Investigation of the Lithiation Mechanism of Fe <sub>3</sub> O <sub>4</sub> ‐Based Composite Anode: the Effect of the Carbon Matrix
Bibliographic record
Abstract
Abstract In this work, the Fe 3 O 4 /CNTs composite and Fe 3 O 4 /C composite were synthesized by a facile hydrothermal method without any reducing agents and pyrolysis of glucose, respectively, and the process of the first lithiation of commercial Fe 3 O 4 , Fe 3 O 4 /CNTs and Fe 3 O 4 /C electrode were systematically investigated by EIS as function of electrode potential. It is found that at intermediate degrees of intercalation, the characteristic Nyquist plots of Fe 3 O 4 electrode are composed of three parts, namely, the small semicircle in high‐frequency region can be attributed to the migration of lithium‐ion through SEI film (the resistance of SEI film coupled with SEI film capacitance) as well as contact problems; the semicircle in the medium‐frequency region is attributed to the electronic properties of the material and the large arc in low‐frequency region is due to charge transfer step. Moreover, the changes of kinetic parameters for lithiation process of Fe 3 O 4 /CNTs composite as a function of electrode potential in the first discharge cycle was discussed in detail, and the effect of carbon matrix type on the electrochemical performance and lithiation mechanism of Fe 3 O 4 electrode is given.
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".