Method to increase surface-enhanced Raman scattering signal

    公开(公告)号:US11754502B1

    公开(公告)日:2023-09-12

    申请号:US17828632

    申请日:2022-05-31

    CPC classification number: G01N21/658 B05D5/00 B05D7/24

    Abstract: A method of increasing a surface-enhanced Raman scattering (SERS) signal of a compound is provided. The method includes dissolving the compound in water to form a solution, adding a substrate at least partially coated with gold nanoparticles to the solution to form a mixture, removing the substrate from the mixture and washing with water to form a SERS sample having at least a portion of molecules of the compound adsorbed to the gold nanoparticles on the substrate, and recording a SERS spectrum of the SERS sample. The gold nanoparticles are in a two-dimensional (2D) monolayer assembly on the substrate and are 10-250 nm in size. The SERS signal of the SERS spectrum is higher than a SERS signal of a SERS spectrum of the compound on the substrate without the gold nanoparticles.

    COATING PRODUCTION LINE SYSTEM
    10.
    发明公开

    公开(公告)号:US20230173533A1

    公开(公告)日:2023-06-08

    申请号:US18062242

    申请日:2022-12-06

    Applicant: coatingAI AG

    Inventor: Marlon BOLDRINI

    CPC classification number: B05C11/1021 B05C19/008 B05D7/24 B05D2401/32

    Abstract: A coating production line system for coating work pieces comprises a coating powder, a coating apparatus, an inspection unit to measure the thickness of the applied coating, a conveyor unit to move the work pieces through the system, and a control unit to use thickness requirements and coating parameters to control the coating apparatus based on said coating parameters with a machine learning instance. A database comprises coating powder characteristics parameter as input vector for the machine learning instance for generating an output vector to control the coating apparatus being a first additional part vector. The control unit determines the coating quality based on a comparison between the thickness data acquired from the inspection unit and the retrieved thickness requirement data as second additional part vector. The first and second additional part vectors are fed back as additional parts to the next input vector for the machine learning instance.

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