Heat capacity measurements of sub-nanoliter volumes of liquids using bimaterial microchannel cantilevers
Bibliographic record
Abstract
Lab-on-a-Chip compatible techniques for thermal characterization of miniaturized volumes of liquid analytes are necessary in applications such as protein blotting, DNA melting, and drug development, where samples are either rare or volume-limited. We developed a closed-chamber calorimeter based on a bimaterial microchannel cantilever (BMC) for sub-nanoliter level thermal analysis. When the liquid-filled BMC is irradiated with infrared (IR) light at a specific wavelength, the IR absorption by the liquid analyte results in localized heat generation and the subsequent deflection of the BMC, due to a thermal expansion mismatch between the constituent materials. The time constant of the deflection, which is dependent upon the heat capacity of the liquid analyte, can be directly measured by recording the time-dependent bending of the BMC. We have used the BMC to quantitatively measure the heat capacity of five volatile organic compounds. With a deflection noise level of ∼10 nm and a signal-to-noise ratio of 68:1, the BMC offers a sensitivity of 30.5 ms/(J g−1 K−1) and a resolution of 23 mJ/(g K) for ∼150 pl liquid for heat capacity measurements. This technique can be used for small-scale thermal characterization of different chemical and biological samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".