All-Solid-State Lithium Metal Batteries with Sulfide Electrolytes: Understanding Interfacial Ion and Electron Transport
Bibliographic record
Abstract
ConspectusWith the ever-growing demand for high energy density and high safety of energy storage technologies, all-solid-state lithium metal batteries (ASSLMBs) including all-solid-state lithium ion batteries (ASSLIBs) and all-solid-state lithium–sulfur batteries (ASSLSBs) have received considerable attention in recent years. To realize ASSLMBs, various solid-state electrolytes have been rapidly developed. Among them, sulfide electrolytes (SEs) demonstrate the highest ionic conductivity (>10 mS·cm–1) and the most favorable mechanical properties. However, the commercialization of SE-based ASSLMBs has been stymied by sluggish interfacial ion and electron transport kinetics, which arises from the detrimental interfacial reactions, poor interfacial solid–solid contact, and lithium dendrite growth. To overcome the interfacial challenges, an insightful understanding of the complex interfacial ion and electron transport processes in SE-based ASSLMBs is of the foremost importance. Although most of the previous review papers underscored the interfacial challenges and summarized the corresponding strategies, a fundamental understanding of the interfacial ions and electron-transport kinetics in SE-based ASSLMBs has not yet been presented. This Account therefore primarily summarizes our recent understanding of SE-based ASSLMBs from the perspectives of interfacial ion and electron transport, aiming to provide an insightful understanding of interfacial kinetics.We first compare various solid-state electrolytes including SEs, oxide electrolytes, halide electrolytes, polymer electrolytes, borohydride electrolytes in terms of their ionic conductivity, electrochemical stability windows, and mechanical properties, aiming to justify the advantages of SEs in ASSLMBs. Then, the interfacial challenges at both cathode and anode interfaces of SE-based ASSLIBs and SE-based ASSLSBs are presented, respectively. These interfacial challenges significantly hinder the interfacial ion and electron transport. To boost the interfacial charge transfer kinetics, our recent research advances related to the cathode interface between transition-metal oxides (TMOs) and SEs are then discussed. In parallel, our latest progress on SE-based ASSLSBs is also summarized from the perspective of interfacial kinetics. At the anode interface, our innovative strategies to enhance interfacial Li ion transport between Li metal and SEs are recapped. Finally, conclusions and our perspectives on SE-based ASSLMBs are presented. We believe that this specific Account provides an insightful understanding of interfacial ion and electron transport behavior in SE-based ASSLMBs.
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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 | 0.001 | 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".