High-temperature hydrodechlorination of ozone-depleting chlorodifluoromethane (HCFC-22) on supported Pd and Ni catalysts
Bibliographic record
Abstract
The hydrodechlorination of chlorodifluoromethane (HCFC-22) was performed by a catalytic reaction and noncatalytic thermal decomposition at high temperatures of 400-800 °C. After 47 h of time-on-stream on a supported palladium (Pd) catalyst, the gas phase composition of difluoromethane (HFC-32) is 41.0%, with 4.9% of the HCFC-22 remaining, indicating the conversion of up to 95.1% of HCFC-22. The supported nickel catalyst's deactivation is significant as it exhibits the low conversion of HCFC-22 under the same reaction conditions. The deactivation of the catalyst is caused by the polymerization of adsorbed methyl radicals, which competes with the formation of HFC-32. With concentrated reactants at high reaction temperatures, there was an increase in the catalytic activity; however, unwanted tar, methane, and trifluoromethane (HFC-23) by-products are also produced. The use of catalyst suppresses the formation of these by-products. Considering the compositions of the products of the catalytic and noncatalytic reactions, we demonstrate that the use of the supported-metal catalysts and hydrogen flow suppresses tar formation and lowers the required reaction temperature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".