LiPAA with Short‐chain Anion Facilitating Li<sub>2</sub>S<i><sub>x</sub></i> (<i>x</i> ≤ 4) Reduction in Lean‐electrolyte Lithium–sulfur Battery
Bibliographic record
Abstract
Lean electrolyte usage in lithium–sulfur battery (LSB) meets the demand of the high energy density. However, lean condition makes the electrolyte‐related interface discrete, leading to retardation of ion transfer that depends on interfaces. Consequently, electrochemical reactions face restraint. Herein, lithium polyacrylate acid (LiPAA) with short‐chain anions (molecular weight of 2000) is introduced into the cathode. Because of the polysulfide (PS)‐philic instinct of the short‐chain PAA anions, short‐chain PS is captured inside of the cathode. In addition, LiPAA supplies Li+ to the short‐chain PS captured. The strong interaction between Li2S4 and LiPAA effectively decreases Li2S4 migration to the anode during discharging. In a sense, the ion mass transfer pattern is thus changed comparing to traditional long‐way mode between cathode and anode. Galvanostatic intermittent titration technique (GITT) proves that the interfacial reaction resistance is greatly decreased in the region where Li2Sx (x ≤ 4) reduction contributes most. In the same time, the reversibility of electrochemical reduction/oxidation is improved. Owing to the accelerated Li2Sx (x ≤ 4) reduction, Li implanting of only 0.3 wt.% plus O introduction up to 1.4 wt.% enables the LSB perform well even with 1/4 of regular electrolyte dosage (5 μL mg−1) and high‐sulfur loading (4.2 mg cm−2), increasing its rate capacity C0.8/0.5 from 52.6% (without the LiPAA) to 92.3% (with the LiPAA) as well as a capacity of 518.7 mAh g−1 after 400 cycles at 0.8 mA cm−2.
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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) | 0.001 | 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".