Electrochemical Reduction of CO<sub>2</sub> by SnO<sub><i>x</i></sub> Nanosheets Anchored on Multiwalled Carbon Nanotubes with Tunable Functional Groups
Bibliographic record
Abstract
Abstract Sn‐based electrocatalysts are promising for the electrochemical CO2 reduction reaction (CO2RR), but suffer from poor activity and selectivity. A hierarchical structure composed of ultrathin SnOx nanosheets anchored on the surface of the commercial multiwalled carbon nanotubes (MWCNTs) is synthesized by a simple hydrothermal process. The electrocatalytic performance can be further tuned by functionalization of the MWCNTs with COOH, NH2, and OH groups. Both SnOx@MWCNTs−COOH and SnOx@MWCNTs−NH2 show excellent catalytic activity for CO2RR with nearly 100 % selectivity for C1 products (formate and CO). SnOx@MWCNTs−COOH has favorable formate selectivity with a remarkably high faradaic efficiency (FE) of 77 % at −1.25 V versus standard hydrogen electrode (SHE) and a low overpotential of 246 mV. However, SnOx@MWCNTs−NH2 manifests increased selectivity for CO with higher current density. Density functional theory calculations and experimental studies demonstrate that the interaction between Sn species and functional groups play an important role in the tuning of the catalytic activity and selectivity of these functionalized electrocatalysts. SnOx@MWCNTs−COOH and SnOx@MWCNTs−NH2 both effectively inhibit the hydrogen evolution reaction and prove stable without any significant degradation over 20 h of continuous electrolysis at −1.25 V versus SHE.
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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".