Experimental methods in chemical engineering: Temperature programmed reduction—TPR
Bibliographic record
Abstract
Abstract Temperature programmed reduction (TPR) characterizes the oxido‐reduction properties of bulk and supported catalysts. or CO passes over a pre‐conditioned solid sample as a furnace ramps the temperature at a constant rate. A thermal conductivity detector (TCD) or mass spectrometer records the effluent concentration. In the pre‐conditioning step, Ar or He flushes residual air and absorbed water from the solid sample to maximize the signal‐to‐noise ratio of the TCD signal. We calculate the number of active sites based on the detector signal that correlates with how much hydrogen reacts. The temperature at which it begins to react represents the minimum activation temperature. TPR is cheap, fast, easy to run, and the data is straightforward to interpret. The technique is more popular with chemical engineers than with the broader scientific community. Among the 27 articles that Can. J. Chem. Eng . published in 2016 and 2017 that apply TPR to analyze catalysts, 22 mention reactors, 20 report XRD spectra, 18 mention gas chromatography, and another 15 quantify surface area by BET. Synergy with other temperature programmed methods is lower, as only 5 mention temperature programmed desorption, 5 thermal gravimetric analysis, and 3 temperature programmed oxidation.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.015 |
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".