In the quest for more efficient and sustainable energy solutions, researchers Hamidreza Moradi and Melika Filvantorkaman from the University of Massachusetts Amherst have made a significant stride in the field of wearable technology. Their work, published in the journal Nature Communications, introduces a novel approach to powering wearable biosensors, which are increasingly in demand for continuous health monitoring.
Wearable biosensors require a reliable and continuous power source, but traditional skin-mounted thermoelectric generators face limitations due to the small temperature differences available in real-world environments. Moradi and Filvantorkaman’s research addresses this challenge by combining multiband plasmonic absorption with machine-learning-guided optimization to enhance energy conversion.
The researchers designed a broadband metasurface made of cross-bowtie nanoantennas that can absorb infrared radiation across a wide range of 2 to 12 microns. This allows the device to capture human body emission, ambient infrared radiation, and near-infrared sunlight. Electromagnetic simulations demonstrated strong field enhancement in the nanoscale gaps of the antennas, leading to localized thermoplasmonic heating directly above flexible Bi2Te3 thermoelectric junctions.
By coupling optical, thermal, and electrical modeling, the team found that this localized heating increases the effective temperature difference from the typical 3 to 4 degrees Celsius of standard wearable thermoelectric generators to approximately 13 degrees Celsius. This improvement results in a power density of about 0.15 milliwatts per square centimeter under indoor-relevant infrared flux, representing a four- to six-fold improvement over existing flexible devices.
To optimize the device’s performance, the researchers employed a machine-learning surrogate model trained on multiphysics data. This model accurately predicts temperature rise and electrical output (with an R-squared value greater than 0.92) and identifies optimal device geometries through Pareto-front analysis.
The practical applications of this research for the energy sector are significant. The hybrid thermoplasmonic, thermoelectric, and machine-learning framework offers a scalable route toward more efficient, compact, and flexible energy harvesters. This technology could be integrated into wearable biosensors for autonomous and long-term physiological monitoring, reducing the need for frequent battery replacements and enhancing the sustainability of wearable health technologies.
Moreover, the principles demonstrated in this study could be adapted for other applications in the energy industry, such as improving the efficiency of thermoelectric generators in various environments where temperature differences are minimal. The combination of plasmonic field-enhancement and machine-learning-guided optimization presents a promising avenue for advancing energy harvesting technologies.
In summary, Moradi and Filvantorkaman’s research provides a novel and effective approach to enhancing the performance of wearable thermoelectric generators, paving the way for more sustainable and efficient energy solutions in the wearable technology sector and beyond.
This article is based on research available at arXiv.