Building better lithium-sulfur batteries: from LiNO3 to solid oxide catalyst
Bibliographic record
Abstract
Lithium nitrate (LiNO3) is known as an important electrolyte additive in lithium-sulfur (Li-S) batteries. The prevailing understanding is that LiNO3 reacts with metallic lithium anode to form a passivation layer which suppresses redox shuttles of lithium polysulfides, enabling good rechargeability of Li-S batteries. However, this view is seeing more challenges in the recent studies, and above all, the inability of inhibiting polysulfide reduction on Li anode. A closely related issue is the progressive reduction of LiNO3 on Li anode which elevates internal resistance of the cell and compromises its cycling stability. Herein, we systematically investigated the function of LiNO3 in redox-shuttle suppression, and propose the suppression as a result of catalyzed oxidation of polysulfides to sulfur by nitrate anions on or in the proximity of the electrode surface upon cell charging. This hypothesis is supported by both density functional theory calculations and the nitrate anions-suppressed self-discharge rate in Li-S cells. The catalytic mechanism is further validated by the use of ruthenium oxide (RuO2, a good oxygen evolution catalyst) on cathode, which equips the LiNO3-free cell with higher capacity and improved capacity retention over 400 cycles.
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 | 0.000 | 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 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".