bluuebunny/crossref_metadata_2025_split huggingface dataset:
(Energy Technology Division Research Award) Multiphysics, Multiscale Modeling of Electrochemical Energy-Conversion Technologies
Authors: Weber Adam Z. 
Published: September 2023

As electrochemical technologies become increasingly important in our energy paradigm, there is a need to examine them holistically. For commercialization and optimization, one requires a detailed understanding of the underlying physics and phenomena. Furthermore, for such technologies to become practical, they need to operate at high current densities to minimize various cell costs. This operating space necessitates the need for efficient transport of reactants and removal of products from the reaction site, where it is increasingly recognized how critical this local environment and transport are to performance. Mathematical modeling is ideally suited to understand these various interactions and provide insights and optimization strategies. In this talk, various porous electrodes will be examined in terms of their intrinsic transport phenomena. Technologies to be discussed include proton-exchange-membrane fuel cells and electrolyzers and gas-diffusion electrodes for CO2 reduction.
For PEMFCs, such mass transport is dominated by transport of oxygen molecules to the reaction site within the catalyst layer. For electrolysis applications, similar gas-diffusion-electrode (GDE) architectures are being investigated and the various tradeoffs endemic in such structures will be discussed such as reactant water transport. In this talk, we will explore the various tradeoffs endemic in GDE architectures for various electrochemical reactions including CO2 and CO reduction and O2 and H2 consumption and evolution. For all technologies, such tradeoffs are quantified through multiphysics modeling and key diagnostics of the cells including breakdowns of the various limiting phenomena at both the micro and macroscales, where the local conditions and environment around the reaction center impact reactivity in both transient and steady state conditions, enabling metastable states. It will be shown how transport phenomena dominate a lot of the performance and selectivity of the catalysts, which is critical to understand to improve overall performance. In addition, we will explore how different integration schemes can greatly impact overall response, which can overshadow any intrinsic changes due to different electrocatalyst materials, thereby providing different design rules. A key focus of this talk will be in exploring and describing the various transport and competing phenomena within the electrochemical cells and how to describe them physically and numerically
(Invited) Multiscale Diffusion-Reaction Heat Transfer Analysis for Predicting Thermal Runaway in Li-Ion Cells and Battery Packs
Authors: Jain Ankur 
Published: September 2023

Thermal runaway in Li-ion cells and battery packs remains a topic of much importance for ensuring the safety of electrochemical energy conversion and storage systems. Thermal runaway also indirectly affects performance, as conservative overdesign to reduce the risk of thermal runaway affects system-level performance metrics such as energy storage density. Given the considerable challenges in experimental investigation of thermal runaway, theoretical models to predict thermal runaway characteristics of a cell or battery pack are critically needed. Such models can help guide the design of experiments, and ensure that practical systems are reasonably safe without being needlessly overdesigned.
This talk will summarize ongoing work on theoretical analysis of multilayer diffusion-reaction heat transfer problems for predicting thermal runaway in Li-ion cells and battery packs. Following a linearization of heat generation terms, the governing energy conservation equation is solved in a multilayer geometry representative of a Li-ion cell comprising multiple electrode and separator layers, or of a battery pack containing several cells. It is mathematically shown that under certain conditions, this problem may admit a finite number of imaginary eigenvalues, which are shown to cause thermal runaway. It is shown that the occurrence of imaginary eigenvalues is governed by a delicate balance between heat generation, thermal conduction within the multilayer body and heat removal from the boundaries, based on which, an explicit threshold condition for thermal runaway is derived. This threshold condition is interpreted in the context of parameter values relevant to Li-ion cells. For example, it is shown that boundary cooling is severely limited in its role to prevent thermal runaway, whereas improving thermal transport within the cell is likely to be a lot more effective. This theoretical model is also extended to a two-layer problem comprising a finite layer adjoined by a semi-infinite body that may model immersion cooling of a Li-ion cell or battery pack. It is shown that while this diffusion-reaction problem is, in principle, unconditionally unstable, the finite time period of any discharge process may allow the temperature field to still remain within a safe envelope, if designed appropriately. Based on this mathematical model, scenarios related to immersion cooling of pouch cells are investigated.
The theoretical work described in this talk is broadly relevant to several diffusion-reaction systems. In the particular context of Li-ion cells, these results may form the foundation of techniques to proactively predict and prevent thermal runaway in Li-ion cells and battery packs.
(Keynote) Electrode Stimulation: Ohmic Microscopy and the Monopolar Electrode
Authors: Scherson Daniel , Heer Arvind , Mantelli Harlan , Georgescu Nicholas , Han Qi 
Published: September 2023

