Opportunities and Challenges for Catalysis in Carbon Dioxide Utilization
Bibliographic record
Abstract
The environmental and societal consequences of the increasing levels of carbon dioxide in our atmosphere are among the most significant challenges society currently faces. Carbon dioxide utilization, in which carbon dioxide is either used directly or converted into more valuable products, is likely to be one component of a broad strategy to reduce carbon dioxide emissions, a challenge that will require both technological and policy changes. Catalysis is crucial to the successful conversion of carbon dioxide into value-added products. Here, we provide a review on chemical and biological systems for carbon dioxide conversion directed toward the readers of ACS Catalysis, which focuses on providing a general perspective on the field, rather than technical details. We discuss both challenges related to the conversion of carbon dioxide into specific products such as carbon monoxide, formic acid, methanol, methane, ethylene, fuels, carboxylic acids, and polymers as well as general challenges for the field. We also compare and contrast different methods for carbon dioxide conversion, for example homogeneous versus heterogeneous catalysis or photosynthetic versus nonphotosynthetic biological conversion, and highlight areas where one approach may have advantages over another. In a concluding section, we identify problems related to carbon dioxide conversion that will need to be addressed for technology to be both viable and reduce carbon dioxide emissions.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".