Intensified processes for CO2 capture and valorization by catalytic conversion
Bibliographic record
Abstract
Energy and environmental issues are today's major concerns. To solve huge energy needs, the increasing use of fossil fuels leads to significant amounts of CO 2 emissions, which have major negative effects on the environment. An urgent reduction in CO 2 emissions is therefore an absolute priority to minimize the actual global warming. Carbon capture & utilization (CCU) has been introduced as a sustainable avenue. Viewing CO 2 as a resource (renewable feedstock) rather than a waste, its conversion into different value-added products offers an attractive and efficient alternative to CO 2 storage via chemical recycling. However, CO 2 is a very stable molecule whose conversion is a very difficult and complex task. On the other hand, from a sustainable development perspective, CO 2 conversion by catalytic hydrogenation reactions requires hydrogen derived from renewable sources. Because of numerous benefits, our group has been focussing high attention to the application of different process intensification tools to proposed technologies for CO 2 capture in gas/liquid contactors (including membrane separation and enzymatic processes), highly pure hydrogen production with in-situ CO 2 capture, and CO 2 conversion by catalytic hydrogenation, which will be reviewed in the present paper.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".