2024
DOI: 10.1002/aenm.202304229
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3D Host Design Strategies Guiding “Bottom–Up” Lithium Deposition: A Review

Xi Wang,
Zhen Chen,
Kai Jiang
et al.

Abstract: Lithium metal batteries (LMBs) have the potential to be the next‐generation rechargeable batteries due to the high theoretical specific capacity and the lowest redox potential of lithium metal. However, the practical application of LMBs is hindered by challenges such as the uncontrolled growth of lithium dendrites, unstable solid electrolyte interphase (SEI), and excessive volume change of Li metal. To solve these issues, the design of high‐performance lithium metal anodes (LMAs) with various 3D structures is … Show more

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Cited by 3 publications
(2 citation statements)
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“…Utilising these atomistic models for machine learning applications can accelerate a bottom-up design and property prediction of complex fluids, an approach that has been successfully used in synthetic biology, catalyst development and even controlled lithium deposition. 30–32 Existing transformer models could also be utilised to suggest target molecules during the production of complex fluids, and to predict the properties of candidate molecules. 33,34 Other similar areas that also use artificial intelligence to find solutions to complex problems could assist the further development of model generation methodologies and validation methods using adequate training data.…”
Section: Introductionmentioning
confidence: 99%
“…Utilising these atomistic models for machine learning applications can accelerate a bottom-up design and property prediction of complex fluids, an approach that has been successfully used in synthetic biology, catalyst development and even controlled lithium deposition. 30–32 Existing transformer models could also be utilised to suggest target molecules during the production of complex fluids, and to predict the properties of candidate molecules. 33,34 Other similar areas that also use artificial intelligence to find solutions to complex problems could assist the further development of model generation methodologies and validation methods using adequate training data.…”
Section: Introductionmentioning
confidence: 99%
“…With the rapid development of electric vehicles and electronic devices, the demand for high-energy-density batteries has surged. , In this context, lithium (Li) metal batteries (LMBs) have gained considerable attention as an ideal choice. The Li metal, with its extremely high theoretical capacity (3860 mA h g –1 ), lowest reduction potential (3.04 V versus the standard hydrogen electrode (SHE)), and low weight density (0.53 g cm –3 ), is regarded as the “holy grail” for manufacturing more efficient electric vehicles compared with lithium-ion batteries. However, the utilization of LMBs also presents a series of challenges during charge/discharge cycles, such as poor cycle life, inferior stability, and safety concerns, posing significant obstacles to their commercial application. These challenges include the following: (1) irregular dendrite growth that can puncture the separator, leading to short circuits and safety issues ; (2) inferior cycle performance caused by continuous side reactions toward the Li metal and considerable “dead lithium” formation ; (3) complete anode pulverization and electrical failure evoked by infinite volume change. , Therefore, overcoming the above-mentioned obstacles is crucial for mitigating the cycling deficiencies of LMBs. To date, many methods have been proposed to enhance the electrochemical performance of LMBs, such as constructing a modification layer on the lithium metal current collector, adding functionalized electrolyte additives, building an artificial solid electrolyte interface (ASEI), and so on.…”
Section: Introductionmentioning
confidence: 99%