The application of nonthermal plasma in methanol synthesis via CO<sub>2</sub> hydrogenation
Bibliographic record
Abstract
Abstract CH 3 OH is an energy carrier that can be generated from renewable resources and be used as a fuel in fuel cells and internal combustion engines and a platform chemical for the synthesis of value‐added chemicals or gasoline. Carbon dioxide (CO 2 ) hydrogenation is one of the widely researched methods to generate methanol. The traditional CO 2 hydrogenation reaction method (requires high H 2 pressure and temperatures) has attracted considerable attention. However, the new emerging field of catalysis referred to as nonthermal plasma (NTP) catalysis has also been developed extensively for methane reforming and CO 2 hydrogenation to methane and CO. The plasma‐assisted approach not only presents remarkable advantages, such as room temperature and atmospheric H 2 pressure but also has great potential to be powered by renewable electricity in a flexible way since it can be easily switched on/off. In this account, we review the recent articles published on methanol synthesis from CO 2 and H 2 using NTP. We reviewed and discussed the mechanism of this reaction under NTP, the modification of the reactor configurations, and the rationale behind the catalyst design. In the end, we discussed the advantages and disadvantages of each of these works and the future perspectives of this interesting privileged reaction. We believe this review is of interest to researchers active in sustainable heterogeneous catalysis.
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 | 0.001 |
| 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".