Prospects of Sunlight Driven Air-to-Methanol Synthesis via CO2 Electrolysis
Bibliographic record
Abstract
The race to save planet earth has led to significant advancement in technologies for harvesting renewable energy, carbon capture and conversion. Futures scenarios are being envisioned where CO 2 is captured from air and converted to valuable fuels and chemicals, with methanol (MeOH) being the most coveted product. Here we assess two potential air-to-MeOH pathways that harvest solar power via concentrated photovoltaic (CPV) cells for direct air capture (DAC) of CO 2 and subsequent conversion to MeOH by exploiting CO 2 electrolysis. Specifically, we perform techno-economic and life-cycle analysis on single-step (direct CO 2 -to-MeOH electrolysis) and three-step (integration of H 2 O electrolysis, CO 2 -to-CO electrolysis, and hydrogenation reactor) air-to-MeOH routes. Our results indicate that in current scenario, the envisioned air-to-MeOH routes are not economically and environmentally compelling with high levelized costs of MeOH ~1180–1730 $/ton MeOH and CO 2 emissions of ~2.29–2.69 /ton MeOH . Using sensitivity analysis, we reveal targets for CPV capital cost ($290/kW), DAC capital cost ($375/(ton-CO 2 /year)), and electricity emission intensity (<275 kg-CO 2 /MWh) which will make the three-step route commercially and environmentally viable as a near-term technology. In contrast, direct CO 2 -to-MeOH electrolysis will need drastic performance improvement to be economically competitive, with required current densities >300 mA/cm 2 , energy efficiency >45% and stack stability >2 years. We hope this study will garner the key stakeholders to advance discussions about the cost and potential of this envisioned air-to-fuel technology.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".