Coupling of Various Aqueous Anodic Reactions with Direct and Indirect Electroreduction of CO<sub>2</sub> in Organic Media
Bibliographic record
Abstract
The electrochemical reduction of CO2 (CO2RR) to useful chemicals such as hydrocarbons, alcohols, and carboxylic acids is a promising CO2 utilization strategy. However, CO2RR in aqueous media requires high electricity input due to the chemical inertness of CO2, its poor solubility in water, and the reliance on the sluggish oxygen evolution reaction (OER) at the anode. Here, we investigate how alternative reaction designs within a CO2 electrolyzer can address these issues. Specifically, we explore the effect of changing catholyte medium, cathodic reaction pathway, and oxidizing substrates at the anode on the electrolyzer performance and energy requirement. First, we perform CO2RR in aprotic electrolytes that not only increase the solubility of CO2, but also eliminate competing hydrogen evolution reaction. Second, we introduce organic precursors (organic halides) in aprotic media to produce electrochemically generated nucleophiles, which can react with CO2 at lower potential than required for direct CO2 activation, yielding a variety of useful carboxylic acid (indirect CO2RR). Third, we couple both direct and indirect CO2RR in organic medium with aqueous anodic reactions associated with wastewater treatment (urea and ammonia electrooxidation) that require a significantly lower energy input than OER. We investigate the advantages and limitations of ion-exchange membranes of different types that render efficient aprotic and aqueous compartment segregation to conduct these reactions within a single cell. We also elucidate the effects of the electrolyzer operating parameters (applied potential, organic precursor concentration, temperature) on the efficiency and selectivity of CO2RR. In addition, we propose a detailed mechanism of direct and indirect CO2RR in organic electrolytes based on density functional theory calculations and extensive experimental results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".