Enhanced Electrochemical Performance of Mechano-Activated Nanoparticles of the Low Temperature Orthorhombic Phase of Li<sub>2</sub>FeSiO<sub>4</sub>
Bibliographic record
Abstract
Lithium transition metal silicates Li2 M SiO4 ( M = Fe, Mn, Co, etc.) have been under intense research due to their high theoretical capacity of 330 mAh/g. However, most of the present work have been focused on the electrochemistry of the high temperature monoclinic phase (m-LFS), which shows clear phase transition to a thermodynamically stable low-temperature orthorhombic phase (o-LFS) during cycling. In this context we thought of interest to investigate the cycling behavior of the low-temperature o-LFS that we synthesized via hydrothermal processing. However, as synthesized o-LFS crystals have large ~1.5 µm size, which is undesirable for li-ion intercalation. Therefore, for particle size reduction, we used high energy planetary ball milling and obtained nanoparticles of ~50 nm. Interestingly, XRD results showed improved crystallinity during ball milling. Moreover, electrochemical performance of such mechanically activated nanoparticles showed improved capacity with increasing milling time. This intriguing (unusual) behavior could have significant implications to the development of high capacity LIB materials. Keywords: Li2FeSiO4; Orthorhombic; Low temperature; Electrochemical performance; Li-ion battery
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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".