Interface Engineering of Sulfide-Based All-Solid-State Lithium Batteries
Bibliographic record
Abstract
All-solid-state lithium batteries (ASSLBs) have gained intensive attention worldwide because of their intrinsic safety and potential high energy density. 1 As a critical component in ASSLBs, various solid-state electrolytes have been extensively studied over the past decade. So far, the ionic conductivity of solid-state electrolytes, particularly sulfide electrolytes (SEs, i.e. Li 10 GeP 2 S 12 ) is close to that of conventional liquid electrolytes. However, the electrochemical performance of SE-based ASSLBs is hindered by the large interfacial resistance between electrodes and sulfide electrolytes. The underlying reasons are detrimental interfacial reactions and insufficient solid-solid contact between electrodes and SEs in ASSLBs. 2 In our research, taking the advantages of atomic/molecular layer deposition techniques, an interfacial layer with designed functionalities can be conformably interposed between the electrodes and solid-state sulfide electrolytes, aiming at overcoming the interfacial resistance. At the anode interface, a artifical solid electrolyte interphase (SEI) has been engineered to suppress the interfacial rections and lithium dendrite growth, sucessfully enabling the use of Li metal in SE-based ASSLBs. 3 At the cathode interface, a dual-shell interfacial nanostructure was rationally designed, in which the inner shell LiNbO 3 suppressing the interfacial reactions, while the outer shell Li 10 GeP 2 S 12 providing an intimate electrode-electrolyte contact. 4 As a result, the dual-shell strucutured LGPS@LNO@LCO cathode exhibits a high initial specific capacity of 125.8 mAh.g -1 (1.35 mAh.cm -2 ) with an initial Coulombic efficiency of 90.4% at 0.1 C and 87.7 mAh.g -1 even at 1C. Furthermore, a plastic crystal electrolyte has been developed, which can simultaneously suppress the interfacial reactions and lithium dendrite growth in ASSLBs. 5 The above work not only demonstrates various strategies to enable high-energy-density ASSLBs but also provides new insights into the interfacial challenges of ASSLBs. References: 1. Y. Zhao, K. Zheng and X. Sun, Joule , 2018, 1-22. 2. C. Wang , Q. Sun, Y. Liu, Y. Zhao, X. Li, X. Lin, M. N. Banis, M. Li, W. Li, K. R. Adair, D. Wang, J. Liang, R. Li, L. Zhang, R. Yang, S. Lu and X. Sun, Nano Energy , 2018, 48 , 35-43. 3. C. Wang , Y. Zhao, Q. Sun, X. Li, Y. Liu, J. Liang, X. Li, X. Lin, R. Li, K. R. Adair, L. Zhang, R. Yang, S. Lu and X. Sun, Nano Energy , 2018, 53 , 168-174. 4. C. Wang , X. Li, Y. Zhao, M. N. Banis, J. Liang, X. Li, Y. Sun, K. R. Adair, Q. Sun, Y. Liu, F. Zhao, S. Deng, X. Lin, R. Li, Y. Hu, T.-K. Sham, H. Huang, L. Zhang, R. Yang, S. Lu and X. Sun, Small Methods , 2019, 1900261 , doi:10.1002/smtd.201900261. 5. C. Wang , K. R. Adair, J. Liang, X. Li, Y. Sun, X. Li, J. Wang, Q. Sun, F. Zhao, X. Lin, R. Li, H. Huang, L. Zhang, R. Yang, S. Lu and X. Sun, Adv. Funct. Mater. , 2019, 1900392 , DOI:10.1002/adfm.201900392. Figure 1
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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".