Two Targets with One Shot: Cost-Efficient Electrochemical CO<sub>2</sub> Reduction Coupled with the Synthesis of Cu- and Zn-Based Metal–Organic Frameworks
Bibliographic record
Abstract
To address the primary issue associated with electrochemical CO 2 reduction reaction (eCO 2 RR) utilizing conventional electrolyzers such as low-energy efficiency, we present a techno-economically paired electrolyzer for eCO 2 RR, which is efficiently coupled with the electrochemical synthesis of Zn- or Cu-based metal–organic frameworks (MOFs) under mild conditions. The electro-oxidative generation of Zn 2+ or Cu 2+ in the presence of either H 3 BTC, H 2 BDC, or H 2 AIP (5-aminoisophthalic acid) as linker leads to the in situ formation of the respective MOFs in the anodic compartment. Conversely, the cathodic part is equipped with a high-performance hybrid electrocatalyst composed of cobalt phthalocyanine (CoPc) and homemade N-doped ionic liquid-derived ordered mesoporous carbons (GIOMC and IFMC). It was interestingly found that OMCs, regardless of whether they contained high or low nitrogen content, exhibited strong affinity for interacting with CoPc. This interaction resulted in the uniform distribution of CoPc, which is of paramount importance for achieving enhanced electrocatalytic performance. These studies indicate that the introduced hybrid catalysts (CoPc@GIOMC and CoPc@IFMC) demonstrate suitable stability and electrochemical performance, even after prolonged electrolysis periods. The MOFs prepared using both controlled potential and constant-current protocols strongly emphasize the successful integration of oxidatively generated MOFs with eCO 2 RR in an efficient manner. The structural analysis of the synthesized MOFs demonstrated excellent alignment with those prepared using solvothermal methods. To the best of our knowledge, this work represents the first example of utilizing the paired eCO 2 RR in conjunction with the simultaneous electrosynthesis of MOFs under mild and environmentally friendly conditions.
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 | 0.000 | 0.000 |
| 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".