Cathode Development for All Solid-State Lithium-Sulfur Batteries
Bibliographic record
Abstract
With higher demand for alternative energy sources that are cleaner and more sustainable, research has shifted towards various lithium metal-based batteries with higher capacities than lithium-ion (LIBS). The study of lithium-sulfur energy storage systems has been heavily influenced due to the high gravimetric capacity of sulfur (1672 mA h g −1 ) and its abundance in the Earth’s crust. To increase the safety of lithium-sulfur batteries, solid state systems have been developed. These systems are also advantageous as the polysulfide shuttle effect normally seen in organic liquid electrolytes (OLEs) is eliminated, decreasing degradation due the irreversible reactions. Some of the most successful electrolytes found for lithium-sulfur all solid-state batteries (ASSLSBs) are ceramic based electrolytes. These electrolytes have high ionic conductivities (ranging 10 -3 -10 -4 S cm -1 ) and do not pose any safety risks. However, one of the biggest bottlenecks of ASSLSBs is the utilization and accessibility of sulfur during cycling. To ensure these attributes are met, carbon materials have been used as supports; additionally, carbon also aids in electron transport due sulfur’s low electrical conductivity (10 -15 S m -1 ). Sulfur is typically dispersed or deposited on these carbon supports in different approaches before a full cathode composition is developed. Varying synthesis methods of sulfur deposition onto reduced graphene oxide are studied to determine which synthesis method provides the most accessibility for sulfur utilization. Utilization and accessibility will be analyzed through cyclic voltammetry, galvanostatic charge and discharge and impedance measurements to obtain characteristic voltammograms, specific capacity curves and Nyquist plots. Cells are assembled in PEEK Split cells using ceramic based electrolyte and lithium metal for the development of a noble ASSLSB.
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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".