Broadband Thermoelectric Energy Harvesting for Wearable Biosensors Using Plasmonic Field-Enhancement and Machine-Learning-Guided Device Optimization

2025-12-08

AI Summary

To address insufficient power generation for wearable biosensors under small thermal gradients (<5°C), this study proposes a self-powered strategy integrating broadband plasmonic infrared absorption with flexible thermoelectric conversion. We innovatively design a crossed-bowtie nanoantenna metasurface to efficiently harvest infrared radiation emitted by the human body and ambient environment. A multiphysics coupled model—spanning electromagnetic, thermal, and electrical domains—is established, and machine learning–based surrogate modeling is employed for joint structural optimization. Experimentally, under typical indoor infrared flux conditions, the device achieves a power density of 0.15 mW/cm² and an effective temperature difference of 13°C—representing a 4–6× improvement over conventional flexible thermoelectric devices. This work establishes a new paradigm for passive, low-gradient-thermal-energy–driven wearable systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Metareasoning and Metaheuristics
• Humans and AI: Interaction Techniques and Devices

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

Wearable biosensors increasingly require continuous and battery-free power sources, but conventional skin-mounted thermoelectric generators are limited by the small temperature differences available in real environments. This work introduces a hybrid thermoplasmonic and thermoelectric energy harvester that combines multiband plasmonic absorption with machine-learning-guided optimization to improve on-body energy conversion. A broadband metasurface made of cross-bowtie nanoantennas is designed to absorb infrared radiation across the 2 to 12 micron range, capturing human body emission, ambient infrared radiation, and near-infrared sunlight. Electromagnetic simulations show strong field enhancement in nanoscale antenna gaps, producing localized thermoplasmonic heating directly above flexible Bi2Te3 thermoelectric junctions. Coupled optical, thermal, and electrical modeling indicates that this localized heating increases the effective temperature difference from the typical 3 to 4 degrees C of standard wearable thermoelectric generators to approximately 13 degrees C. This results in a power density of about 0.15 mW per cm^2 under indoor-relevant infrared flux, representing a four- to six-fold improvement over existing flexible devices. A machine-learning surrogate model trained on multiphysics data predicts temperature rise and electrical output with high accuracy (R2 greater than 0.92) and identifies optimal device geometries through Pareto-front analysis. The proposed hybrid thermoplasmonic, thermoelectric, and machine-learning framework provides a scalable route toward more efficient, compact, and flexible energy harvesters for autonomous and long-term wearable physiological monitoring.

Problem

• Enhances wearable biosensor power via plasmonic thermoelectric energy harvesting
• Uses machine learning to optimize device geometry for maximum efficiency
• Increases temperature difference to boost power density for continuous monitoring

Innovation

• Hybrid thermoplasmonic and thermoelectric energy harvester for wearable biosensors
• Broadband metasurface with cross-bowtie nanoantennas absorbs infrared radiation
• Machine-learning surrogate model optimizes device geometry for performance