Temperature and Ramp Rate-Controlled Phase Engineering of Copper Oxides in Non-Crystalline Carbon Tubes for Enhanced Alkaline Water Splitting
Bibliographic record
Abstract
Trapping active metal oxides within noncrystalline carbon tubes (nCTs) offers unique advantages for electrocatalysis, including abundant dangling bonds, a high surface-to-volume ratio, excellent conductivity, and enhanced long-term durability. However, precise control of the oxide phase during heat treatment remains a key challenge. Herein, we systematically investigate the phase composition of copper oxides (Cu x O, x = 0,1,2) confined within nCTs, depending on temperature and ramp rate, and explore their application in electrochemical alkaline water splitting. Comprehensive spectroscopic and microscopic analyses reveal that both parameters critically influence the structural and compositional integrity of the composites. Among the synthesized materials, the optimized electrode, nCT–Cu 2 O/CuO, delivers superior electrocatalytic performance, achieving a current density of 10 mA/cm 2 at relatively low overpotentials of 168 mV for the hydrogen evolution reaction (HER) and 284 mV for the oxygen evolution reaction (OER). Favorable Tafel slopes of 73 for HER and 62 mV/dec for OER further underscore its excellent kinetics. The electrolyzer based on the as-synthesized electrodes delivers a current density of 10 mA/cm 2 at a cell potential as low as 1.57 V. Experimental observations, supported by density functional theory calculations for both HER and OER, attribute these outstanding properties to the combined effects of the well-dispersed CCO nanoparticles within the nCT matrix, which enhance active site density, promote charge transfer, and introduce confinement effects. This work presents a cost-effective and scalable strategy for fabricating efficient bifunctional electrodes, offering insights for advancing sustainable hydrogen production technologies.
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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".