Advanced Materials for Energy Conversion and Storage 2022: Energy Conversion and Energy Storage MIX II
Sponsored by: TMS Functional Materials Division, TMS: Energy Conversion and Storage Committee
Program Organizers: Jung Choi, Pacific Northwest National Laboratory; Soumendra Basu, Boston University; Paul Ohodnicki, University of Pittsburgh; Partha Mukherjee, Purdue University; Surojit Gupta, University of North Dakota; Amit Pandey, Lockheed Martin Space; Kyle Brinkman, Clemson University

Thursday 8:30 AM
March 3, 2022
Room: 212B
Location: Anaheim Convention Center

Session Chair: Peter Godart, Massachusetts Institute of Technology

8:30 AM  Invited
NOW ON-DEMAND ONLY - 3D Printed Carbon and Graphene Aerogels for Energy Storage Applications: Swetha Chandrasekaran1; Dun Lin2; Bin Yao2; Jean-Baptiste Forien1; Juergen Biener1; Victor Beck1; Yat Li2; Marcus Worsley1; 1lawrence livermore national laboratory; 2University of California, Santa Cruz
    Energy related processes, such as energy storage and catalysis typically utilize high surface area electrodes that possess macro- and micropores like carbon aerogels (CAs). Traditional CA synthesis results in isotropic, random nanoporous networks that work well for applications relying on diffusional mass transport. However, many other applications would benefit from integration of engineered macroporous network structures that enable fast charge-transport kinetics. Our work focuses on 3D printing these aerogels via direct ink writing (DIW) to precisely deposit inks in a pre-defined tool path to form 3D structures to improve both mass transport and output power efficiency for electro-chemical applications. We have also demonstrated that by using 3D printed graphene aerogels as scaffold, we can support ultrahigh mass loadings of pseudocapacitive materials. Apart from DIW, 3D-printed sacrificial polymeric templates were used to generate templated CAs with integrated engineered nonrandom macroporous network structures. Prepared by LLNL under Contract DE-AC52-07NA27344.

8:50 AM  Invited
Bulk Ferroelectric Metamaterial with Enhanced Piezoelectric and Biomimetic Mechanical Properties from Additive Manufacturing: Jun Li1; 1University of Wisconsin-Madison
    Three-dimensional (3D) ferroelectric materials are promising electromechanical building blocks for achieving human-machine interfacing, energy sustainability, and enhanced therapeutics. However, current natural or synthetic materials cannot offer both high piezoelectric responses and desired mechanical toughness at the same time. Here, a nacre-mimetic ferroelectric metamaterial was created with a ceramic-like piezoelectric property and a bone-like fracture toughness through a novel low-voltage-assisted 3D printing technology. The one-step printed bulk structure, consisting of periodically intercalated soft ferroelectric and hard electrode intercalated layers, exhibited a significantly enhanced longitudinal piezoelectric charge coefficient of over 150 pC N-1, as well as a superior fracture resistance of ~ 5.5 MPa·m1/2 more than three times higher than piezo-ceramics. The excellent printability together with the unique combination of both high piezoelectric and mechanical behaviors allowed us to create bone-like structure with tunable anisotropic piezoelectricity and bone-comparable mechanical properties, marking a cornerstone toward manufacturing practical, high-performance, and smart biological systems.

9:10 AM  Invited
Continuous Process for Harvesting Energy from Aluminum Scrap via Liquid-metal Activation: Peter Godart1; Douglas Hart1; 1Massachusetts Intitute of Technology
    Scrap aluminum, in addition to being abundant in landfills throughout the world, is also highly energy dense. It has been shown that this energy can be extracted in a controlled manner via oxidation of the aluminum by water. To enable the reaction, the otherwise passivating aluminum-oxide layer can be disrupted by introducing a liquid metal alloy containing gallium and indium into the aluminum grain boundary network. Previously, recovery of the activating metals as a liquid was not possible due to oxidation of the gallium. In this work, however, a method for recovering the activating metals as a liquid using saltwater is presented, enabling a continuous energy harvesting system whose only inputs are scrap aluminum and water, and whose outputs are hydrogen gas and heat. This process was demonstrated using practical scrap aluminum (used beverage cans) to achieve a >97% hydrogen yield fraction and a >99% liquid metal recovery ratio.

9:30 AM  
Machine Learning-driven Analytics for Solid-state Batteries: Debanjali Chatterjee1; Bairav S. Vishnugopi1; Partha P. Mukherjee1; 1Purdue University
    Solid-state batteries (SSBs) hold the potential to improve the energy density and power density of conventional lithium-ion batteries while offering enhanced safety attributes. Microstructural arrangement of both the porous cathode and solid electrolyte play a critical role in determining percolation pathways and effective mechanical/transport properties. Coupled kinetic and transport processes in these electrodes, the spatial distribution of constituent phases and the physiochemical interactions between them dictate the electrochemical performance of the SSB. However, microstructure of the porous electrode and solid electrolyte is intrinsically stochastic, heterogeneous, and anisotropic, which makes the characterization of their effective properties complex. In this regard, machine learning can be used as a fast and effective tool to characterize key electrode microstructural attributes. In this work, we use a combination of physics-based modeling and machine learning-driven analytics to characterize key microstructural features like transport percolation, pore connectivity and active interfacial area in SSB systems.

9:50 AM Break

10:10 AM  
Use of Machine Learning Methods to Predict Remaining Useful Life of Lithium-ion Batteries after Experiencing Non-catastrophic Nail Puncture: Casey Jones1; Meghana Sudarshan1; Alex Serov1; Vikas Tomar1; 1Purdue University
    The purpose of this work was to simulate the operation of a Lithium-ion cell in an abusive environment, such as those found in electric vehicles, aerospace applications, etc., and use the collected data to predict the rate of degradation and remaining useful life with different machine learning methods. A nail puncture test was performed with a depth approximately halfway through each cell during cycling at a rate of 1C, and the cells were allowed to continue cycling afterwards. The penetrations caused a rapid spike in temperature, resulting in decomposition of the electrolyte and solid electrolyte interface. Combined with the physical damage to the electrodes, this generated an accelerated rate of aging in the cells under test. The resulting data on cycle number, capacity, temperature, and other factors was used to develop machine learning algorithms that were implemented in order to predict the remaining useful life of the cells after puncture.