Designing Electrolytes and Electrode-electrolyte Interfaces for Next-Generation Lithium Metal Batteries
Bibliographic record
Abstract
State-of-the-art lithium-ion batteries (LIBs) are approaching their energy density limits and thus may not be the answer to the ever-increasing demand for higher specific energy density in today’s energy storage and power applications. Li metal is considered the ultimate anode material due to its ultra-high specific capacity 3860 mAh g-1, more than 10 times higher than lithiated graphite. Solid-state electrolytes (SSEs) provide a potential solution to advance the performance of Li metal batteries (LMBs). However, the device integration of SSEs, especially Li-stuffed garnet, is exceptionally challenging. Another critical aspect for LMBs is to limit excess Li metal at the anode. In this thesis, the interface between Li metal anode and Li-stuffed garnet Li6.5La2.9Ba0.1Zr0.4Ta1.6O12 is investigated. Poor contact between Li and garnet is identified as the reason for high interfacial resistance. A viable surfactant-assisted wet chemical method to deposit ZnO layer on Li-stuffed garnet is reported to reduce the interfacial resistance to as low as 10 Ω cm2. A composite polymer-ceramic electrolyte (CPE) for room temperature solid-state Li-S battery (SSLSB) is demonstrated. The CPE has low interfacial resistance against both Li metal anode and sulfur cathode. An engineered sulfur-Ketjen black(S@KB) composite cathode is coupled with CPE to demonstrate a SSLSB with a pronounced specific capacity of 1108 mAh g−1 and areal capacity of 1.77 mAh cm−2. As CPE is prepared by a solution casting method, lean solvent confinement affects the morphological structure and ionic conductivity of CPE. A higher amount of solvent retention leads to higher ionic conductivity but at the cost of membranes’ mechanical properties. In order to study anode-free Li-metal batteries (AFLMBs), a special coin cell configuration is designed with high compression. The high pressure leads to more stable cycling performance, providing a more accurate assessment of AFLMBs. A carbonate-glyme hybrid electrolyte for AFLMB is demonstrated with capacity retention of 73% for 50 cycles. The hybrid electrolyte possesses a unique solvation structure, where diglyme solvates both Li-ions and film-forming additive, while carbonates dilute the mixture, enabling facile ion migrations.
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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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".