Gold Nanocrystals and Conjugated Polymer-Wrapped Single-Walled Carbon Nanotube Composites for Catalyzing CO<sub>2</sub> Electroreduction
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Metal nanocrystals (NCs) have been synthesized and used as highly efficient electrocatalysts for the electrocatalytic CO 2 reduction reaction (eCO 2 RR) in recent years. Electrocatalysts with various metal sizes and morphologies have achieved remarkable improvement in the CO 2 reduction. However, the syntheses are typically energy-demanding and the catalysts exhibit low mass activity. Ultrasmall metal NCs synthesized by a one-step process at room temperature and an ambient environment stand out because of cost-effectiveness in materials and energy. Here, we report a facile synthesis to produce 1 nm gold nanocrystals in ambient conditions, which was realized by an in situ reduction of AuCl 3 on a semiconducting single-walled carbon nanotube (sc-SWCNT) surface. In addition, the bipyridine (BPy) units in tube-wrapped polymers function as chelating sites for anchoring Au 3+ and AuNCs. The ultrasmall size of AuNCs was achieved by fast AuCl 3 diffusion and the anchoring function of BPy units. A slow diffusion or absence of BPy units resulted in AuNCs in larger sizes. The NC density was controlled by AuCl 3 feed amounts by varying the Au-to-BPy molar ratios. Density functional theory was applied to simulate the Au-BPy coordination. The fabricated nanocomposites exhibited a high Faradaic CO selectivity up to 86% at 25 mA/cm 2 and a high mass activity up to 5.61 A/mg (Au) at 100 mA/cm 2, which is the highest value reported so far in AuNC electrocatalysts for CO 2 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 | 0.001 | 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".