Bioconversion of <scp>CO<sub>2</sub></scp> and potential of gas fermentation for mainstream applications: Critical advances and engineering challenges
Bibliographic record
Abstract
Abstract The undesirable consequences of climate change are attributed to the ever‐increasing emissions of greenhouse gases, especially carbon dioxide. For the sustainable development of human society, there is an urgent need to develop novel techniques for efficient conversion and utilization of carbon dioxide. Hence, carbon capture and storage, artificial bioconversion of carbon dioxide are topics of great interest for current researchers across the globe. Here, we elucidate the different techniques of carbon fixation, which fall under the broad categories of natural fixation pathways, thermo‐catalytic conversion techniques, and synthetic carbon fixation pathways. Based on a comparative analysis, gas fermentation is a promising method for the microbial conversion of CO2‐containing gases into fuels and chemicals. However, for mainstream applications, in‐depth reviews of different reactor configurations and the various engineering aspects related to process development are still lacking. We have analyzed the published literature relating to artificial bioconversion of carbon dioxide and focused on the factors like equipment selection and reactor design that can assist in scale‐up of gas fermentation technology. The review provides an in‐depth understanding of engineering aspects related to stirred tank and bubble column reactors, focusing on the technical design parameters and discussing the conditions for optimal operation and performance during commercial gas fermentation processes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".