MIL‐96‐Al for Li–S Batteries: Shape or Size?
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) with controllable shapes and sizes show a great potential in Li-S batteries. However, neither the relationship between shape and specific capacity nor the influence of MOF particle size on cyclic stability have been fully established yet. Herein, MIL-96-Al with various shapes, forming hexagonal platelet crystals (HPC), hexagonal bipyramidal crystals (HBC), and hexagonal prismatic bipyramidal crystals (HPBC) are successfully prepared via cosolvent methods. Density functional theory (DFT) calculations demonstrate that the HBC shape with highly exposed (101) planes can effectively adsorb lithium polysulfides (LPS) during the charge/discharge process. By changing the relative proportion of the cosolvents, HBC samples with different particle sizes are prepared. When these MIL-96-Al crystals are used as sulfur host materials, it is found that those with a smaller size of the HBC shape deliver higher initial capacity. These investigations establish that different crystal planes have different adsorption abilities for LPS, and that the MOF particle size should be considered for a suitable sulfur host. More broadly, this work provides a strategy for designing sulfur hosts in Li-S 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 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".