Heterogeneous catalytic hydrogenation of CO<sub>2</sub>by metal oxides: defect engineering – perfecting imperfection
Bibliographic record
Abstract
Metal oxides with their myriad compositions, structures and bonding exhibit an incredibly diverse range of properties. It is however the defects in metal oxides that endow them with a variety of functions and it is the ability to chemically tailor the type, population and distribution of defects on the surface and in the bulk of metal oxides that delivers utility in different applications. In this Tutorial Review, we discuss how metal oxides with designed defects can be synthesized and engineered, to enable heterogeneous catalytic hydrogenation of gaseous carbon dioxide to chemicals and fuels. If this approach to utilization and valorization of carbon dioxide could be developed at industrially significant rates, efficiencies and scales and made economically competitive with fossil-based chemicals and fuels, then carbon dioxide refineries envisioned in the future would be able to contribute to the reduction of greenhouse gas emissions, ameliorate climate changes, provide energy security and enable protection of the environment. This would bring the vision of a sustainable future closer to reality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".