Understanding the Nature of Absorption/Adsorption in Nanoporous Polysulfide Sorbents for the Li–S Battery
Bibliographic record
Abstract
The possibility of achieving high-energy, long-life storage batteries has tremendous scientific and technological significance. A prime example is the Li–S cell, which can offer a 3–5-fold increase in energy density compared with conventional Li-ion cells, at lower cost. Despite significant recent advances, there are challenges to its wide-scale implementation. Upon sulfur reduction, intermediate soluble lithium polysulfides readily diffuse into the electrolyte, causing capacity fading and poor Coulombic efficiency in the cell. Herein, we increase the capacity retention and cycle life of the Li–S cell through the use of nanocrystalline and mesoporous titania additives as polysulfide reservoirs and examine the role of surface ad sorption vs pore ab sorption. We find that the soluble lithium polysulfides are preferentially absorbed within the pores of the nanoporous titania at intermediate discharge/charge. This provides the major factor in stabilizing capacity although surface binding (adsorption) also plays a more minor role. A cell containing TiO 2 with a 5 nm pore diameter exhibited a 37% greater discharge capacity retention after 100 cycles than a cell without the titania additive, which was optimum compared to the other titania that were examined.
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 | 0.000 |
| 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".