(Invited) Atomically-Precise Interfacial Materials and Design of Solid-State Batteries
Bibliographic record
Abstract
The state-of the-art rechargeable Lithium-ion batteries (LIBs) use liquid electrolytes and are the major choice for current EVs and portable electronic applications. However, these LIBs still suffer from many issues related to safety, lifespan and energy density. Accordingly, solid-state lithium batteries (SSLBs) have recently emerged as a promising alternative energy storage device due to their ability to overcome the intrinsic disadvantages of liquid-electrolyte LIBs and possess a greater volumetric energy density due to the use of solid-state electrolytes (SSEs). However, the interfacial issues between SSEs and electrodes (both cathode and anode) have a significant impact on the stability and lifetime of SSLBs [1-2]. The origin of these interfacial phenomena is the unstable contact and chemical reactions between electrodes and electrolytes to form an interlayer with extremely low electronic and/or ionic conductivities, which restricts the performance of the SSLBs. An artificial, uniform and ultrathin interfacial layer is critical to address these challenges [2]. Atomic layer deposition (ALD) and molecular layer deposition (MLD) are unique coating techniques that can realize excellent coverage and conformal deposition with precisely controllable at the nanoscale level due to its self-limiting nature, which are ideal for addressing the challenges of interface in SSLBs [2]. Our work applys ALD/MLD to rationally design novel coatings to address the interfacial challenges in SSLBs. The goal is to prevent capacity degradation of SSLBs caused by high interfacial resistance and chemical/electrochemical reactions between electrodes and electrolytes. We will demonstrate to (i) stabilize the interface between cathode electrodes and electrolytes and prevent the formation of intrinsically high resistance layers, (ii) suppress elemental inter-diffusion during the operation of SSLBs, (iii) fabricate facile ionic transportation channels to facilitate ion exchange between different components of SSLBs, and (iv) buffer volume changes during cycling of SSLBs. References: 1. Y. Zhao, X. Sun. Molecular Layer Deposition Technique for Energy Conversion and Storage. ACS Energy Lett. (2018),3,899-914. 2. Y. Zhao, X. Sun .Addressing Interfacial Issues in Liquid-based and Solid-State Batteries by Atomic and Molecular Layer Deposition. Joule.2018, in press. 3. Y. Zhao, X. Sun, et al., Robust Metallic Lithium Anode Protected by Molecular Layer Deposition Technique, Small Methods, (2018),1700417. DOI: 10.1002/smtd.201700417 4. C. Wang, X. Sun, et al., Stabilizing interface between Li10SnP2S12 and Li metal by molecular layer deposition. Nano Energy.53 (2018) 168–174. 5. C. Wang, X. Sun, et al., Boosting the performance of lithium batteries with solid-liquid hybrid electrolytes: Interfacial properties and effects of liquid electrolytes. Nano Energy.48 (2018) 35-43. 6. J. Liang, X. Sun, et al., In-Situ Li3PS4 Solid-State Electrolyte Protection Layers for Superior Long Life and High Rate Li-Metal Anodes. Adv. Mater. 2018, in press. 7. X. Li, X. Sun, et al., High-performance all-solid-state Li–Se batteries induced by sulfide electrolytes, Energy Environ. Sci., 2018,DOI: 10.1039/C8EE01621F.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".