Low Resistance, Carbon Black-free Magnetite Anode for Li-ion Batteries Obtained by One-step Attachment of Carbon Nanotubes
Bibliographic record
Abstract
The paper describes a simple, one-step synthetic route for the fabrication of nanometric iron (III) oxide attached to multiwalled carbon nanotubes (MWCNT). TEM images show that magnetite nanoparticles with primary particle sizes of ca. 10 nm are preferentially located on the outer walls of carbon nanotubes. The obtained nanocomposites have been examined on reversible electrochemical insertion of lithium cations in a Li-ion cell. The presence of MWCNT brings about a substantial increase of the magnetite reversible capacities in the absence of any additional carbonaceous conductivity enhancing agent. With increasing MWCNT content in the material, reversible capacity consistently rises from 207 mAhg-1 for pure iron (III) oxide up to 763 mAhg-1 for the composite with 40 % of MWCNT. Cyclic voltammetry measurements reveal the expected large hysteresis between the reduction and oxidation peaks associated with Li+ insertion/deinsertion into magnetite crystal lattice. The reductive current peak maxima steadily rise with increasing MWCNT content from 0.15 A g-1 for pure iron (III) oxide up to 0.4 A g-1 for the composite with 40 % of MWCNT which confirms faster kinetics of electrochemical processes. EIS measurements directly proved the internal resistance decrease connected with the incorporation of MWCNT.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".