Emerging Trends in Sustainable CO<sub>2</sub>‐Management Materials
Bibliographic record
Abstract
Abstract With the rising level of atmospheric CO 2 worsening climate change, a promising global movement toward carbon neutrality is forming. Sustainable CO 2 management based on carbon capture and utilization (CCU) has garnered considerable interest due to its critical role in resolving emission‐control and energy‐supply challenges. Here, a comprehensive review is presented that summarizes the state‐of‐the‐art progress in developing promising materials for sustainable CO 2 management in terms of not only capture, catalytic conversion (thermochemistry, electrochemistry, photochemistry, and possible combinations), and direct utilization, but also emerging integrated capture and in situ conversion as well as artificial‐intelligence‐driven smart material study. In particular, insights that span multiple scopes of material research are offered, ranging from mechanistic comprehension of reactions, rational design and precise manipulation of key materials (e.g., carbon nanomaterials, metal–organic frameworks, covalent organic frameworks, zeolites, ionic liquids), to industrial implementation. This review concludes with a summary and new perspectives, especially from multiple aspects of society, which summarizes major difficulties and future potential for implementing advanced materials and technologies in sustainable CO 2 management. This work may serve as a guideline and road map for developing CCU material systems, benefiting both scientists and engineers working in this growing and potentially game‐changing area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".