Electrochemical Performance of Hollow &alpha;-Fe<sub>2</sub>O<sub>3</sub> Spheres as Anode Material for Lithium-ion Battery
Bibliographic record
Abstract
α-Fe2O3 spheres were synthesized by a facile hydrothermal method followed by a calcination step. The crystalline structure and morphology of the synthesized materials were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The morphology of the sample consisted of porous hollow spheres that ranged about hundreds of nanometers and were composed of well crystallized nanoparticles about a dozen nm. The electrochemical properties of the sample were evaluated by cyclic voltammetry (CV) and charge/discharge measurements. The discharge/charge capacities in the first cycle achieved 1336/934 mAh g-1 at the rate of 0.2 C. The reversible capacity in the 50th cycle remained 840 mAh g-1 with impressive retention rate of 90%. This good lithium storage property was probably ascribed to the porous and hollow structure and nanoscale α-Fe2O3 particles, which enlarged the surface area and shortened the pathway for lithium ion migration. The appealing electrochemical capability indicated the potential implementation of hollow Fe2O3 spheres as anode material for future lithium-ion battery.
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".