Embracing The Unwanted: Beyond Lithium Ion?
Prof. Daniel Steingart
Stanley Thompson Associate Professor of Chemical Metallurgy and Chemical Engineering
Co-Director of the Columbia Electrochemical Energy Center
Columbia University, New York, NY
Friday, Sep. 20, 2019 @ 11:45 am - 12:45 pm
The commercialization of the lithium ion battery decades ago was remarkable in a few regards. To the consumer, the energy per unit mass/volume was doubled the state of the art of the time. To the battery researcher, it introduced a new way of engineering batteries: an (almost)
platform technology. While proton shuttle batteries (NiCd and NiMH cells) existed prior to the lithium ion system, the relative "simplicity" of lithium speciation in both the electrolyte and electrode ushered in a plethora of material combinations for the lithium shuttle. This also allowed battery scientists and engineers to focus on half-cell innovations, and then later combine the optimized half cell results into a full cell which would generally function as expected. The lithium concentration in the electrolyte was
constant, so aside from (unwanted) solvent reduction, the behavior of the electrolyte was also constant as a function of state of charge. But as with many innovations, many learnings and tribal knowledge from "coupled systems" were shelved, and academic battery research focused less on systematic Interaction and more on specific component behaviors. At the same time, environmentally critical applications demand more energy and power per unit mass, volume, and dollar. To meet this demand, the battery research community is exploring a world "beyond lithium ion" where the engineering conveniences of the ion-shuttle battery may have to be sacrificed for further improved performance. So what was overlooked? In this seminar we will examine (critically) the challenges of competing against incumbent lithium ion technology, and then regardless of technology how the chemical and mechanical behaviors in full cells can be more than the superposition of the individual active components of the cell. We will also explore exploitations of full system interactions can be such that two wrongs might make a right.
Daniel Steingart is the Stanley Thompson Associate Professor of Chemical Metallurgy and Chemical Engineering and the co-director of the Columbia Electrochemical Energy Center. His group studies the systematic behaviors of material deposition, conversion, and dissolution in electrochemical reactors with a focus on energy storage devices. His current research looks to exploit traditional failure mechanisms and unwanted interactions in batteries, turning unwanted behaviors into beneficial mechanisms. His efforts in this area over the last decade have been adopted by various
industries and have led directly or indirectly to five electrochemical energy related startup companies, the latest being Feasible, an effort dedicated to exploiting the inherent acoustic responses of closed electrochemical systems. Steingart joined Columbia Engineering in 2019 from Princeton
University where he was an associate professor in the department of mechanical and aerospace engineering and the Andlinger Center for Energy and the Environment. Earlier, he was an assistant professor in chemical engineering at the City College of the City University of New York. Even earlier he was an engineer at two energy related startups. He received his PhD from the University of California, Berkeley, in 2006.
PhD Candidate in Mechanical Engineering
Jeffrey Kysar, Alan West, and Anil Lalwani
Fabrication of precision metallic microneedles using template assisted
electrochemical deposition Microneedles are an attractive area of research
for applications that require minimally invasive delivery, sampling, or monitoring. The cochlea, or the inner ear, is an area of application for microneedles, particularly due to the inaccessible nature of it. Historically, this inaccessibility has made disorders of the inner ear very difficult to diagnose or treat. We have developed a method for precise additive manufacturing of fully metallic microneedles using two-photon lithography and electrochemical deposition, that provides the engineer with immense design freedom. Furthermore, this technique allows for prescribing the mechanical properties throughout the needle by manipulating the microstructure of the metal during deposition.
BIO: Aykut is a fourth-year Ph.D. student in Mechanical Engineering. His research lies in the intersection of Mechanical Engineering, Chemical Engineering, and Medicine. He is currently working to make precision
microneedles to better diagnose and treat inner ear disorders. He is co-advised by Prof. Jeffrey Kysar, Prof. Alan West, and Dr. Anil Lalwani.
Distinguished Postdoctoral Fellow
Andlinger Center for Energy and the Environment,
Low-cost grid energy storage: cost limits of lithium-ion batteries
Finding low-cost energy storage solutions is critical to minimizing the curtailment of renewable electricity generation and matching diurnal variations in electricity demand and production. Today’s lithium-ion battery costs are low enough to be economically competitive for short duration applications (4 hours or less), but are still too expensive for longer
applications. Cell design and material choices have a substantial influence on the manufacturing cost of lithium-ion cells, but with tradeoffs in round trip efficiency. Here, we model cell performance and manufacturing
costs to assess how improvements affect the levelized cost of stored electricity for long duration (8-16 hour) applications. We find that
for long-duration applications, charging and discharging efficiencies are high, and have relatively little impact on the levelized cost of storage. Using existing alternative cell designs can reduce costs to below $100/kWh, but capital cost, operation and maintenance expenses, and total electricity delivered play a large role in the levelized cost of storage.
