Porosity as a Design Element for Developing Catalytic Molecular Materials for Electrochemical and Photochemical Carbon Dioxide Reduction
Bibliographic record
Abstract
Abstract The catalytic reduction of carbon dioxide (CO2) using sustainable energy inputs is a promising strategy for upcycling of atmospheric carbon into value‐added chemical products. This goal has inspired the development of catalysts for selective and efficient CO2 conversion using electrochemical and photochemical methods. Among the diverse array of catalyst systems designed for this purpose, 2D and 3D platforms that feature porosity offer the potential to combine carbon capture and conversion. Included are covalent organic frameworks (COFs), metal–organic frameworks (MOFs), porous molecular cages, and other hybrid molecular materials developed to increase active site exposure, stability, and water compatibility while maintaining precise molecular tunability. This mini‐review showcases catalysts for the CO2 reduction reaction (CO2RR) that incorporate well‐defined molecular elements integrated into porous materials structures. Selected examples provide insights into how different approaches to this overall design strategy can augment their electrocatalytic and/or photocatalytic CO2 reduction activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".