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Record W1990651652 · doi:10.1080/10934529.2011.586262

High-temperature hydrodechlorination of ozone-depleting chlorodifluoromethane (HCFC-22) on supported Pd and Ni catalysts

2011· article· en· W1990651652 on OpenAlexfundno aff
Jeong‐Myeong Ha, Dae Woo Kim, Jaehoon Kim, Byoung Sung Ahn, Yunje Kim, Jeong Won Kang

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2011
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsnot available
FundersKorea Environmental Industry and Technology InstituteUniversity of Windsor
KeywordsCatalysisChemistryDifluoromethanePalladiumMethaneHydrogenOzoneInorganic chemistryOrganic chemistryRefrigerant

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.237
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2011
Admission routes1
Has abstractyes

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