Experimental methods in chemical engineering: Thermogravimetric analysis—TGA
Bibliographic record
Abstract
Abstract Thermogravimetric analysis (TGA) is a quantitative analytical technique that monitors the mass of a sample from 1 mg to several g as a furnace ramps temperature to as high as 1600°C under a stable or changing gas flow. The first gravimetric test was in 27 BC when Vitruvius measured limestone's change of mass as it calcined to lime. In modern chemical engineering, researchers apply the technique to derive conversions, kinetics, and mechanisms for any process with a change of mass by isothermal, non‐isothermal, and quasi‐isothermal methods. The mass drops as the sample decomposes, volatile compounds evaporate, or the oxidation state decreases, while in reactive environments (with O 2 , for example), the mass of transition metals may increase. TGA is incapable of detecting phase transitions, polymorphic transformations, or reactions for which mass is invariant. DSC or DTA couple with TGA to help deconvolute a DSC plot by separating physical changes from chemical changes. Evolved gas analysis techniques monitor the gaseous products exiting the TGA furnace on‐line as the temperature ramps. A bibliometric map of keywords from articles citing TGA indexed by Web of Science in 2016 and 2017 identified five research clusters: nanoparticles, performance, and films; crystal structures, acid, and oxidation; composites, nanocomposites, and mechanical properties; kinetics, pyrolysis, and temperature; and adsorption, water and wastewater, and aqueous solutions. This review provides an overview of the basic principles of modern TGA.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.024 |
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".