Electrocatalytic Reactions for Converting CO2 to Value-Added Products: Recent Progress and Emerging Trends
Bibliographic record
Abstract
Carbon dioxide (CO2) emissions are an important environmental issue that causes greenhouse and climate change effects on the earth. Nowadays, CO2 has various conversion methods to be a potential carbon resource, such as photocatalytic, electrocatalytic, and photo-electrocatalytic. CO2 conversion into value-added products has many advantages, including facile control of the reaction rate by adjusting the applied voltage and minimal environmental pollution. The development of efficient electrocatalysts and improving their viability with appropriate reactor designs is essential for the commercialization of this environmentally friendly method. In addition, microbial electrosynthesis which utilizes an electroactive bio-film electrode as a catalyst can be considered as another option to reduce CO2. This review highlights the methods which can contribute to the increase in efficiency of carbon dioxide reduction (CO2R) processes through electrode structure with the introduction of various electrolytes such as ionic liquid, sulfate, and bicarbonate electrolytes, with the control of pH and with the control of the operating pressure and temperature of the electrolyzer. It also presents the research status, a fundamental understanding of carbon dioxide reduction reaction (CO2RR) mechanisms, the development of electrochemical CO2R technologies, and challenges and opportunities for future research.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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 | 0.002 |
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".