In a recent communication (1), we described a novel method of controlling the electrostatic potential in the solution neighboring a working electrode, we have dubbed the
monopolar electrode
, ME, polarized at a constant potential, EME, using a potentiostat. This tactic involved passing current between a second or
stimulating electrode
, SE, placed in front, and at a relatively small distance from the ME, and a distant auxiliary electrode, AE. Depending on the polarity of the current flowing through the SE, the electrostatic potential just outside the ME could be modified so as to increase or decrease the surface overpotential, and, thus modulate the rates of redox reactions at the ME|electrolyte interface. This presentation will review critical aspects of ohmic microscopy, and illustrate with two examples the
electrode stimulation
effect, using a ring disk electrode, RRDE, in which current flowing through the disk acting as the SE, is employed to induce reactions on the ring, acting as the ME, namely: i. the oxidation of adsorbed carbon monoxide on Pt using a Pt|glassy carbon RRDE and ii. the reduction of selenite to elemental Se on Au in aqueous 0.1 M HClO4 using a Au|Au RRDE. In both these cases, the
stimulation
current was generated by scanning the potential of SE within its double layer region, so as to minimize any changes in the actual composition of the solution adjacent to the ME, while monitoring the current flowing through the ME, IME
. Under these conditions, the electrostatic potential in solution follows the primary current density. The efficiency of the
stimulation
process was determined by coulometric analysis of the CO oxidation current and in the case of selenite via a coulometric analysis of the current due to the oxidation of elemental Se produced by the reduction of selenite. The stimulation

efficiency, in this latter case, could be accurately determined from a coulometric analysis of the peak for Se oxidation observed by subsequently scanning the ME linearly toward positive potentials to yield selenite. Excellent quantitative agreement was obtained between IME
and EME
as a function of Idisk
and theoretical simulations employing COMSOL using parameters extracted from independent measurements performed under otherwise identical experimental conditions. This overall approach will open new prospects for gaining insight into the rates of surface diffusion and other interfacial dynamics phenomena.
Acknowledgements
This work was supported by a grant from NSF, CHE-1412060

References

Han, Q.; Georgescu, N. S.; Gibbons, J.; Scherson, D. Acta
2019, 325, 134957.
(Invited) Multi-Scale Battery Modelling: Understanding Coupled Electrochemical and Mechanical Effects
Authors: Wu Billy , Ai Weilong , Kirkaldy Niall , Bonkile Mayur , Jiang Yang , Naylor-Marlow Max , Patel Yatish , Liu Xinhua , Wang Huizhi , Martinez-Paneda Emilio , Offer Gregory James 
Published: September 2023

