Accurate and rapid measurement of fluid thermal conductivity

A rapid energy transition will require new heat transfer fluids, and a faster means of discovering and optimizing them. Existing methods, however, are constrained by speed, accuracy, and sample volume - with accurate measurements requiring large sample volumes and long equilibration times. Here, we present a measurement approach that bypasses precise temperature measurement and heat flux measurements. Thermal conductivity, k, is determined by comparing thermally driven voltage variations across an array of resistive heaters embedded in fluid cavities. This measurement, relative to the reference material, minimizes errors from ambient temperature fluctuation, unquantified heat losses, and measurement uncertainties, and it eliminates direct temperature sensing. We report a microfluidic device and measurement method that implements in-run on-chip auto-calibration with a reference material; we test the device on a wide range of substances, including liquids, gases, mixtures, and nanofluids. It delivers results in <10 s, using ~5 µL of sample, about two orders of magnitude faster than conventional steady-state methods - while maintaining accuracy competitive with gold-standard techniques (mean signed error 0.0030 ± 0.0059 W/(m · K) (1.4% ± 3.1%), median absolute error 0.0019 W/(m · K) (MAPE 2.6 %), and expanded uncertainty of k, U = 8.3% ± 0.24% (SD across 10 runs)). This approach makes thermal conductivity measurement accessible to accelerated materials discovery and optimization workflows.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2025-11-26
DOI
https://doi.org/10.1038/s41467-025-65553-x
Citations
1
Primary Topic
Thermal properties of materials
Type
article
Field-Weighted Citation Impact
0.52

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accurate and rapid measurement of fluid thermal conductivity

Mohammad Zargartalebi, Mohammad Amin Kazemi, David Sinton
1 citations
Nature Communications
Thermal properties of materials
0.52
article

Accurate and rapid measurement of fluid thermal conductivity

Mohammad Zargartalebi, Mohammad Amin Kazemi, David Sinton
article en
1 citations

Abstract

A rapid energy transition will require new heat transfer fluids, and a faster means of discovering and optimizing them. Existing methods, however, are constrained by speed, accuracy, and sample volume - with accurate measurements requiring large sample volumes and long equilibration times. Here, we present a measurement approach that bypasses precise temperature measurement and heat flux measurements. Thermal conductivity, k, is determined by comparing thermally driven voltage variations across an array of resistive heaters embedded in fluid cavities. This measurement, relative to the reference material, minimizes errors from ambient temperature fluctuation, unquantified heat losses, and measurement uncertainties, and it eliminates direct temperature sensing. We report a microfluidic device and measurement method that implements in-run on-chip auto-calibration with a reference material; we test the device on a wide range of substances, including liquids, gases, mixtures, and nanofluids. It delivers results in <10 s, using ~5 µL of sample, about two orders of magnitude faster than conventional steady-state methods - while maintaining accuracy competitive with gold-standard techniques (mean signed error 0.0030 ± 0.0059 W/(m · K) (1.4% ± 3.1%), median absolute error 0.0019 W/(m · K) (MAPE 2.6 %), and expanded uncertainty of k, U = 8.3% ± 0.24% (SD across 10 runs)). This approach makes thermal conductivity measurement accessible to accelerated materials discovery and optimization workflows.

Nature CommunicationsVol. 16(1)
University of Toronto (CA)
Canada Research Chairs, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 30%
Thermal properties of materials
0.52
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.