A simple one-pot method for growing metal (Co, Ni, Cu) oxide naoparticles on N-doped reduced graphene oxide and their application in lithium-ion storage
Bibliographic record
Abstract
Abstract Nanomaterials have been used as the electrodes of lithium-ion batteries during the past years. Designing nanomaterials with a simple method is significant for saving the cost of lithium-ion batteries that will be applied in electric vehicles and portable electronic devices. In this study, a simple one-pot strategy was proposed to grow MOx (M = Co, Ni, Cu) metal oxide nanoparticles on N-doped reduced graphene oxide (rGO) sheets. The MOx/N-doped rGO composites were subsequently studied as the anodes of lithium-ion batteries. Results demonstrated that the one-pot method effectively prevents the aggregation of nanoparticles and the restacking of graphene sheets, which is important to improve the electrochemical performance of composites. Electrochemical measurements of the three composites as anode materials exhibited the high reversible capacitance, rate capability, and cyclic stability. The proposed simple one-pot method may provide researchers and industry with a new strategy to manufacture cost-effective graphene-based nanomaterials for high performance anodes of lithium-ion batteries.
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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) | 0.001 | 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".