Bio‐Inspired Computational Design of Vascularized Electrodes for High‐Performance Fast‐Charging Batteries Optimized by Deep Learning

Sui, Chenxi; Li, Yao-Yu; Li, Xiuqiang; Higueros, Genesis; Wang, Keyu; Xie, Wanrong; Hsu, Po‐Chun

doi:10.1002/aenm.202103044

Cited by 13 publications

(16 citation statements)

References 63 publications

(15 reference statements)

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“…[2,3,[12][13][14][15][16][17][18][19][20][21][22][23] To increase the energy density while maintaining a high power output, computational and experimental results suggest that the diameter of the low-tortuosity pores and the thickness of the solid matrix with the active charge-storing material should be in the range of 5-20 µm, smaller than what is obtained from many of the aforementioned fabrication methods. [3,[24][25][26] Critical in the design of these electrodes is the aspect ratio between the pore diameter (D p ) and overall thickness of the electrode (h e ), as well as the material-to-pore ratio given by the thickness of the solid matrix (t m ) and D p , which defines the electrode density Mass transport is performance-defining across energy storage devices, often causing limitations at high current rates. To optimize and balance the devicescale energy and power density for a given energy storage demand, tailored electrode architectures with precisely controllable phase dimensions are needed in combination with low-tortuosity channels that maximize the geometric component of diffusion and species flux.…”

Section: Doi: 101002/adma202209694mentioning

confidence: 99%

“…Tailoring ratios between D p , t m and h e within low-tortuosity architectures to accommodate for the demanded in-plane supply of ions to the active material at a given current rate will allow for application-specific optimization between energy and power densities in this multidimensional parameter design space. [3,19,20,[24][25][26] Here we report the nonequilibrium soft matter processing technique of hybrid inorganic phase inversion (HIPI) that meets the challenge to fabricate free-standing and low-tortuosity composite electrode architectures. HIPI is a scalable manufacturing strategy that allows for individualized tuning of the most impactful structural features on relevant length scales.…”

Section: Introductionmentioning

confidence: 99%

“…[ 2,3,12–23 ] To increase the energy density while maintaining a high power output, computational and experimental results suggest that the diameter of the low‐tortuosity pores and the thickness of the solid matrix with the active charge‐storing material should be in the range of 5–20 µm, smaller than what is obtained from many of the aforementioned fabrication methods. [ 3,24–26 ] Critical in the design of these electrodes is the aspect ratio between the pore diameter ( D p ) and overall thickness of the electrode ( h e ), as well as the material‐to‐pore ratio given by the thickness of the solid matrix ( t m ) and D p , which defines the electrode density and porosity ( Figure A). Tailoring ratios between D p , t m and h e within low‐tortuosity architectures to accommodate for the demanded in‐plane supply of ions to the active material at a given current rate will allow for application‐specific optimization between energy and power densities in this multi‐dimensional parameter design space.…”

Section: Introductionmentioning

confidence: 99%

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Architected Low‐Tortuosity Electrodes with Tunable Porosity from Nonequilibrium Soft‐Matter Processing

2022

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Mass transport is performance‐defining across energy storage devices, often causing limitations at high current rates. To optimize and balance the device‐scale energy and power density for a given energy storage demand, tailored electrode architectures with precisely controllable phase dimensions are needed in combination with low‐tortuosity channels that maximize the geometric component of diffusion and species flux. A material‐agnostic nonequilibrium soft‐matter process is reported to fabricate free‐standing inorganic composite electrodes with adjustable thicknesses of 100s of µm, featuring straight and accessible channels ranging in diameter from 5–30 µm, coupled with tunable material‐to‐pore ratios. Such architected anode and cathode electrodes exhibit electrochemical and architectural stability over extended cycling in a full‐cell battery. Further, mass‐transport constraints appear at high current densities, and the lithiation step is identified as rate‐performance limiting, a result of insufficient lithium‐ion supply and concentration polarization. The results demonstrate the need for and feasibility of tailored electrode architectures where dimensional ratios between low‐tortuosity channels, the charge‐storing matrix, and electrode thickness are tunable to meet coupled power and energy‐storage requirements.

