A Novel Iterative-Fitting Approach for the Thermal Characterization of Thin Fibers
Abstract
The transient electrothermal technique (TET) is widely used to measure the thermal properties of thin fibers and films, but its accuracy declines when radiative heat losses are significant and surface emissivity is unknown. These conditions are difficult to avoid at the microscale, while conventional analyses either neglect them or rely on separate measurements that introduce sequential errors. We present a TET implementation that determines multiple thermal properties and effective surface emissivity from only two transient measurements. An analytical model incorporating thermal radiation and variable heat generation supports sensitivity-guided, multiparameter fitting. Two forms of the temperature solution yield thermal conductivity and diffusivity, and thermal conductivity and volumetric heat capacity, respectively. An iterative fitting procedure then recovers intrinsic properties corrected for environmental effects while reducing the required sample count and measurement time. Measurements of amorphous silicon dioxide (SiO2) optical fiber and Hi-Nicalon Type S silicon carbide (SiC) fiber agreed with literature values to within an average error of approximately 10% across all samples. Experimental and numerical uncertainty analyses quantify measurement variability and relate it to sample geometry. This method simultaneously resolves multiple intrinsic thermal properties, corrects radiative-loss errors, and provides quantitative guidance for sample geometry selection.
Related
- Thermal delay response and high-stability RF transfer in hollow-core fibers
- In Situ Thermal Preparation by CO2-Laser and Chemical Characterization of Oxide Substrates Across Multiple Crystallographic Orientations
- Characterization of Thermal Recovery Activation in Fast Photochromic Flavoprotein Charge-Transfer Complexes.
- Model-fitting perspective on the robustness and speed of CLASS in reflection matrix microscopy
- Fast Self-Triggering and Segment-Fitting of Cosmic Muons with a small-Diameter Monitored Drift Tube Detector
BibTeX
@misc{spirnock2026novel,
title = {{A Novel Iterative-Fitting Approach for the Thermal Characterization of Thin Fibers}},
author = {William Spirnock and Alison Hake and Dihui Wang and Timothy Sullivan and Dana He and Heng Ban},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.03933},
eprint = {2610.03933},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.03933},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - A Novel Iterative-Fitting Approach for the Thermal Characterization of Thin Fibers
AU - William Spirnock
AU - Alison Hake
AU - Dihui Wang
AU - Timothy Sullivan
AU - Dana He
AU - Heng Ban
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.03933
UR - https://arxiv.org/abs/2610.03933
AB - The transient electrothermal technique (TET) is widely used to measure the thermal properties of thin fibers and films, but its accuracy declines when radiative heat losses are significant and surface emissivity is unknown. These conditions are difficult to avoid at the microscale, while conventional analyses either neglect them or rely on separate measurements that introduce sequential errors. We present a TET implementation that determines multiple thermal properties and effective surface emissivity from only two transient measurements. An analytical model incorporating thermal radiation and variable heat generation supports sensitivity-guided, multiparameter fitting. Two forms of the temperature solution yield thermal conductivity and diffusivity, and thermal conductivity and volumetric heat capacity, respectively. An iterative fitting procedure then recovers intrinsic properties corrected for environmental effects while reducing the required sample count and measurement time. Measurements of amorphous silicon dioxide (SiO2) optical fiber and Hi-Nicalon Type S silicon carbide (SiC) fiber agreed with literature values to within an average error of approximately 10% across all samples. Experimental and numerical uncertainty analyses quantify measurement variability and relate it to sample geometry. This method simultaneously resolves multiple intrinsic thermal properties, corrects radiative-loss errors, and provides quantitative guidance for sample geometry selection.
ER -