Tuning anisotropic ion transport in mesocrystalline lithium orthosilicate nanostructures with preferentially exposed facets
Bibliographic record
Abstract
Li2TMSiO4 (TM = Mn, Fe, Co, etc.) is regarded as a new class of cathode materials for next generation Li-ion batteries because of the theoretical possibility of reversible deintercalation of two Li ions from the structure (ca. 330 mA hg−1). Nevertheless, the silicate cathode still suffers from low electronic conductivity, slow Li ion diffusion and structural instability upon deep cycling. To solve these problems, for the first time, we propose a rational design of mesocrystalline Li2FeSiO4 hollow discoids with an ordered single-crystal-like structure and highly exposed (001) facets. The Li2FeSiO4 mesocrystals display a near theoretical discharge capacity, superior rate capability and good cycling stability. The enhanced Li storage performance is ascribed to the unique structural features with a large surface area generated from the hollow mesocrystal structure and a shortened Li+ diffusion path along (001) exposed facets. This new facile, elegant synthesis method that enables the manipulation of crystal growth and subsequent improvements in the electronic and ionic kinetics and structural integrity should have a positive impact on the research and development of silicate materials as promising cathodes for next generation Li-ion batteries. A battery-electrode material that achieves the excellent performance predicted by theory has been synthesised by researchers in China and Canada. A lithium-ion battery is charged by removing lithium from one electrode and storing it in a second. The energy is released when a circuit is connected and the lithium ions flow back the other way, thus producing a current. Li2FeSiO4 is a new electrode material that is exciting battery researchers because it theoretically has a large capacity for the removal of two lithium ions. A team led by Weifeng Wei from the Central South University in Changsha has now developed a simple method for creating hollow Li2FeSiO4 discoids. They show that this material exhibits near theoretical discharge capacity plus good stability as the battery is repeatedly charged and discharged. Mesocrystalline Li2FeSiO4 hollow discoids with an ordered single-crystal-like structure and highly exposed (001) facets have been successfully synthesized as a lithium-ion cathode. Li2FeSiO4@C mesocrystals display a near theoretical discharge capacity, superior rate capability and good cycling stability due to their unique structural features with a large surface area generated from the hollow mesocrystal structure and a shortened Li+ diffusion path along (001) exposed facets.
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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".