New Insights on Catalytic Valorization of Carbon Dioxide by Conventional and Intensified Processes
Bibliographic record
Abstract
Carbon capture is an emerging technology that is often mentioned as a potential solution to the global warming crisis. However, most of the captured carbon is now treated as waste, discarded, and not used in any meaningful way. On the other hand, from a sustainable development point of view, CO 2 valorization could be a very appealing approach that offers an economic potential when this compound is recycled into valuable chemical products. In this regard, this state-of-the-art review encompasses a wide range of different processes to achieve the conversion of CO 2 into a large variety of products. It does not focus solely on a specific reaction or method, but rather brings different aspects together to provide a far more complete portrait of this emerging subject. Several methods and approaches are discussed, namely thermochemical, electrochemical, photochemical, photoelectrochemical, biochemical, and plasma-assisted conversion. The current state of CO 2 valorization, as well as emerging alternatives and stimulating prospects, was examined, while bearing in mind the realities that limit the application of such technologies. Special emphasis was placed on the optimization of reaction conditions and the development of materials that ensure the best possible production of valuable and environmentally friendly compounds from CO 2 . This review also highlights the challenges related to the application of CO 2 valorization at an industrial scale, which are also important in directing further research in this domain and assessing the prospects of these technologies. Due to the growing number of published papers on the subject, and the limited number of papers offering such a large perspective, we thus believe that this review will be a valuable addition to guide all researchers involved in the field of CO 2 valorization, but also for industrial and political leaders interested in investing in and developing these promising new technologies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".