Bibliographic record
Abstract
Plastic pollution has become one of the main environmental problems that afflict land, waterways and oceans. Currently, about 141 megatonnes (Mt) of plastic wastes are produced annually in the world, while only about 13% of those are recycled. The great majority of plastic wastes are incinerated or dumped, which pollutes the environment. Remanufacturing or recycling is a way to reduce plastic waste, but plastics cannot be recycled endlessly. Forty percent of plastic wastes have a short life time and are contaminated with residues or contain an abundance of water. However, these contaminated plastic wastes can be converted to energy. As plastics are petrochemicals, conversion of plastic wastes to carbon free hydrogen (H₂) and other solid chemicals will not only protect the environment but also reduce greenhouse gas (GHG) emissions compared to converting plastic wastes to liquid fuel. Catalysts help to improve the product selectivity toward hydrogen or other chemicals. In this project, an experimental investigation on catalytic hydrothermal conversion of plastics to hydrogen under a supercritical water condition was carried out. Hydrothermal conversion is suitable for feedstocks with high water content, and supercritical water is able to dissolve organics. The effect of catalysts that have high selectivity toward hydrogen production was investigated. The results reveal that without a catalyst, only CO₂ was observed in the produced gas. Alkaline metals NaOH and KOH yielded gas with H₂ selectivity of 74.4% and 44.3%, respectively. More CO than H₂ vol% was formed when Raney®-Nickel 2800 was used as catalyst. Liquid selectivity also varied significantly with changing the catalyst.
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".