Using a composite Catalyst in The gasification process, in-situ Plastic Waste is converted to Energy.
Bibliographic record
Abstract
Abstract The world is eager to find a solution to reduce the phenomenon of climate change and transition to sustainable and renewable resources in all facets of our lives as we live in this period. Climate change and the emission of greenhouse gases are closely related issues. In that study, we focused on finding ways to reduce municipal management of plastic solid waste and conversion to enhance gases and solid carbon instead of direct incineration or disposal. In order to fracture a variety of plastic debris (PEHD, PELD, PVC, and PS) in a fixed bed reactor system, we created composite materials made of three transition metal oxides (manganese, titanium, and iron). These materials were built using layers of bentonite clay. To enrich and improve gas yields and product quality, the gasification reaction has been set up with customizable, parameter-optimized heating rate, loading, and reaction time settings. For the distribution of composites and products, many characterization techniques, including XRD, TEM, SEM, GC, and EDX (Gas yield and char weight ratio), have been used.
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".