CELL MODULE
    2.
    发明申请

    公开(公告)号:US20250056901A1

    公开(公告)日:2025-02-13

    申请号:US18928788

    申请日:2024-10-28

    Abstract: A cell module is provided. The cell module includes a first substrate; a second substrate disposed opposite to the first substrate; a cell unit disposed between the first substrate and the second substrate; a first thermosetting resin layer disposed between the cell unit and the first substrate; a crosslinked polymer layer disposed between the cell unit and the first thermosetting resin layer; and a second thermosetting resin layer disposed between the cell unit and the second substrate. The crosslinked polymer layer includes a crosslinked polymer, and the crosslinked polymer has a crosslinking degree of from 35.4 to 67.4%.

    CELL MODULE
    3.
    发明公开
    CELL MODULE 审中-公开

    公开(公告)号:US20240304741A1

    公开(公告)日:2024-09-12

    申请号:US18525139

    申请日:2023-11-30

    CPC classification number: H01L31/0481

    Abstract: A cell module is provided. The cell module includes a first substrate; a second substrate disposed opposite to the first substrate; a cell unit disposed between the first substrate and the second substrate; a first thermosetting resin layer disposed between the cell unit and the first substrate; a first protective layer disposed between the cell unit and the first thermosetting resin layer; and a second thermosetting resin layer disposed between the cell unit and the second substrate. The first protective layer includes a first polymer, wherein the cross-linking degree of the first polymer is 0 to 42.3%.

    JUDGING METHOD FOR A MODULE PEELING TIME OF A SOFT ELECTRONIC FABRIC MODULE AND A SYSTEM APPLYING THE SAME

    公开(公告)号:US20240177055A1

    公开(公告)日:2024-05-30

    申请号:US18089108

    申请日:2022-12-27

    CPC classification number: G06N20/00 G06N7/01

    Abstract: A judging method for a module peeling time of a soft electronic fabric module is provided. The method includes: preselecting a plurality of module material combinations, the plurality of module material combinations respectively comprising a substrate material, a wire material and a packaging material; extracting the plurality of module material combinations to generate a plurality of module material combination parameters; generating a plurality of machine learning training data based on the plurality of module material combination parameters and a plurality of module pre-processing conditions; and training a machine learning model according to the plurality of machine learning training data to provide an optimized prediction model for judging a module peeling time.

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