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Record W2560365339 · doi:10.1002/cjce.22754

Trimetallic Au‐Cu‐K/AC for acetylene hydrochlorination

2016· article· en· W2560365339 on OpenAlexaffvenue
Benxian Shen, Jigang Zhao, Xiaotao Bi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of British Columbia
FundersFundamental Research Funds for the Central UniversitiesChina Scholarship Council
KeywordsAcetyleneCatalysisSpace velocityVinyl chlorideMercury (programming language)Noble metalChemistryMetalInorganic chemistryChlorideWhiskerPlatinumHydrocarbonSelectivityOrganic chemistryCopolymerPolymerPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract The metal chloride of KCl was chosen to modify Au‐Cu/AC to decrease the noble metal of gold and enhance the catalytic performance. Then a mercury‐free catalyst of Au‐Cu‐K/AC was prepared by the impregnation method. The catalytic performances of mercury‐free catalyst for acetylene hydrochlorination were conducted for 1600 h in a fixed bed reactor by a single‐tube pilot unit. The fresh and used catalysts were also characterized in comparison. The results showed that the acetylene conversion on mercury‐free catalyst decreased slowly from 98 % to 89 %, and the vinyl chloride monomer (VCM) selectivity was kept at 99.7 % under reaction conditions of temperature 165 °C, gas hourly space velocity (GHSV) 40 h −1 , and feed volume ratio of HCl to C 2 H 2 of 1.05 during 1600 h on stream. The results showed that the additives of K with Cu can make the active species of gold dispersed well and retard the aggregation of particles. The reason for the slow decline of acetylene conversion for Au‐Cu‐K/AC catalyst was the whisker carbon deposition, shown in a faint yellow colour over the catalyst surface, which consisted of short‐chain hydrocarbon molecules. Further study for accelerated deactivation of sole metal in the catalyst gives the clues that the non‐noble metal of Cu in Au‐Cu‐K/AC catalyst plays a key role to form the deposition.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations30
Published2016
Admission routes2
Has abstractyes

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