Etched Colloidal LiFePO<sub>4</sub>Nanoplatelets toward High-Rate Capable Li-Ion Battery Electrodes
Bibliographic record
Abstract
LiFePO4 has been intensively investigated as a cathode material in Li-ion batteries, as it can in principle enable the development of high power electrodes. LiFePO4, on the other hand, is inherently "plagued" by poor electronic and ionic conductivity. While the problems with low electron conductivity are partially solved by carbon coating and further by doping or by downsizing the active particles to nanoscale dimensions, poor ionic conductivity is still an issue. To develop colloidally synthesized LiFePO4 nanocrystals (NCs) optimized for high rate applications, we propose here a surface treatment of the NCs. The particles as delivered from the synthesis have a surface passivated with long chain organic surfactants, and therefore can be dispersed only in aprotic solvents such as chloroform or toluene. Glucose that is commonly used as carbon source for carbon-coating procedure is not soluble in these solvents, but it can be dissolved in water. In order to make the NCs hydrophilic, we treated them with lithium hexafluorophosphate (LiPF6), which removes the surfactant ligand shell while preserving the structural and morphological properties of the NCs. Only a roughening of the edges of NCs was observed due to a partial etching of their surface. Electrodes prepared from these platelet NCs (after carbon coating) delivered a capacity of ∼ 155 mAh/g, ∼ 135 mAh/g, and ∼ 125 mAh/g, at 1 C, 5 C, and 10 C, respectively, with significant capacity retention and remarkable rate capability. For example, at 61 C (10.3 A/g), a capacity of ∼ 70 mAh/g was obtained, and at 122 C (20.7 A/g), the capacity was ∼ 30 mAh/g. The rate capability and the ease of scalability in the preparation of these surface-treated nanoplatelets make them highly suitable as electrodes in Li-ion batteries.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".