The state-of-the-art technologies and recent trends of sustainable pathways to transform captured carbon into eight key valuable products across industrial applications
Bibliographic record
Abstract
Carbon dioxide (CO2) emissions in the atmosphere are both of natural and anthropogenic sources. Obviously, the CO2 is at the heart of the carbon cycle, which constantly exchanges carbon elements between the compartments of water, air and soil. These CO2 emissions are either diffuse or concentrated. The latter, coming mainly from the industrial sectors and energy production, could be exploited as raw materials when the CO2 capture systems are operational. The chemical valorization of CO2 as a raw material is not a new idea and substantial research works dating from the 1980s attempt to lead to innovative synthetic routes. In the last decade, the decrease in the availability of fossil resources, have led to a renewed interest in this the valorization of CO2. Consequently, the chemical valorization of CO₂ encounters inherent challenges, most notably the substantial capital requirements and high implementation costs. Addressing these barriers necessitates the development of viable technological and economic strategies. However, the catalytic hydrogenation of CO2 into value-added chemicals and alternative fuels is one of the attractive green approaches. Even though external energy consumption is a crucial impediment, but deemed possible to overcome. Thus, it is possible to take advantage of the commercial potential of CO2 by exploiting it as a raw material in new chemical and industrial applications. In this present scientific endeavor, the state of the art and recent trends CO2 valorization into valuable products like methanol, ethanol, formic acid, syngas, hydrocarbons, dimethyl ether, urea and its mineralization process are reviewed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".