Interfacial Electric Field Sharping by Non‐Ionic Halogens Enables Selective CO <sub>2</sub> ‐to C <sub>2+</sub> Electroreduction
Bibliographic record
Abstract
ABSTRACT Halide ions are widely employed to accelerate the electrochemical CO 2 reduction reaction (CO 2 RR), but their practical application is hampered by site blocking and electrode corrosion. Here we present an organic‐inorganic hybrid (OIH) strategy that embeds non‐ionic halogenated molecules (C 8 H 17 X, X = Cl, Br, I) into the Cu 2 O matrix to shape interfacial electric fields. This design preserves the kinetic benefits of halides while eliminating instability from ionic incorporation. The optimized C 8 H 17 Cl‐Cu 2 O OIHs delivers an outstanding C 2+ Faradaic efficiency of 80.6% with a partial current density of 161.2 mA cm −2 . Spectroscopic and theoretical analyses reveal that non‐ionic halogens act as molecular “field shapers,” generating strong interfacial electric fields that strengthen *CO adsorption and balance atop/bridge configurations. This tailored *CO landscape promotes efficient C–C coupling by simultaneously increasing coverage and dimerization kinetics. Our findings establish non‐ionic halogen modifiers as a robust platform for stabilizing and steering interfacial electric fields in CO 2 RR, offering a general strategy for designing selective and durable electrocatalysts.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".