SILK-BASED ARTICLES HAVING VARIABLE ACTIVE AGENT RELEASE RATES

    公开(公告)号:US20250049701A1

    公开(公告)日:2025-02-13

    申请号:US18746604

    申请日:2024-06-18

    Abstract: Coated silk fibroin articles having variable active agent release rates and methods of making and using the same are disclosed. The coating can have variable water permeability. In some cases, the variable permeability is temperature-dependent. In these cases, the release rate of the active agent can be controlled by adjusting the water permeability by raising and lowering the temperature, so long as the temperature is maintained below a melting point of the coating. Elevating the temperature above the melting point causes loss of a portion of the coating, thereby permanently increasing the release rate.

    Chemoresponsive Dyes and Chemiresisive Sensors for Rapid Assay of Scent

    公开(公告)号:US20250044276A1

    公开(公告)日:2025-02-06

    申请号:US18717087

    申请日:2022-12-07

    Inventor: Sameer Sonkusale

    Abstract: A sensor array that is configured to distinguish between volatilomes, each of which comprises a plurality of volatile organic compounds, includes a fibrous substrate with sensors disposed thereon. Some of the sensors change color in response to exposure to particular volatile organic compounds and others change resistance in response to exposure to particular volatile organic compounds. These volatile organic compounds are collected by a patch that is placed on the skin of a subject and then heated in the presence of the sensors.

    SYSTEMS AND METHODS FOR MANAGING COMPLEX SYSTEMS

    公开(公告)号:US20240411279A1

    公开(公告)日:2024-12-12

    申请号:US18692902

    申请日:2022-09-19

    Abstract: Systems and methods are provided for creating and using digital models of real-world systems with a learning or data assimilation method. The systems and methods may be used to create and/or use a quantifiable model of a real-world system formed of a variety of sub-systems. and create and/or manage or quantified error or bias of the model. The learning network may be used to jointly estimate model parameters, dynamic input loads, and the statistical characteristics of the prediction error that includes the effects of modeling error and measurement noise. The learning network may be an adaptive recursive Bayesian inference framework. The prediction error may be of the form of a non-stationary Gaussian process with unknown and time-variant mean vector and covariance matrix to be estimated.

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