The Application of Atomic Layer Deposition in Lithium-Ion Batteries
Bibliographic record
Abstract
Lithium-ion batteries (LIBs) have been considered a promising energy storage system for various applications such as electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs). Intensive studies have been focused on improving LIBs in terms of power and energy density, cycling lifetime, safety characteristics, and cost. Atomic layer deposition (ALD) emerges as a powerful technique for addressing these issues due to its exclusive advantages over other thin film deposition techniques. Based on a self-limiting growth mechanism, ALD enables ultra uniform and conformal deposition of thin films and provides exquisite control of the thin film thickness down to angstrom. The advantages stand out more on high-aspect-ratio three-dimensional (3D) substrates due to the nature of the gas phase reactions [1, 2]. In LIBs, We first employed ALD to design nanocomposites used as electrode or solid-state electrolyte (SSE) materials, which can further be applied in the fabrication of 3D all-solid-state microbatteries [3]. Another application of ALD in LIBs is to deposit the ultra thin film on the electrode material as a surface modification in order to promote the performance of LIBs [4]. Our work also focuses on the development of lithium phosphate and lithium tantalum as SSE by ALD [5, 6]. The ALD processes have been established for both materials. LiOtBu is used as the lithium source. The as prepared thin films exhibit an amorphous structure. Electrochemical measurements are applied to characterize the ionic conductivity of these two thin films, which reaches an order of 10-8 scm-1. In addition, our work summarizes our recent study of engineering electrode/electrolyte interfaces by ALD. The complete surface coating acts an effective protection from the undesired side reactions (metal dissolutions in some cathode materials) and thus improves the capacity retention [6, 7]. The negligible thickness of the thin film allows ions to pass through. Besides, ALD prepared SSE (LiTaO3) as coating layers even benefits the transporting of lithium ions compared with simple binary oxide coatings such as Al2O3. [4] Furthermore, coatings with good toughness restrain the volume change of the electrode materials upon cycling, preventing pulverization. [8] We have been purposely developing different materials by ALD and combining them with LIBs. Lithium protection via ALD is another focus of our research. With fairly high energy density, lithium metal suffers from parasitic reactions with solvents, contaminations, and shuttled active species in the electrolyte. It has been reported that applying a thin chemical protection layer is a practical solution towards stabilizing lithium metal anodes in battery performances [9]. It has proven that ALD is a critical resort in the advances of next-generation LIBs in the future. [1] X. Meng, X. Yang, X. Sun, Adv. Mater. 2012, 24, 3589–3615 [2] J. Liu, X. Sun, Nanotechnology 2015, 26, 024001 [3] J. Liu, M. Banis, Q. Sun, A. Lushington, R. Li, T. K Sham, X. Sun, Adv. Mater. 2014, 26, 6472-6477 [4] X. Li, J. Liu, M. Banis, A. Lushington, R. Li, M. Cai, X. Sun, Energy Environ. Sci. 2014, 7, 768-778 [5] B. Wang, J. Liu, Q. Sun, R. Li, T. K Sham, X. Sun, Nanotechnology 2014, 25, 504007 [6] J. Liu, M. Banis, X. Li, A. Lushington, M. Cai, R. Li, T. K. Sham, X. Sun, J. Phys. Chem. C 2013, 117, 20260-20267 [7] B. Xiao, J. Liu, Q. Sun, B. Wang, M. Banis, D. Zhao, Z. Wang, R. Li, X. Cui, T. K. Sham, X. Sun, Adv. Sci. 2015, 1500022 [8] D. Wang, J. Yang, J. Liu, X. Li, R. Li, M. Cai, T. K. Sham, X. Sun, J. Mater. Chem. A 2014, 2, 2306-2312 [9] A. Kozen, C. Lin, A. J. Pearse, M. A. Schroeder, X. Han, L. Hu, S. Lee, G. W. Rubloff, Malachi Noked, ACS. Nano. 2015, 9, 5884-5892
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".