English

Spatially resolved lock-in micro-thermography (SR-LIT): A tensor analysis-enhanced method for anisotropic thermal characterization

Applied Physics 2024-04-18 v1

Abstract

While high-throughput (HT) computations have streamlined the discovery of promising new materials, experimental characterization remains challenging and time-consuming. One significant bottleneck is the lack of an HT thermal characterization technique capable of analyzing advanced materials exhibiting varying surface roughness and in-plane anisotropy. To tackle these challenges, we introduce spatially resolved lock-in micro-thermography (SR-LIT), an innovative technique enhanced by tensor analysis for optical thermal characterization. Our comprehensive analysis and experimental findings showcase notable advancements: We present a novel tensor-based methodology that surpasses the limitations of vector-based analysis prevalent in existing techniques, significantly enhancing the characterization of arbitrary in-plane anisotropic thermal conductivity tensors. On the instrumental side, we introduce a straightforward camera-based detection system that, when combined with the tensor-based methodology, enables HT thermal measurements. This technique requires minimal sample preparation and enables the determination of the entire in-plane thermal conductivity tensor with a single data acquisition lasting under 40 seconds, demonstrating a time efficiency over 90 times superior to state-of-the-art HT thermology. Additionally, our method accommodates millimeter-sized samples with poor surface finish, tolerating surface roughness up to 3.5 {\mu}m. These features highlight an innovative approach to realizing HT and accurate thermal characterization across various research areas and real-world applications.

Keywords

Cite

@article{arxiv.2404.11424,
  title  = {Spatially resolved lock-in micro-thermography (SR-LIT): A tensor analysis-enhanced method for anisotropic thermal characterization},
  author = {Dihui Wang and Heng Ban and Puqing Jiang},
  journal= {arXiv preprint arXiv:2404.11424},
  year   = {2024}
}
R2 v1 2026-06-28T15:57:22.675Z