Design Guidelines for Sulfonyl/Sulfamoyl Fluoride Additives to Modulate Lithium Anode Coulombic Efficiency
Bibliographic record
Abstract
The lithium metal anode has a high theoretical capacity (3860 mAh / g) and low electrochemical potential (‐3.04 V vs SHE), making it an ideal anode material for high energy density Li batteries. However, the high reactivity of Li metal with electrolytes results in the formation of a solid electrolyte interphase (SEI). In conventional electrolytes, the SEI is unstable, leading to continuous capacity loss and low Coulombic efficiencies (CE). Successful principles for Li metal electrolyte design to achieve high CE have largely focused on promoting the sacrificial reduction of anions (such as lithium bis(fluorosulfonyl)imide (LiFSI)) believed to be beneficial for the SEI. Alternatively, additive development for Li metal has identified several chemical classes that have been shown to effectively modify either the Li plating morphology or the SEI chemistry, and consequently increase CE. Motivated by the high CE of LiFSI systems and the performance of previously studied additives for Li metal, sulfonyl/sulfamoyl fluorides (R‐SO2F and R‐R’ NSO2F, respectively) are examined as a model class of functional electrolyte additives for Li cycling. This thesis examines what parameters govern the performance of this model class of additives, including the additive chemical structure and baseline electrolyte solvent. The effects of additives on CE were evaluated in select high‐CE electrolytes consisting of LiFSI dissolved in representative organic solvents, as well as the commercially relevant carbonate electrolyte LiPF6 dissolved in EC:DEC (LP40). The observed variations in CE, which suggest competitive reactions among solvents, anions, and additive, are then rationalized by characterization of post‐cycling SEI chemical compositions, gas evolution, and Li plating morphologies. The results identify the function of the nitrogen center unique to sulfamoylfluorides in promoting Li+ coordination and preventing structural fragmentation of the additive during cycling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".