BIO: Rebecca Ciez is a Distinguished Postdoctoral Fellow at the Andlinger Center for Energy and the Environment, where her research focuses on the technology and policy challenges of integrating energy storage for decarbonizing electricity, transportation, and industrial systems. She holds a bachelor’s degree in Mechanical Engineering from Columbia University and a Ph.D. in Engineering and Public Policy from Carnegie Mellon University.
Modeling of Complex Inorganic Materials for Energy
Applications with First Principles and Machine Learning Models
Dr. Nongnuch Artrith
Friday, Oct. 11, 2019 @ 11:45 am - 12:45 pm
Many complex materials for energy applications such as heterogeneous catalysts and battery cathode materials have compositions with multiple chemical species and properties that are determined by complex structural features. This complexity makes them challenging to model directly with first principles methods. As an alternative, machine-learning techniques can be used to interpolate first principles calculations. Such machine-learning potentials (MLPs) enable linear-scaling atomistic simulations with an accuracy that is close to the reference method at a fraction of the computational cost. Here, I will give an overview of recent applications of
MLPs based on artificial neural networks (ANNs)  to the modeling of challenging materials classes, e.g., nanoalloys in solution , oxide nanoparticles , and amorphous materials [4,5]. The original multi-species ANN potential formalism  scales quadratically with the number of
chemical species. This has previously prevented the modeling of compositions with more than a few elements. To overcome this limitation, we have recently developed an alternative mathematically simple and computationally efficient descriptor with a complexity that is independent of the number of chemical species . The new methodology has been
implemented in our free and open source atomic energy network (aenet) package (http://ann.atomistic.net) [8-9]. This development creates new opportunities for the modeling of complex materials for example in the field of catalysis and materials for energy applications.
1. J. Behler and M. Parrinello, Phys. Rev. Lett. 98 146401 (2007).
2. N. Artrith and A. M. Kolpak, Nano Lett. 14 2670-2676 (2014); Comput. Mater. Sci. 110 20-28 (2015).
3. J. S. Elias, N. Artrith, et.al, and Y. Shao-Horn, ACS Catal. 6, 1675-1679 (2016).
4. N. Artrith, A. Urban, G. Ceder, J. Chem. Phys. 148, 241711 (2018), and arXiv 1901.09272 (2019).
5. V. Lacivita, N. Artrith, G. Ceder, Chem. Mater. 30, 7077–7090 (2018).
6. N. Artrith, T. Morawietz, and J. Behler, Phys. Rev. B 83, 153101 (2011).
7. N. Artrith, A. Urban, and G. Ceder, Phys. Rev. B 96, 014112 (2017).
8. N. Artrith and A. Urban, Comput. Mater. Sci. 114, 135-150 (2016).
9. N. Artrith, J. Phys. Energy 1, 032002 (2019).
Nong Artrith obtained her PhD in Theoretical Chemistry from Ruhr University Bochum, Germany (Prof. Joerg Behler) for the development of machine learning methods for atomistic models used in chemistry and materials science. Then, Nong was awarded an FFTF fellowship from Schlumberger Foundation (supporting women in STEM) for postdoctoral work at MIT with Prof. Alexie Kolpak, where she applied machine learning models to understand catalyst systems. Nong subsequently joined Prof. Gerbrand Ceder’s group at UC Berkeley as an associate specialist to develop machine learning models for amorphous electrode materials for Li-ion batteries. Currently, Nong is a research scientist in the Department of Chemical Engineering at Columbia University and is funded to 50% by the Center for Functional Nanomaterials at Brookhaven National Lab. In 2019, Nong has been named a Scialog Fellow for Advanced Energy Storage.
1/18/19: Prof. Byungha Shin, KAIST
1/25/19: Nick Brady/Jack Davis, Ph.D. Student at Columbia
2/15/19: Daniel Esposito, Solar Fuels Engineering Lab at Columbia
3/1/19: Jake Russell/Anna Dorfi, Ph.D. Student at Columbia
3/15/19: Alex Couzis, CCNY/Urban Electric Power
4/26/19: Jon Vardner/Steven Denny, Ph.D. Student at Columbia
5/10/19: Qian Cheng/Brian Tackett, Ph.D. Student at Columbia