Accurate and rapid measurement of fluid thermal conductivity
Bibliographic record
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. The discovery of efficient heat transfer fluids is limited by slow, manual-intensive measurement methods. Here, the authors design a microfluidic device that rapidly and accurately measures thermal conductivity using identical resistive heaters in symmetric microchannels, requiring only ~5 μL of sample in under 10 seconds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".