Stabilizing the Li Metal Interface: Molecular Layer Deposition for Advanced Next-Generation Energy Storage Systems
Bibliographic record
Abstract
The Li metal anode is sought-after for its high theoretical capacity (3860 mAh g-1) and low electrochemical potential (-3.04 V versus the standard hydrogen electrode). Nevertheless, the intrinsic instability of the Li metal towards both solid and liquid electrolytes leads to unstable solid electrolyte interphase (SEI) formation and dendritic lithium, causing safety concerns and rapid performance degradation [1]. Among the potential techniques used to protect the Li metal surface and inhibit dendrite growth, molecular layer deposition (MLD) has proven to be an invaluable tool for the development of nanoscale interfacial coatings with unique properties [2]. The layer-by-layer assembly of molecular fragments and conformal coating abilities of MLD yield the ability to tune the chemical and mechanical properties of the Li metal interface. It is believed that the exploration and adoption of new MLD films will open up new avenues for stabilizing the Li metal anode. Herein, we show several examples of high-performance Li metal anodes in liquid and solid-state systems achieved by polymeric coatings synthesized through MLD techniques [3-8]. New hybrid organic-inorganic coatings belonging to the metalcone family are shown to dramatically enhance the Coulombic efficiency and rate capability of Li metal anodes. Furthermore, the rational design and ordering of these coatings coupled with inorganic atomic layer deposition coatings reveal the importance of bilayer type structures in promoting enhanced mechanical properties for suppressing dendrite growth. The stabilizing properties of these advanced thin films are further extended to several next-generation battery systems including Li-S and Li-O2, proving effective when coupled with high energy density cathode materials. Moreover, advanced characterization techniques such as in-situ X-ray absorption spectroscopy, nanoindentation measurements, Rutherford backscattering spectrometry, and time-of-flight secondary ion mass spectrometry are used to reveal the mechanisms behind the electrochemical lithiation processes as well as cycling stability. References [1] Cheng, X.B.; Zhang, R.; Zhao, C.Z.; Zhang Q. Toward Safe Lithium Metal Anode in Rechargeable Batteries: A Review. Chem. Rev., 2017, 117, 10403-10473. [2] Meng, X.; Yang, X.Q.; Sun, X. Emerging applications of atomic layer deposition for lithium-ion battery studies. Adv. Mater., 2012, 24, 3589-3615. [3] Zhao, Y.; Goncharova, L.V.; Sun, Q.; Li, X.; Lushington, A.; Wang, B.; Li, R.; Dai, F.; Cai, M.; Sun, X. Robust Metallic Lithium Anode Protection by the Molecular Layer Deposition Technique. Small Methods, 2018, 2, 1700417. [4] Adair, K.R.; Zhao, C.; Banis, M.N.; Zhao, Y.; Li, R.; Cai, M.; Sun, X. Highly Stable Lithium Metal Anode Interface via Molecular Layer Deposition Zircone Coatings for Long Life Next-Generation Battery Systems. Angew. Chem. Int. Ed. 2019, 58, 15797-15802. [5] Sun, Y.; Zhao, Y.; Wang, J.; Liang, J.; Wang, C.; Sun, Q.; Lin, X.; Adair, K.R., Luo, J.; Wang, D.; Li, R.; Cai, M.; Sham, T.K.; Sun, X. A Novel Organic “Polyurea” Thin Film for Ultralong‐Life Lithium‐Metal Anodes via Molecular‐Layer Deposition. Adv. Mater. 2019, 31, 1806541. [6] Zhao, Y.; Amirmaleki, M.; Sun, Q.; Zhao, C.; Codirenzi, A.; Goncharova, L.V.; Wang, C.; Adair, K.; Li, X.; Yang, X.; Zhao, F.; Li, R.; Filleter, T.; Cai, M.; Sun, X. Natural SEI-Inspired Dual-Protective Layers via Atomic/Molecular Layer Deposition for Long-Life Metallic Lithium Anode. Matter, 2019, 1, 1215-1231. [7] Wang, C.; Zhao, Y.; Sun, Q.; Li, X.; Liu, Y.; Liang, J.; Li, X.; Lin, X.; Li, R.; Adair, K.R.; Zhang, L.; Yang, R.; Lu, S.; Sun, X. Stabilizing interface between Li10SnP2S12 and Li metal by molecular layer deposition. Nano Energy, 2018, 53, 168-174.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".