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Section: Doi: 101002/adma202209694mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Architected Low‐Tortuosity Electrodes with Tunable Porosity from Nonequilibrium Soft‐Matter Processing

2022

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“…Nature has already given plenty of examples for minimizing transport resistance in a certain system. 26 To adapt to external environments, a hierarchically porous network in natural systems could accomplish delivering nutrition with an extraordinary efficiency. 27,28 Systems with hierarchical pores that are interconnected in multiple length scales are often observed in plant peels, human bones, and vascular and respiratory systems.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Nature has already given plenty of examples for minimizing transport resistance in a certain system . To adapt to external environments, a hierarchically porous network in natural systems could accomplish delivering nutrition with an extraordinary efficiency. , Systems with hierarchical pores that are interconnected in multiple length scales are often observed in plant peels, human bones, and vascular and respiratory systems. , Considering this, we hypothesize that transport efficiency can be maximized, with porosity remaining constant while applying pore hierarchy in thick electrodes, as shown in the right part of Scheme a.…”

Section: Introductionmentioning

confidence: 99%

Unraveling the Effects of Hierarchical Bimodal Microscale Porosity on Thick Electrodes

Zhang

et al. 2022

J. Phys. Chem. C

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The thick electrode design is preferential in high-energy lithium-ion batteries (LIBs) systems. However, the sluggish ionic transport in homogeneous porous thick electrodes severely limits the areal capacity at high charging/discharging rates. The hierarchical porous design is a promising approach to mitigate kinetic limitations because it can distribute mass effectively in natural systems. In this study, the effects of bimodal microscale pores are fully investigated in thick electrodes from both architectural and electrochemical perspectives. Notably, by introduction of the bimodal microscale porous structure, the rate capability improves remarkably in thick electrodes with a low porosity (39%). By combining experimental results with simulations, this work presents a rational design guideline for preparing thick electrodes with a porosity at the commercial level, as well as simultaneous high energy and power densities, which brings new insights into the advanced electrode architecture design in scalable high-energy and high-power energy storage systems for practical applications.

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π Learning: A Performance‐Informed Framework for Microstructural Electrode Design

Niu

Zhao

et al. 2023

Advanced Energy Materials

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batteries, electrolyzers, and electrochemical CO 2 reduction reactors all depend heavily on the nature of the meso-scale reaction sites and charge conductivity in their electrodes. [2][3][4] The meso-scale electrode structure is often determined by the complex interactions of various phases under different thermal, mechanical, and chemical conditions. Therefore, how to design the next generation of high-performance electrodes remains a critical challenge.Data-driven design based on machine learning (ML) has been increasingly recognized as a revolutionary technology in structure design and has been demonstrated to successfully identify various excellent structures in protein sequences, [5] drug molecules, [6] and nanomaterials. [7] This paradigm opens the door for innovative design of high-performance catalyst materials and electrodes in electrochemical engineering. [8][9][10] A typical example is to accelerate the discovery of optimal atomic structures and compositions driven by density functional theory (DFT) calculations. [11][12][13] For instance, Ma et al. [14] proposed two deep learning algorithms to screen emerging electrode materials for sodium-ion and potassium-ion batteries. This data-driven approach for material screening is promising to accelerate the discovery of high-performance electrode materials. Benayad [15] et al. reviewed recent ML applications in Designing high-performance porous electrodes is the key to next-generation electrochemical energy devices. Current machine-learning-based electrode design strategies are mainly orientated toward physical properties; however, the electrochemical performance is the ultimate design objective. Performance-orientated electrode design is challenging because the current data driven approaches do not accurately extract high-dimensional features in complex multiphase microstructures. Herein, this work reports a novel performance-informed deep learning framework, termed π learning, which enables performance-informed microstructure generation, toward overall performance prediction of candidate electrodes by adding most relevant physical features into the learning process. This is achieved by integrating physicsinformed generative adversarial neural networks (GANs) with convolutional neural networks (CNNs) and with advanced multi-physics, multi-scale modeling of 3D porous electrodes. This work demonstrates the advantages of π learning by employing two popular design philosophies: forward and inverse designs, for the design of solid oxide fuel cells electrodes. π learning thus has the potential to unlock performance-driven learning in the design of next generation porous electrodes for advanced electrochemical energy devices such as fuel cells and batteries.

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Bio‐Inspired Computational Design of Vascularized Electrodes for High‐Performance Fast‐Charging Batteries Optimized by Deep Learning

Cited by 13 publications

References 63 publications

Architected Low‐Tortuosity Electrodes with Tunable Porosity from Nonequilibrium Soft‐Matter Processing

Architected Low‐Tortuosity Electrodes with Tunable Porosity from Nonequilibrium Soft‐Matter Processing

Unraveling the Effects of Hierarchical Bimodal Microscale Porosity on Thick Electrodes

π Learning: A Performance‐Informed Framework for Microstructural Electrode Design

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