In‐Liquid Plasma Catalysis: Tools for Sustainable H <sub>2</sub> ‐free Heavy Oils Upgrading
Bibliographic record
Abstract
Abstract Non‐thermal plasma (NTP) catalysis, mirroring the trend towards non‐conventional electron‐mediated molecular activation, unfolds novel routes for chemical reactions. Operating at ambient pressure, lower temperatures, and energized by electricity that can powered by renewable sources, NTP offers a cost‐effective and efficient means of sustainable fuel production. This approach has the potential to revolutionize the oil and gas industry, meeting current energy demands while circumventing the challenges posed by conventional thermal catalysis processes, thereby enhancing environmental sustainability and energy security. Through an exploration of the synergistic effects between plasma, catalysis, and hydrocarbon molecules, this concept paper emphasizes the significant advancements made in in‐liquid plasma catalysis strategies for fuel production from heavy oil upgrading. We also provide insights into the heterogeneous catalyst design and the role of plasma gas as an additional catalyst for achieving efficient and sustainable energy solutions. The prospects of in‐liquid plasma catalysis, emphasizing its transformative role in shaping the energy future, are also discussed.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".