Graphene-Wrapped ZnMn<sub>2</sub>O<sub>4</sub> Nanoparticles with Enhanced Performance as Lithium-Ion Battery Anode Materials
Bibliographic record
Abstract
ZnMn2O4 nanoparticles (NPs) wrapped by reduced graphene oxide (rGO) were fabricated via a two-step solvothermal method and used as an anode material for lithium-ion batteries (LIBs). Compared to pure ZnMn2O4, the ZnMn2O4 NPs/rGO composites deliver higher capacities of 1230 mAh g−1 and 578 mAh g−1 after 200 cycles at a current density of 100 mA g−1 and 500 mA g−1, respectively. The enhanced electrochemical performance of ZnMn2O4 NPs/rGO composites is mainly attributed to a distinctive structure (ZnMn2O4 NPs surrounded by flexible rGO), which can promote the diffusion of Li+, accelerate the transport of electrons and buffer volume expansion during the Li+ insertion/extraction process. Furthermore, the rGO sheets can effectively prevent the agglomeration of ZnMn2O4 NPs, thus, improving structural stability of the composites. The excellent electrochemical performance indicates that such ZnMn2O4 NPs/rGO composite structure has a great potential for high-performance LIBs.
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".