(Invited) Electrochemical and in Situ FTIR Spectroscopic Studies of CO<sub>2</sub> Reduction at 3D Nanostructured Catalysts
Bibliographic record
Abstract
There is a growing interest in developing high-performance catalysts for the electrochemical reduction of carbon dioxide (CO2) to address the increasingly serious impacts of global climate change. In this talk, we report on the design of advanced three-dimensional (3D) nanomaterials (e.g., nanoporous gold, Cu nanodendrites and Co nanodendrites) for the efficient electrochemical reduction of CO2. The morphology, composition and structure of the synthesized 3D nanomaterials were characterized with various imaging and spectroscopic techniques, including FE-SEM, XRD, EDX and XPS. The effects of an applied potential on the electrochemical reduction of CO2 were investigated using various electrochemical methods. The products generated from the CO2 electrochemical reduction were identified by gas chromatography and nuclear magnetic resonance (NMR) spectroscopy. The kinetics of the CO2 reduction reaction at the 3D nanomaterials was further studied using in situ electrochemical Fourier transform infrared (FTIR) spectroscopy. The critical roles of nanostructured surfaces in the electrochemical reduction of CO2 are discussed.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".