Relevance of LiPF<sub>6</sub> as Etching Agent of LiMnPO<sub>4</sub> Colloidal Nanocrystals for High Rate Performing Li-ion Battery Cathodes
Bibliographic record
Abstract
LiMnPO4 is an attractive cathode material for the next-generation high power Li-ion batteries, due to its high theoretical specific capacity (170 mA h g(-1)) and working voltage (4.1 V vs Li(+)/Li). However, two main drawbacks prevent the practical use of LiMnPO4: its low electronic conductivity and the limited lithium diffusion rate, which are responsible for the poor rate capability of the cathode. The electronic resistance is usually lowered by coating the particles with carbon, while the use of nanosize particles can alleviate the issues associated with poor ionic conductivity. It is therefore of primary importance to develop a synthetic route to LiMnPO4 nanocrystals (NCs) with controlled size and coated with a highly conductive carbon layer. We report here an effective surface etching process (using LiPF6) on colloidally synthesized LiMnPO4 NCs that makes the NCs dispersible in the aqueous glucose solution used as carbon source for the carbon coating step. Also, it is likely that the improved exposure of the NC surface to glucose facilitates the formation of a conductive carbon layer that is in intimate contact with the inorganic core, resulting in a high electronic conductivity of the electrode, as observed by us. The carbon coated etched LiMnPO4-based electrode exhibited a specific capacity of 118 mA h g(-1) at 1C, with a stable cycling performance and a capacity retention of 92% after 120 cycles at different C-rates. The delivered capacities were higher than those of electrodes based on not etched carbon coated NCs, which never exceeded 30 mA h g(-1). The rate capability here reported for the carbon coated etched LiMnPO4 nanocrystals represents an important result, taking into account that in the electrode formulation 80% wt is made of the active material and the adopted charge protocol is based on reasonable fast charge times.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".