Electrochemical Reduction of CO<sub>2</sub> at Coinage Metal Nanodendrites in Aqueous Ethanolamine
Bibliographic record
Abstract
Abstract Electrocatalytic reduction of CO2 into usable chemicals is a promising path to address climate change and energy challenges. Herein, we demonstrate the synthesis of unique coinage metal (Cu, Ag, and Au) nanodendrites (NDs) via a facile galvanic replacement reaction (GRR), which can be effective electrocatalysts for the reduction of CO2 in an ethanolamine (EA) solution. Each metal ND surface was directly grown on glassy‐carbon (GC) substrates from a mixture of Zn dust and the respective precursor solution. The electrocatalytic activities of the synthesized ND surfaces were optimized for CO2 reduction in EA solution by varying their composition. It was determined that a 0.05 mol fraction of EA exhibited the highest catalytic activity for all metal NDs. Linear sweep voltammetry (LSV) and electrochemical impedance spectroscopy (EIS) techniques showed that metal‐ND electrodes possessed higher current densities, lower onset potentials and lower charge‐transfer resistances for CO2 reduction than their smooth polycrystalline electrode counterparts, indicating improved CO2 reduction catalytic activity. It was determined, using FTIR and NMR spectroscopy, that formate was produced as a result of the CO2 reduction.
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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 | 0.000 |
| 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 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".