Bioresorbable Multilayer Photonic Cavities as Temporary Implants for Tether-Free Measurements of Regional Tissue Temperatures

Objective and Impact Statement.Real-time monitoring of the temperatures of regional tissue microenvironments can serve as the diagnostic basis for treating various health conditions and diseases.Introduction.Traditional thermal sensors allow measurements at surfaces or at near-surface regions of the skin or of certain body cavities.Evaluations at depth require implanted devices connected to external readout electronics via physical interfaces that lead to risks for infection and movement constraints for the patient.Also,surgical extraction procedures after a period of need can introduce additional risks and costs.Methods.Here,we report a wireless,bioresorbable class of temperature sensor that exploits multilayer photonic cavities,for continuous optical measurements of regional,deep-tissue microenvironments over a timeframe of interest followed by complete clearance via natural body processes.Results.The designs decouple the influence of detection angle from temperature on the reflection spectra,to enable high accuracy in sensing,as supported by in vitro experiments and optical simulations.Studies with devices implanted into subcutaneous tissues of both awake,freely moving and asleep animal models illustrate the applicability of this technology for in vivo measurements.Conclusion.The results demonstrate the use of bioresorbable materials in advanced photonic structures with unique capabilities in tracking of thermal signatures of tissue microenvironments,with potential relevance to human healthcare.

Epidermal piezoresistive structure with deep learning-assisted data translation

Continued research on the epidermal electronic sensor aims to develop sophisticated platforms that reproduce key multimodal responses in human skin,with the ability to sense various external stimuli,such as pressure,shear,torsion,and touch.The development of such applications utilizes algorithmic interpretations to analyze the complex stimulus shape,magnitude,and various moduli of the epidermis,requiring multiple complex equations for the attached sensor.In this experiment,we integrate silicon piezoresistors with a customized deep learning data process to facilitate in the precise evaluation and assessment of various stimuli without the need for such complexities.With the ability to surpass conventional vanilla deep regression models,the customized regression and classification model is capable of predicting the magnitude of the external force,epidermal hardness and object shape with an average mean absolute percentage error and accuracy of<15 and 96.9%,respectively.The technical ability of the deep learning-aided sensor and the consequent accurate data process provide important foundations for the future sensory electronic system.