A Review of Carbon Capture and Valorization Technologies
Bibliographic record
Abstract
Global fossil fuel consumption has induced emissions of anthropogenic carbon dioxide (CO2), which has emanated global warming. Significant levels of CO2 are released continually into the atmosphere from the extraction of fossil fuels to their processing and combustion for heat and power generation including the fugitive emissions from industries and unmanaged waste management practices such as open burning of solid wastes. With an increase in the global population and the subsequent rise in energy demands and waste generation, the rate of CO2 release is at a much faster rate than its recycling through photosynthesis or fixation, which increases its net accumulation in the atmosphere. A large amount of CO2 is emitted into the atmosphere from various sources such as the combustion of fossil fuels in power plants, vehicles and manufacturing industries. Thus, carbon capture plays a key role in the race to achieve net zero emissions, paving a path for a decarbonized economy. To reduce the carbon footprints from industrial practices and vehicular emissions and attempt to mitigate the effects of global warming, several CO2 capturing and valorization technologies have become increasingly important. Hence, this article gives a statistical and geographical overview of CO2 and other greenhouse gas emissions based on source and sector. The review also describes different mechanisms involved in the capture and utilization of CO2 such as pre-combustion, post-combustion, oxy-fuels technologies, direct air capture, chemical looping combustion and gasification, ionic liquids, biological CO2 fixation and geological CO2 capture. The article also discusses the utilization of captured CO2 for value-added products such as clean energy, chemicals and materials (carbonates and polycarbonates and supercritical fluids). This article also highlights certain global industries involved in progressing some promising CO2 capture and utilization techniques.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".