Electrified hydrocarbon-to-oxygenates coupled to hydrogen evolution for efficient greenhouse gas mitigation
Bibliographic record
Abstract
Abstract Chemicals manufacture is among the top greenhouse gas contributors. More than half of the associated emissions are attributable to the sum of ammonia plus oxygenates such as methanol, ethylene glycol and terephthalic acid. Here we explore the impact of electrolyzer systems that couple electrically-powered anodic hydrocarbon-to-oxygenate conversion with cathodic H2 evolution reaction from water. We find that, once anodic hydrocarbon-to-oxygenate conversion is developed with high selectivities, greenhouse gas emissions associated with fossil-based NH3 and oxygenates manufacture can be reduced by up to 88%. We report that low-carbon electricity is not mandatory to enable a net reduction in greenhouse gas emissions: global chemical industry emissions can be reduced by up to 39% even with electricity having the carbon footprint per MWh available in the United States or China today. We conclude with considerations and recommendations for researchers who wish to embark on this research direction.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".