Optimization studies of waste plastics-lignite catalytic coliquefaction
Bibliographic record
Abstract
The optimization studies of an investigation for waste plastics-lignite catalytic coliquefaction are reported in this paper. At first, two SiO 2 supported, Pt-Co and Pt-Mo promoted bimetallic catalysts were prepared. Next, catalytic coliquefactions were attempted on 1/1 mixtures of lignite and waste polymers, e.g. polystyrene, polyisoprene and polyethylene in the presence of various organic solvents. Taking into consideration the results obtained from the variables examined, two newer series of experiments were designed and undertaken aiming at the optimization of this complex transformation. In the first series, the best performing catalyst and the selected most-effective waste plastics were employed and different mixtures of the two more active solvents were added. In the second series, coliquefactions of the precedent mixtures in two stages was attempted in order to optimize overall conversion so far attained. It was found that the mentioned catalytic coprocessing is an interesting form of hydrocarbon synthesis, although the efficacy of the conversion achieved was influenced by the chemical characteristics of the polymers added and of the solvents employed.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".