Application of Porous Materials for CO2 Reutilization: A Review
Bibliographic record
Abstract
CO2 reutilization processes contribute to the mitigation of CO2 as a potent greenhouse gas (GHG) through reusing and converting it into economically valuable chemical products including methanol, dimethyl ether, and methane. Solar thermochemical conversion and photochemical and electrochemical CO2 reduction processes are emerging technologies in which solar energy is utilized to provide the energy required for the endothermic dissociation of CO2. Owing to the surface-dependent nature of these technologies, their performance is significantly reliant on the solid reactant/catalyst accessible surface area. Solid porous structures either entirely made from the catalyst or used as a support for coating the catalyst/solid reactants can increase the number of active reaction sites and, thus, the kinetics of CO2 reutilization reactions. This paper reviews the principles and application of porous materials for CO2 reutilization pathways in solar thermochemical, photochemical, and electrochemical reduction technologies. Then, the state of the development of each technology is critically reviewed and evaluated with the focus on the use of porous materials. Finally, the research needs and challenges are presented to further advance the implementation of porous materials in the CO2 reutilization processes and the commercialization of the aforementioned technologies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".