Bibliographic record
Abstract
Abstract Since 1950, only 9% of all plastic produced has undergone recycling, and a mere 10% of that has been recycled multiple times. Most discarded plastic (around 73%) ends up in landfills or is improperly managed, resulting in widespread littering. The main reason for low recycling rates is the lack of recycling technology for multi‐polymer and multi‐layer materials and unsortable mixed plastic waste. This plastic waste is one of the most accumulating types, including for example single‐use food packaging with an average lifetime of less than 6 months. These multi‐layer films, consisting of various polymer types, are not feasible for traditional mechanical recycling, which requires well‐sorted, clean, and homogeneous materials. Several methods for plastic recycling have been proposed to address this issue and tackle the overwhelming influx of plastic waste, among which steam gasification stands out as one of the most promising approaches for recycling mixed, contaminated, and unsortable plastics. This method utilizes high temperatures (800°C) to atomize the plastics, resulting in a gas mixture of , CO, and and small hydrocarbons. The resulting gas can be reformed through hydrocarbon syntheses, for example, via methanol to propylene and ethylene, and successive into new mono‐ and polymers of equal quality to fossil‐based plastics. Moreover, since the high temperatures atomize any organic structure, biomass can be used as a substitute for an extension of the carbon feed, ultimately reducing reliance on fossil feedstock. With these advantages, steam gasification can significantly increase recycling rates and contribute to a bio‐integrated circular carbon economy.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".