Nanostructured materials for lithium-ion batteries: Surface conductivity vs. bulk ion/electron transport
Bibliographic record
Abstract
Lithium metal phosphates are amongst the most promising cathode materials for high capacity lithium-ion batteries. Owing to their inherently low electronic conductivity, it is essential to optimize their properties to minimize defect concentration and crystallite size (down to the submicron level), control morphology, and to decorate the crystallite surfaces with conductive nanostructures that act as conduits to deliver electrons to the bulk lattice. Here, we discuss factors relating to doping and defects in olivine phosphates LiMPO4 (M = Fe, Mn, Co, Ni) and describe methods by which in situ nanophase composites with conductivities ranging from 10(-4)-10(-2) S cm(-1) can be prepared. These utilize surface reactivity to produce intergranular nitrides, phosphides, and/or phosphocarbides at temperatures as low as 600 degrees C that maximize the accessibility of the bulk for Li de/insertion. Surface modification can only address the transport problem in part, however. A key issue in these materials is also to unravel the factors governing ion and electron transport within the lattice. Lithium de/insertion in the phosphates is accompanied by two-phase transitions owing to poor solubility of the single phase compositions, where low mobility of the phase boundary limits the rate characteristics. Here we discuss concerted mobility of the charge carriers. Using Mössbauer spectroscopy to pinpoint the temperature at which the solid solution forms, we directly probe small polaron hopping in the solid solution Li(x)FePO4 phases formed at elevated temperature, and give evidence for a strong correlation between electron and lithium delocalization events that suggests they are coupled.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".