PEDOT Encapsulated and Mechanochemically Engineered Silicate Nanocrystals for High Energy Density Cathodes
Bibliographic record
Abstract
Abstract Lithium iron silicate (LFS) attracts a lot of attention due to its 330 mAh g−1 theoretical capacity (2 Li+ per formula unit). However, inherently it exhibits poor Li‐ion intercalation kinetics, interfacial reactivity, and complex phase transitions resulting in lower than one Li+ capacity and poor retention. In this work, a core–shell architecture is devised largely overcoming these obstacles. At first, the nanostructure of Pmn21 LFS is annealed via mechanochemical processing enabling the activation of Li‐ion diffusion. Subsequently, the LFS nanocrystals are coated via in situ poly(3,4‐ethylenedioxythiophene) (PEDOT) polymerization involving partial chemical de‐lithiation/re‐lithiation, the latter catalyzed with FeCl3. As a result of the devised mechanochemical/interphasial engineering of the LFS@PEDOT nanocrystals, their Li‐ion storage capacity is augmented to > 1 Li, namely 200 mAh g−1 after 50 cycles or 1.2 Li+ units—the highest capacity reported for the Pmn21 LFS cathode. A key attribute of the new PEDOT coating technique is the generation of a Fe3+‐rich subsurface layer that contributes to structure stabilization via accelerated phase transition to inverse Pmn21 phase, in addition to rendering the nanocrystals electronically conductive and protected against reaction with electrolyte. Such core–shell engineered nanocrystals provide a powerful paradigm in developing viable high energy density cathodes for next‐generation Li‐ion batteries.
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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) | 0.000 | 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 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".