Lithium-ion batteries are a key enabler for a low-carbon future, however their performance and lifetime are influenced by complex and coupled electrochemical, thermal and mechanical factors across different length and time scales. In this talk, we explore the key degradation modes which limit battery lifetime and how multi-scale models can help describe these effects towards improved cell designs and device control. At the particle level, we explore how phase field fatigue models of cathode particles can be used to understand crack growth, leading to loss of active material [1]. Here, non-linear crack growth is observed due to the fatigue of material properties and crack merger, leading to a transition from slow to rapid crack growth rate. Use of these models can identify critical C-rates and particle sizes which mitigate cracking.
At the continuum scale, we then investigate how these stresses evolve during cycling, with stress heterogeneities arising initially at the electrode-separator interface, but later propagating to the electrode-current collector interface [2]. We then extend this study to composite anodes of graphite and silicon, where highly non-linear behaviour is observed [3]. Here, the graphite phase provides the majority of the reaction current density at high state-of-charge operation, with this then shifting to the silicon phase at low state-of-charge operation, with hysteresis effects observed due to the silicon. This behaviour is attributed mostly to the different open circuit potentials of the two different phases. Finally, we explore how these effects propagate to the battery pack scale and highlight the divergence of material level performance from their real-world implementation [4].
References
[1] A coupled phase field formulation for modelling fatigue cracking in lithium-ion battery electrode particles. W Ai, B Wu, E Martínez-Pañeda. Journal of Power Sources. 2022.
[2] Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in Lithium-Ion Pouch Cells. W Ai, L Kraft, J Sturm, A Jossen, B Wu. Journal of the Electrochemical Society. 2019.
[3] A composite electrode model for lithium-ion batteries with silicon/graphite negative electrodes. W Ai, N Kirkaldy, Y Jiang, G Offer, H Wang, B Wu. Journal of Power Sources. 2022.
[4] The effect of cell-to-cell variations and thermal gradients on the performance and degradation of lithium-ion battery packs. X Liu, W Ai, M Naylor Marlow, Y Patel, B Wu. Applied Energy. 2019.

Figure 1
(Invited) Three-Dimensional Modeling of Dendrite Formation during Rapid Charging
Authors: Shimpalee Sirivatch , Lopata Joseph Steven , Garrick Taylor R. , Teel Hunter 
Published: September 2023

Dendrites commonly form during rapid charging of lithium-ion batteries when plating is favored over intercalation. If not mitigated, the potential exists for dendrites to growth through the separator and short the anode and cathode. One metric for diagnosing plating conditions in a computational model is the minimum local potential at the anode, where plating is seen to be thermodynamically favorable below 0.0 V with respect to Li metal.[1, 2] Three-dimensional (3D) models can offer insight to predict the possibility of local lithium plating and can be used as a tool to detect possible global lithium plating onset, so it follows that 3D microstructure models can be used to predict dendrite formation and propagation as well. The volume of fluid (VoF) method, which is commonly used to model the flow of immiscible fluids, can be used to divide the electrolyte region into a liquid phase and a dendrite phase.[3]

This strategy is implemented in a 3D microstructure model of a lithium-ion battery to observe the growth of dendrites at the anode during rapid charging. Under onset conditions at the solid/electrolyte interface (SEI), the solid metal phase is deposited at the SEI. Upon growth, the overall electrolyte resistance at the tip of the metal phase is reduced, leading to dendrite growth. The 3D model provides valuable insight into various conditions that may cause plating, if dendrites are formed due to plating, and whether the dendrites formed may proceed to a shorting even
Reference

T. R. Garrick, J. Gao, X. Yang and B. J. Koch, J Electrochem Soc, 168, 010530 (2021).

U. Janakiraman, T. R. Garrick and M. E. Fortier, J Electrochem Soc, 167, 160552 (2020).

T. Jang, L. Mishra, S. A. Roberts, B. Planden, A. Subramaniam, M. Uppaluri, D. Linder, M. P. Gururajan, J.-G. Zhang and V. R. Subramanian, J Electrochem Soc, 169, 080516 (2022).
(Invited) Dendrite Formation in Lithium Electrodeposition – Insights from Experiments and Modeling
Authors: Maraschky Adam M. , Wasalathanthri Ruwani , Akolkar Rohan 
Published: September 2023

