@article{kabra_birn_kamboj_augustyn_mukherjee_2022, title={Mesoscale Machine Learning Analytics for Electrode Property Estimation}, volume={126}, ISSN={["1932-7455"]}, DOI={10.1021/acs.jpcc.2c04432}, abstractNote={The development of next-generation batteries with high areal and volumetric energy density requires the use of high active material mass loading electrodes. This typically reduces the power density, but the push for rapid charging has propelled innovation in microstructure design for improved transport and electrochemical conversion efficiency. This requires accurate effective electrode property estimation, such as tortuosity, electronic conductivity, and interfacial area. Obtaining this information solely from experiments and 3D mesoscale simulations is time-consuming while empirical relations are limited to simplified microstructure geometry. In this work, we propose an alternate route for rapid characterization of electrode microstructural effective properties using machine learning (ML). Using the Li-ion battery graphite anode electrode as an exemplar system, we generate a comprehensive data set of ∼17 000 electrode microstructures. These consist of various shapes, sizes, orientations, and chemical compositions, and characterize their effective properties using 3D mesoscale simulations. A low dimensional representation of each microstructure is achieved by calculating a set of comprehensive physical descriptors and eliminating redundant features. The mesoscale ML analytics based on porous electrode microstructural characteristics achieves prediction accuracy of more than 90% for effective property estimation.}, number={34}, journal={JOURNAL OF PHYSICAL CHEMISTRY C}, author={Kabra, Venkatesh and Birn, Brennan and Kamboj, Ishita and Augustyn, Veronica and Mukherjee, Partha P.}, year={2022}, month={Sep}, pages={14413–14429} }
 @article{spencer_yildiz_kamboj_bradford_augustyn_2021, title={Toward Deterministic 3D Energy Storage Electrode Architectures via Electrodeposition of Molybdenum Oxide onto CNT Foams}, volume={35}, ISSN={["1520-5029"]}, DOI={10.1021/acs.energyfuels.1c02352}, abstractNote={Three-dimensional (3D) deterministic design of electrodes could enable simultaneous high energy and power density for electrochemical energy storage devices. The goal of such electrode architectures is to provide adequate charge (electron and ion) transport pathways for high power, while maintaining high active material loading (>10 mg cm–2) for high areal and volumetric capacities. However, it remains a challenge to fabricate such electrodes with processes that are both scalable and reproducible. Toward this end, here, we demonstrate how the fabrication of such an electrode is made possible by combining tunable, free-standing, and aligned carbon nanotube (CNT) foams with aqueous electrodeposition of a model intercalation-type transition metal oxide, MoO3. Morphological characterization including X-ray microcomputed tomography indicates that the obtained composite is homogeneous. Electrodes with an active mass loading of up to 18 mg cm–2 reached near-theoretical Li-ion intercalation capacities within 1.7 h. The highest-mass loading electrodes also led to areal and volumetric capacities of 4.5 mA h cm–2 and 290 mA h cm–3, respectively, with 55% capacity retention for charge/discharge times of 10 min. Overall, this work demonstrates a scalable, deterministic 3D electrode design strategy using electrodeposition and free-standing, aligned CNT foams that lead to high areal and volumetric capacities and good rate performance due to well-distributed charge transport pathways.}, number={19}, journal={ENERGY & FUELS}, author={Spencer, Michael A. and Yildiz, Ozkan and Kamboj, Ishita and Bradford, Philip D. and Augustyn, Veronica}, year={2021}, month={Oct}, pages={16183–16193} }