1D/2D Cobalt‐Based Nanohybrids as Electrocatalysts for Hydrogen Generation
Bibliographic record
Abstract
Abstract The synergistic effects derived from optimizing the chemical and structural features of electrocatalysts permit them to attain remarkable activity and stability. Herein, 1D/2D cobalt‐based nanohybrid (CoNH) electrodes are developed; the structural design consists of Co3O4 electrospun nanoribbons (NRs) deposited onto a carbon fiber paper substrate where Co3O4 nanosheets are subsequently grown via an electrodeposition step and UV/ozone treatment. The content of noncovalently functionalized carbon nanotubes within the Co3O4 NRs is first tuned to enhance their charge transfer properties and mechanical stability. The electrocatalytic activity of the electrodes is further improved by a phosphorus modification of the 1D NRs, resulting in the formation of NaCoPO4. The optimized 1D/2D CoNH electrode, i.e., ED‐0.09 wt% fCNTs/P‐CoNHs, displays a similar performance to that of platinum in 0.25 m Na2S/0.35 m Na2SO3 (Tafel slope ≈102 mV dec−1 for the former and ≈96 mV dec−1 for the latter) and outstanding stability for up to 48 h. The versatility and high activity of this electrode is also demonstrated according to tests in a conventional water splitting system (cell voltage 1.55V, to produce 10 mA cm−2) and a solar‐driven electrolyzer (1 m KOH).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".