Experimental methods in chemical engineering: Differential scanning calorimetry—DSC
Bibliographic record
Abstract
Abstract Differential calorimetry assesses energy flow between a sample and its environment. The sample may be heated at a known heating rate (either constant or temperature modulated), or held in an isothermal environment or adiabatic environment depending on instrument and experimental design. The subset of differential calorimetry that deals with known heating or cooling rates is termed differential scanning calorimetry (DSC) and is a foundational technique to modern thermodynamics. It reports the heat flow versus temperature or time from which we calculate specific heat capacity at constant pressure, , enthalpy of fusion, and the heat of reaction. Moreover, it identifies how microstuctural properties evolve and thermal arrests—a characteristic of phase transitions. Heat‐flux DSCs measure the temperature difference between a reference and a sample that sit on a thin two‐dimensional plate. Power compensated DSCs heat reference material and the sample in independent furnaces while maintaining each at the same temperature. The Tian‐Calvet DSC is similar to the heat‐flux DSC, but minimizes error induced at high temperature with ring shaped thermopiles that surround the reference and the sample and in most designs incorporate the independent furnaces characteristic of heat flux DSC (three‐dimensional heat flow probe). Convection and radiation energy leaks compromise accuracy above 600 , particularly for pan‐style heat flux and power‐compensated DSC, which are sensitive to heat transfer by conduction only. The Tian‐Calvet DSC maximizes the signal‐to‐noise ratio by enveloping the sample and reference in the thermopile. Web of Science indexed 11 800 articles in 2016 and 2017 that mentioned DSC and assigned 789 to chemical engineering, which ranks it 5th after polymer science, material science, physical chemistry, and multi‐disciplinary chemistry. A bibliometric analysis recognizes four research clusters: polymers and nano‐composites, alloys and kinetics, nano‐particles and drug delivery, and fibres.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.033 |
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".