Trimetallic Au‐Cu‐K/AC for acetylene hydrochlorination
Bibliographic record
Abstract
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 C2H2 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.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".