High-energy density batteries are essential for powering future electric vehicles and electric aircrafts. The Li-metal anode offers the highest theoretical specific energy among practically available anode materials; however, a major hurdle in the progress towards rechargeable Li-metal batteries is the dendritic electrodeposition of Li metal during the battery charging process. The physicochemical mechanisms behind Li dendrite formation have been a topic of rich and fervent research, but many questions remain presently unanswered. Among these, a key question of significance to battery charging is: at what time instant is Li dendritic growth initiated? In the present talk, this question will be addressed through a combination of experiments and modeling. Chronopotentiometry during galvanostatic Li electrodeposition coupled with in situ optical microscopy reveals that the first emergence of Li dendrites corresponds to the time instant when the surface overpotential reaches a maximum.1 Electrochemical impedance spectroscopy shows that the increase in the surface overpotential preceding this maximum is due to gradual growth of the solid electrolyte interphase (SEI). This SEI layer growth induces Li+ concentration depletion at the electrode-SEI interface, promoting amplification of surface asperities and thus dendrites that rupture the SEI. A transport model incorporating Li+ diffusion across a growing SEI layer is developed, and shown to predict Li dendrite onset times that are in good agreement with experimental observations over a wide range of current densities, temperature, and initial SEI thicknesses.2 A key conclusion of our work is that liquid-phase mass transport limitations, often characterized by the Sand’s equation, do not reliably predict the onset of Li dendrites.3 Rather, transport characteristics and associated instabilities within solid-phase surface films on Li are predominantly responsible for dendrite formation. Our work provides clues for how dendrite formation could be arrested for enabling rechargeable Li-metal anodes for use in next-generation batteries.
References:

A. Maraschky and R. Akolkar, “Mechanism Explaining the Onset Time of Dendritic Lithium Electrodeposition via Considerations of the Li+ Transport within the Solid Electrolyte Interphase”, J. Electrochem. Soc., 165 (14), D696-D703 (2018).

A. Maraschky and R. Akolkar, “Temperature Dependence of Dendritic Lithium Electrodeposition: A Mechanistic Study of the Role of Transport Limitations within the SEI”, J. Electrochem. Soc., 167, 062503 (2020).

R. Wasalathanthri and R. Akolkar, “Perspective—does the sand equation reliably predict the onset of morphological evolution in lithium electrodeposition?”, J. Electrochem. Soc., 169, 092519 (2022).
(Invited) Experimental and Data Analysis Platforms to Better Understand the Corrosion and Discharge of Powdered Zn
Authors: Lenhart Brian , Zuraw Michael , Mustain William Earl 
Published: September 2023

Zinc anodes have been a cornerstone of many commercial and pre-commercial battery technologies including primary and secondary alkaline cells, Zn-air batteries and Zn-ion batteries. To maximize the performance of devices at both the lab and commercial scales, moderate surface area (mm-sized) Zn powder is used – either immobilized onto a diffusion electrode or incorporated into a slurry. Unlike nano-sized materials, micron-sized materials are poorly characterized by traditional electrochemical techniques like thin-film rotating disk electrodes, because the roughness introduced by the relatively large particles disrupts the ideal fluid flow near the surface, negating the mathematical description of the electrode behavior. This leads many researchers to try and characterize the Zn behavior in more application-like cells. The downside of doing this is that there is a strong influence of the counter electrode, electrolyte solution and the efficacy of the specific individual in assembling the cells. Therefore, there is a need to develop new platforms that have the ability to determine the electrochemical properties of powdered Zn – most notably achievable capacity and corrosion current – and to develop analytical models to describe their behavior.
Over the past several years, our group has been developing new platforms to understand how Zn behaves during discharge and charge, including 1-D cells and cells that mimic the geometry of a primary alkaline battery [1]. However, those platforms have their limitations. In this talk, another new cell, coined the “gravity cell” will be discussed. The gravity cell has a small well where Zn (either as raw powder or in slurry form) is gravity deposited onto an indium foil current collector. Typically, a gel electrolyte is placed on top of the Zn bed, followed by liquid electrolyte, which also contains the counter and reference electrodes. The gravity cell is used to both discharge the Zn powder and to measure its corrosion characteristics. This can be done as a function of mass loading, which introduces limitations into the behavior as the Zn discharges from the Zn/gel interface towards the current collector. This type of discharge, limited by Ohmic resistance, requires new mathematical frameworks to extract the desired parameters. This talk will focus on the design of the cell as well as the derivation of the mathematical framework. Then, data will be shown using several types of Zn powder both in the presence and absence of surfactants that aim to reduce the corrosion rate.
References:

