γ-Fe<sub>2</sub>O<sub>3</sub>@CNTs Anode Materials for Lithium Ion Batteries Investigated by Electron Energy Loss Spectroscopy
Bibliographic record
Abstract
Abstract Atomic layer deposition was employed to deposit maghemite (γ-Fe2O3) nanoparticles on carbon nanotubes (CNTs) to prepare the γ-Fe2O3@CNTs composites, which exhibit a superior lithium storage performance as the anode of lithium ion batteries (LIBs). The high reversible capacity of 859.7 mA h/g was observed after 400 cycles at a current density of 500 mA/g. Even at the high current density of 10000 mA/g, the specific cyclic capacity of 464.4 mA h/g can still be obtained. Furthermore, electron energy loss spectroscopy results reveal that the Fe chemical state plays a critical role in the evolution of the capacity of γ-Fe2O3@CNTs composite anodes during the cycling process. The incomplete conversion of the chemical state in γ-Fe2O3 reduces the capacity, while the recovery of the chemical state of γ-Fe2O3 during the cycling process may cause the increase in capacity. This work provides insight into understanding the detailed working mechanism of transition metal oxides in LIBs, which helps in the design of electrode materials with promising lithium storage performance.
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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".