Electrochemical Reduction of Carbon Dioxide at TiO<sub>2</sub>/Au Nanocomposites
Bibliographic record
Abstract
Herein, we report on the facile synthesis of nanocomposite consisting of TiO 2 and Au nanoparticles (NPs) via a tailored galvanic replacement reaction (GRR). The electrocatalytic activity of the synthesized TiO 2 /Au nanocomposites for CO 2 reduction was investigated in an aqueous solution using various electrochemical methods. Our results demonstrated that the TiO 2 /Au nanocomposites formed through the GRR process exhibited improved catalytic activities for CO 2 reduction, while generating more hydrocarbon molecules than the typical formation of CO in contrast to polycrystalline Au. GC analysis and NMR spectroscopy revealed that CO and CH 4 were the gas products, whereas HCOO –, CH 3 COO –, CH 3 OH, and CH 3 CH 2 OH were the liquid products from the CO 2 reduction at different cathodic potentials. This remarkable change was further studied using the density functional theory (DFT) calculations, showing that the TiO 2 /Au nanocomposites may increase the binding energy of the formed · CO intermediate and reduce the free energy compared to Au, thus favoring the downstream generation of multicarbon products. The TiO 2 /Au nanocomposites have high catalytic activity and excellent stability and are easy to fabricate, indicating that the developed catalyst has potential application in the electrochemical reduction of CO 2 to value-added products.
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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 | 0.001 | 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".