Faegh, T. Omasta, M. Hull, S. Ferrin, S. Shrestha, J. Lechman, D. Bolintineanu, M. Zuraw and W.E. Mustain, “Understanding the Dynamics of Primary Zn-MnO2 Alkaline Battery Gassing with Operando Visualization and Pressure Cells”, J. Electrochem. Soc., 165 (2018) A2528-A2535
Efficient Determination and Analysis of Model-Based Optimal Charging Protocols to Minimize Li-Ion Battery Degradation
Authors: Uppaluri Maitri , Subramaniam Akshay , Lim Jin-hyung , Subramanian Venkat R. 
Published: September 2023

Alternate charging protocols for lithium-ion batteries have been proposed to reduce aging and improve their lifetime.1 Physics-based models capture the mass and charge transport of lithium ions within the cell by solving their corresponding governing equations. Optimal charging profiles can be derived from physics-based models by controlling the cell's internal state and restricting phenomena that lead to cell degradation.
Our previous efforts have shown that optimal charging profiles that restrict intercalation-induced stresses, lithium plating, and the SEI layer growth can be derived with reformulated physics-based models. 2
-
4

Deriving optimal charging profiles in real-time can be performed through the optimization of reduced-order models. This work demonstrates several optimal charging profiles derived from the Tanks-in-Series model 5 to minimize various degradation mechanisms. We test these profiles under several cycling protocols and analyze the effect of each mechanism on capacity loss. We have also shown cases of deriving optimal profiles during the cycling period to update the profile based on the aging trajectory of the cell.

References

A. Tomaszewska et al., eTransportation, 1, 100011 (2019).

B. Suthar, V. Ramadesigan, S. De, R. D. Braatz, and V. R. Subramanian, Phys. Chem. Chem. Phys., 16, 277–287 (2014).

B. Suthar, P. W. C. Northrop, R. D. Braatz, and V. R. Subramanian, J. Electrochem. Soc., 161, F3144–F3155 (2014).

M. Pathak, D. Sonawane, S. Santhanagopalan, R. D. Braatz, and V. R. Subramanian, ECS Trans., 75, 51–75 (2017).

A. Subramaniam et al., J. Electrochem. Soc., 167, 013534 (2020).
Using Reference Electrodes to Validate Parameterization of Individual Electrode Soh in the Single Particle Model with Electrolyte
Authors: Pannala Sravan , Olide Everardo , Siegel Jason 
Published: September 2023

Most model parametrization techniques for reduced order physics-based Lithium-ion battery models like the single particle model rely on minimizing the error in the measured and predicted cell terminal voltage. The battery terminal voltage is the difference between the voltages of the positive and negative electrode solid phase potentials. If the individual contribution of each electrode overpotential to the terminal voltage, is not measured, there could be multiple combinations of parameters that provide similar RMSE for voltage prediction, yielding non-unique solutions and local minima. Misattributing the parameters for kinetics and diffusion in each electrode could result in increased plating or over-conservative designs at higher operating currents.
Adding a reference electrode to the battery allows us to measure the individual electrode potentials experimentally. Using this extra measurement, we can parametrize the kinetic and diffusion reactions in positive and negative electrodes with better confidence [1], and validate the electrode-specific state of health parameterization, specifically the increased resistances due to SEI growth and shift in the stoichiometric window as the cell ages [2,3]. In our experiments, we used a pouch cell manufactured in the U of M Battery Lab with Targray NMC 622 Single Crystal cathode powder, Superior SLC 1520-T anode powder, and a custom fabricated Lithium Iron Phosphate (LFP) reference electrode deposited on a porous electrode layer. Further details of the reference electrode are given in [4]. The model was parametrized using discharge data at different C-rates and a modified Hybrid Pulse Power Characterization test. Finally, the RMSE for both the modeled negative and positive electrode potentials are calculated for the validation data set which uses the US06 Drive cycle.

References:

[1] Zhengyu Chu et al., Journal of Energy Storage, Volume 27 (2020).
[2] Suhak Lee et al 2020., J. Electrochem. Soc., 167, 090531 (2020).
[3] Hannah M. Dahn et al., 2012 J. Electrochem. Soc. 159, A1405 (2012).
[4] Taylor R. Garrick et al., J. Electrochem. Soc. 168, 010530 (2021).