Experimental methods in chemical engineering: Temperature programmed surface reaction spectroscopy—<scp>TPSR</scp>
Bibliographic record
Abstract
Abstract Temperature programmed surface reaction spectroscopy (TPSR) is a powerful technique to determine the surface chemistry of bulk metal, supported metal, bulk metal oxide, supported metal oxide, zeolite, and molecular sieve catalysts. It can provide both qualitative and quantitative analysis of the surface active sites present on the catalyst surface, the reaction mechanisms, and kinetics occurring on the catalyst surface by using chemical probe molecules such as alcohols, carboxylates, and specific acidic‐basic reacting gases. In this tutorial review, the highly informative CH 3 OH chemical probe molecule was used to highlight the information that can be obtained from CH 3 OH ‐TPSR experiments. The CH 3 OH molecule readily interacts with the catalyst surface to form surface CH 3 O · and HCOO · intermediates that react to produce HCHO/ HCOOCH 3 / (CH 3 O) 2 CH 2 , CH 3 OCH 3 , and CO/CO 2 products related to the surface redox, acid and basic nature, respectively. Integration of the CH 3 OH ‐TPSR spectra peaks provide the number of surface active sites. The surface kinetic information provided by CH 3 OH ‐TPSR allows to discriminate between different reaction mechanisms (first‐order, second‐order, Langmuir‐Hinshelwood, and Mars‐van Krevelen). We discuss the uncertainty inherent in CH 3 OH ‐TPSR experiments and address source of errors and detection limits. Web of Science indexed over 800 articles citing TPSR since 1990. A bibliometric analysis identified four clusters of reactions and catalysts: the dominant catalysts for partial oxidation and water gas shift were Ni, Ru, and Pt; CeO 2 , Co, Cu, Rh, Pd, and perovskites were the main catalysts for combustion and hydrogenation; Ag and zeolites were grouped with reduction; and, Al 2 O 3 , ZrO 2 , SiO 2 , and V 2 O 5 were applied for dehydrogenation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".