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DOI 10.1021/acsomega.5c12602

Медицинская библиотека · ACS omega · 2026

Integrated Sensor-Composite Material Platform for High-Resolution Voltage Mapping in Tissue-Mimicking Models.

Jain Kajal C, Srivastava Richa, Jamali Armin, Goldschmidtboeing Frank, Woias Peter, Comella Laura M

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Аннотация

Accurate mapping of voltage distributions in tissue-mimicking materials (TMMs) is essential for the reliable design and validation of electrical stimulation therapies. Conventional phantoms with embedded commercial electrodes often suffer from limited spatial resolution and field mapping artifacts due to the electrode size and supporting structures. Here, we present a scalable sensor platform featuring custom copper sensor arrays (1.6 mm diameter, 1 cm spacing), each individually encapsulated by a dielectric layer and embedded in conductive PDMS/MWCNT composites (conductivity ∼0.24 S/m). This platform addresses a key limitation of existing embedded electrode approaches by improving spatial resolution and mapping accuracy while maintaining precisely known sensor coordinates and flexible placement within a conductive TMM. The system incorporates a robust, multiplexed electronic interface for automated, high-density voltage mapping. Voltage mapping experiments under identical AC stimulation performed at 100 Hz with measured signal amplitudes of 0.7 and 1 Vpp demonstrate that the sensor insulation technique enables high-resolution, symmetric voltage maps across the TMM with minimal measurement artifacts or distortion. This platform provides accurate visualization of voltage distributions, from which local electric fields can be inferred, and supports the rigorous preclinical development, validation, and calibration of advanced electrical stimulation protocols across diverse phantom geometries.

Для цитирования: Jain Kajal C, Srivastava Richa, Jamali Armin, Goldschmidtboeing Frank, Woias Peter, Comella Laura M. Integrated Sensor-Composite Material Platform for High-Resolution Voltage Mapping in Tissue-Mimicking Models.. ACS omega. 2026. DOI: 10.1021/acsomega.5c12602.
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