Electrochemical Performance of Cupric Oxide Loaded Carbon Nanotubes as Electrode Material for CO2 Reduction
Bibliographic record
Abstract
This study is conducted to explore the effective and stable electrode materials for electrochemical reduction of CO2. The different compositions (3 - 8%) CuO-supported CNT samples were prepared by a traditional sol-gel method, and the prepared materials were characterized by TGA, SEM, TEM, FTIR and XRD. The characterization results confirmed the uniform impregnation of CuO on the defects of the CNT's, which improved the utilization of meso and microporous distribution in the prepared electrocatalyst. The high surface area and stability of CuO- CNTs allowed dual conduction of electron and intermediate species with high current density at minimum energy supplied. Linear sweep voltammetry and Chronoamperometry were used to measure the electrode's current density and stability, respectively, and 8 % CuO- CNT was found most stable and progressive composition to reduce the CO2 efficiently. Further, the liquid products were analyzed using the gas chromatography, and 20 % faradic efficiency of methane was measured.
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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 | 0.001 | 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".