Synthetic method and application of 2-hydroxyphenyl-5-pyrazinyl ketone

    公开(公告)号:US11746092B2

    公开(公告)日:2023-09-05

    申请号:US17388067

    申请日:2021-07-29

    Inventor: Mei Luo

    CPC classification number: C07D239/26 B01J31/0244

    Abstract: A method of synthesizing a 2-hydroxyphenyl-5-pyrimide ketone represented by the following chemical formula (I), including: weighing 0.048 g of a palladium complex, 0.8413 g of chromone-3-formaldehyde and 2.5719 g of ammonium formate into a 100 mL round bottom flask, then adding 50 mL of anhydrous methanol to dissolve, heating to reflux for 36 h, then stopping the reaction, performing column chromatography with petroleum ether and dichloromethane in a volume ratio of 1:1, and then naturally volatilizing the first component to obtain a light yellow crystal, namely the 2-hydroxyphenyl-5-pyrimidine ketone;
    wherein the chemical formula of the compound (I) is as follows:




    and

    an use of compound (I) as a catalyst in the reaction of benzophenone imine and trimethylsilyl nitrile showing a good catalytic performance, with a conversion rate of 69.1%.

    SYNTHETIC METHOD AND APPLICATION OF 2-HYDROXYPHENYL-5-PYRAZINYL KETONE

    公开(公告)号:US20220033362A1

    公开(公告)日:2022-02-03

    申请号:US17388067

    申请日:2021-09-16

    Inventor: Mei LUO

    Abstract: Disclosed is a method of synthesizing a 2-hydroxyphenyl-5-pyrazinel ketone represented by the following chemical formula (I), comprising: weighing 0.048 g of a palladium complex, 0.8413 g of chromone-3-formaldehyde and 2.5719 g of ammonium formate into a 100 mL round bottom flask, then adding 50 mL of anhydrous methanol to dissolve, heating to reflux for 36 h, then stopping the reaction, performing column chromatography with petroleum ether and dichloromethane in a volume ratio of 1:1, and then naturally volatilizing the first component to obtain a light yellow crystal, namely the 2-hydroxyphenyl-5-pyrazinel ketone, referred to as compound (I); the chemical formula of the compound (I) is as follows: an application of compound (I) as a catalyst in the reaction of benzophenone imine and trimethylsilyl nitrile showing a good catalytic performance, with a conversion rate of 69.1%.

    DYNAMICALLY TRACEABLE PRIVACY-PRESERVING DISTRIBUTED THRESHOLD SIGNATURE SYSTEM AND METHOD

    公开(公告)号:US20240396738A1

    公开(公告)日:2024-11-28

    申请号:US18582670

    申请日:2024-02-21

    Abstract: The present disclosure discloses a dynamically traceable privacy-preserving distributed threshold signature system and method, which are applied to an environment composed of multiple signer modules, multiple aggregator modules, multiple notary modules, multiple tracer modules and a blockchain module; where the signer module signs data, encrypts a signature and uploads encrypted signature to the blockchain module; the aggregator module receives the encrypted signatures from the blockchain module and aggregates them into a synthetic signature, and at the same time sends a corresponding transaction to the blockchain module; the notary module locates the synthetic signature in the blockchain module and partially decrypts the synthetic signature into multiple synthetic signature fragments; the tracer module aggregates synthetic signature fragments and traces a set of signers; and the blockchain module consists of multiple nodes and is responsible for receiving transactions sent by each module.

    Dual physically-driven and data-driven method for reconstructing internal response of bridge

    公开(公告)号:US12112101B2

    公开(公告)日:2024-10-08

    申请号:US18410994

    申请日:2024-01-11

    CPC classification number: G06F30/13 G06F17/16

    Abstract: Disclosed is a dual physically-driven and data-driven method for reconstructing internal response of a bridge. The method includes: obtaining acceleration response by an acceleration sensor under an action of an unknown load of the bridge; embedding a physical logic into a neural network based on a frequency response function; putting a physical formula and corresponding boundary conditions and initial conditions into a loss function as penalty terms, and limiting a space of a feasible solution accordingly; and training a neural network model, and predicting acceleration of an unknown point by inputting an acceleration response set of a known point obtained by the sensor into the network. The formula is solved by converting direct solving of a control formula into optimization of the loss function, such that the problems that the internal response of the bridge is difficult to measure and excessively depends measured data can be effectively solved, and accuracy and robustness of internal response prediction of the bridge can be improved.

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