Scaling CO2 Capture With Downstream Flow CO2 Conversion to Ethanol
Bibliographic record
Abstract
To prevent the global average temperature from increasing more than 1.5°C and lower the concentration of greenhouse gases (GHGs) in the atmosphere, most emissions trajectories necessitate the implementation of strategies that include both GHG mitigation and negative emissions technologies (NETs). For NETs, there are unique research challenges faced by both CO 2 capture and utilization to scale in an economically feasible manner. Starting with incumbent methods, wherein CO 2 is recovered from a high concentration source, and moving toward CO 2 capture from more widely available dilute sources, we outline how CO 2 capture systems interface with downstream utilization in flow reactors. To provide a real-world point of comparison, we analyze CO 2 sourcing for Air Company's CO 2 -to-alcohols pilot and demonstration scale deployments in Brooklyn, New York, USA and Calgary, Alberta, Canada as case studies. The degree of reduction in atmospheric CO 2 depends on product alcohol usage; for example, use as a fixed chemical feedstock provides longer term emissions reduction than as a fuel, which is eventually oxidized. Lastly, we discuss the barriers that are present for economic scale-up of CO 2 capture and utilization technologies broadly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".