Translating Tactics from Direct CO<sub>2</sub> Electroreduction to Electroorganic Coupling Reactions with CO<sub>2</sub>
Bibliographic record
Abstract
Sustainable electrosynthesis technologies are rapidly developing stimulated by the drive for sustainable chemical manufacturing and the increasingly accessible renewable electricity prices. The electrochemical utilization of easily available feedstock, such as carbon dioxide (CO 2 ), has attracted significant attention as it can additionally help closing the disrupted carbon cycle. While direct CO 2 reduction has benefited from recent advancements in the catalyst, electrolyte and system design, developments in electrochemical coupling of CO 2 with organic precursors to yield value‐added chemicals have been lagging behind due to the apparent disconnect between the direct CO 2 reduction and the organic electrosynthesis communities. Currently, electrocarboxylation reactions require high operating voltages, show low current densities, limited selectivity towards target products and are associated with low atom economy due to the reliance on sacrificial anode dissolution. Advancing this indirect electrochemical CO 2 utilization strategy will enable sustainable synthesis of valuable chemicals including non‐steroidal anti‐inflammatory drugs and precursors for plasticizers and commercially‐relevant polymers—all of which are currently produced with high carbon footprint and low atom economy. This perspective discusses the current state‐of‐the‐art in electroorganic synthesis with CO 2 as a one‐carbon synthon and suggests several transferrable strategies from direct CO 2 reduction breakthroughs to advance electrocarboxylation and bring it closer to industrial implementation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".