Energy-conservative CO2 electroreduction for efficient formate co-generation
Bibliographic record
Abstract
Developing energy-conservative and highly efficient electrochemical systems is an appealing approach to co-generating value-added chemicals while simultaneously reducing energy input. In this work, an integrated electrochemical system combining CO 2 electroreduction (CO 2 RR) and polyethylene terephthalate (PET) plastic upcycling is developed for the efficient co-generation of formate. The cathodic electrocatalyst, comprising BiOI nanoclusters confined within carbon nanospheres, demonstrates excellent selectivity and stability for formate formation. The strong electronic metal-support interaction between BiOI nanoclusters and carbon substrate not only enhances CO 2 adsorption and charge transfer capabilities but also effectively modulates the electronic structure to facilitate CO 2 RR. Coupled with a highly active anodic electrocatalyst, NiCo 2 O 4 nanosheets grown on Ni foam, this integrated system achieves a formate Faradaic efficiency (FE) of 90 % at the cathode and 85 % at the anode at 250 mA cm⁻² over 140 hours. Techno-economic analysis further underscores the system’s economic feasibility and underscores its significant potential for commercial applications. • High performance in formate formation is achieved at both the cathode and the anode. • Strong electronic metal-support interaction (ESMI) enhances electrocatalyst durability. • Polyethylene terephthalate (PET) plastic upcycling significantly increases anodic product value. • An energy-efficient electrochemical system demonstrating economic feasibility has been developed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".