Novel cobalt quantum dot/graphene nanocomposites as highly efficient electrocatalysts for water splitting
Bibliographic record
Abstract
A cost-effective, non-noble metal based high-performance electrocatalyst for the oxygen evolution reaction (OER) is critical to energy conversion and storage processes. Here, we report on a facile and effective in situ strategy for the synthesis of an advanced nanocomposite material that is comprised of cobalt quantum dots (Co QDs, ∼3.2 nm), uniformly dispersed on reduced graphene oxide (rGO) as a highly efficient OER electrocatalyst platform. This nanocomposite electrocatalyst afforded a mass activity of 1250 A g(-1) at a low overpotential (η) of 0.37 V, a small Tafel slope of ∼37 mV dec(-1) and a turnover frequency (TOF) of 0.188 s(-1) in 0.1 M KOH, comparing favorably with state-of-the-art RuO2, IrO2 and Pt/C catalysts. The synergy between abundant catalytically active sites through the fine dispersion of Co QDs, and enhanced electron transfer generated from the graphene resulted in first-rate electrocatalytic properties toward the OER. These merits coupled with the higher stability of the nanocomposite hold great promise for triggering breakthroughs in electrocatalysis for water splitting.
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) | 0.000 | 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".