768541014c
  • Email:2e9fcdd2c77fdfdb99cb783d62263f84999764c6438f2df947357224fa701d95ff50ad8d329a6e998b75a5ae4dc22baed5480ae1985540653b035f5dce8c60d33fb12187342f2bd1b48de52759a4ea772d8139e276c4c1aaa2614dca1e877eac63e177862e4683f8ec708b62a27ea4f1c68d1640f5c29bcece25e2f51708494d
  • Research Focus

solid electrolyte

在低温、高温等极端工况下,要同时做到“更安全、更高能量、更长寿命”,电池研发正走到一个关键拐点:从改良传统液态体系,转向更具颠覆性的全固态锂电与无负极(anode-free)锂电。全固态用固态电解质替代可燃液体,从源头提升安全性,但难点在于固体之间“贴不紧、反应慢、阻抗大”;无负极则进一步去掉预制锂负极,把锂直接在集流体上沉积/剥离,把能量密度推到更高,同时也把界面稳定与沉积均匀性推到极限。围绕这一前沿方向,PEO聚合物电解质强调可加工与柔性(需要解决低温导电不足),卤化物电解质强调高电导与界面相容(需要解决高电压稳定与工艺致密化),而高熵设计通过多组元协同,为“电导—稳定—力学—工艺”提供新的优化空间。更先进的是引入AI/大数据+DFT的“加速研发”模式:用DFT计算关键指标(离子迁移难易、缺陷、界面反应倾向),把文献与实验数据整理成可检索数据库,再用机器学习在多目标约束下快速筛选配方与结构,并通过实验验证形成闭环迭代,让固态与无负极电池更快走向可工程化与宽温域应用。