bluuebunny/crossref_metadata_2025_split huggingface dataset:
Studies of Hydrogen Desorption in Graphite Using Extreme Time Scale Chronoamperometry in a Molten Salt Environment
Authors: Bruni Colton , Vergari Lorenzo , Williams Haley , Scarlat Raluca O. 
Published: August 2024

Controlling tritium is a major issue in developing nuclear reactors which employ molten salts; to aid this, we must understand tritium retention in these reactors. An important factor that must be considered for tritium retention is the effect the molten salt environment has on the interactions between tritium and the graphite moderator. Traditionally, Thermal Desorption Spectroscopy (TDS) is used to study the adsorption of hydrogen and its isotopes onto graphite; however, this is done in a vacuum at a constant heating rate to remove desorbed hydrogen where the gas is then measured for hydrogen gas concentration vs. temperature. To test in a molten salt environment, a method is needed to create a concentration gradient to drive diffusion of hydrogen to the surface of the graphite since a vacuum cannot be applied. This can be done electrochemically using a graphite working electrode submerged in salt by applying a constant oxidizing potential. Desorbed hydrogen gas is oxidized by this potential on the surface of the graphite, creating a concentration gradient which continuously drives diffusion as the graphite and salt is heated. This technique, known as Electrochemical Thermal Desorption Spectroscopy (ETDS) can be used to study tritium interactions with graphite in molten salt using hydrogen as a surrogate.
Previous attempts of ETDS used chronoamperometry with a constant heating rate to determine current peaks and relate them to hydrogen desorption peaks from various traps in graphite identified in TDS literature. The largest problem facing this previous work was the contribution to measured current from sources other than hydrogen oxidation at the surface. Work has been done to use a “blank” sample of graphite to run heated chronoamperometry and act as a baseline to subtract from the “charged” sample data to find current contributions only from hydrogen oxidation. Additionally, characterization of the salt is being done to determine a theoretical background current based on the kinetics of possible reactions and the effects of overpotential, concentration and high temperature.
Current challenges facing ETDS development are being studied. In this work, we examine the contributions of background reactions to measured current, as well as current contributions due to the oxidation of hydrogen within the electrode rather than at the surface. The answers to these questions will increase signal to noise ratios of ETDS, allowing the technique to extract valuable kinetic parameters of hydrogen interactions and quantify hydrogen in graphite in a molten salt environment.
Cyclic Electrogravimetry Reveals Details of Copper Electrodeposition in the Presence of Additives
Authors: Leppin Christian , Langhoff Arne , Johannsmann Diethelm 
Published: August 2024

Copper electroplating was studied based on cyclic voltammetry combined with a new type of acoustic microgravimetry. The instrument operates similarly to a quartz crystal microbalance with dissipation monitoring (EQCM-D), meaning that it reports shifts in resonance frequency, Δf, and half bandwidth, ΔΓ, on several overtones. Compared to the existing instruments, the time resolution was improved by a factor of 10 down to 10 ms using multifrequency lockin amplification. The frequency noise after accumulation was reduced below 10 mHz ≙ 0.12 ng/cm2 ≙ 0.14 pm (assuming ρ = 8.96 g/cm3) by modulating the electrode potential and averaging of Δf and ΔΓ. Mostly, Δf and ΔΓ are in line with the Sauerbrey prediction (Δf/n
≈const. and ΔΓ/n < -Δf/nwith n the overtone order). Small unsystematic deviations between overtones due to compressional wave effects are seen but no evidence for strong roughness or viscoelasticity. Further, the Faraday efficiency is almost unity, meaning that Δf is close to the expectations derived from the electrical current. The improved sensitivity enables to determine the time derivative of the frequency shift, which is equal to an apparent mass transfer rate that can be compared to the current directly. Again, small deviations are seen for bulk deposition and stripping. These go back to weak roughness effects most pronounced in the initial phase of bulk deposition and, also, to the presence of a Cu(I) intermediate during dissolution. When adding benzotriazole (BTA) or thiourea (TU) as plating additives, the gravimetric and the voltammetric response changes. For BTA, the deviations between mass transfer rate and current vanish indicative of decreased roughness. For TU additional features appear, related to a potential-dependent adsorption of TU and to increased underpotential deposition (UPD). In the presence of TU, the electrode-electrolyte interface displays viscoelasticity, evident by the bandwidth increases during deposition.[1]

Caption: Currents, time derivatives of the frequency shift, and bandwidth shift obtained during copper electrodeposition without additives (A), in the presence of 10 µM benzotriazole (B), and in the presence of 10 µM thiourea (C). Reprinted with permission from Reference [1].
References
[1] Leppin, C.; Langhoff, A.; Höfft, O.; Johannsmann, D. A Modulation QCM Applied to Copper Electrodeposition and Stripping. Electroanalysis
2021, 33 (12), 2529–253.

Figure 1
(Digital Presentation) The Effects of Heat Treatment on Electroreduction of Tungsten (VI) Oxide
Authors: Medvezhynska Olha , Kuleshov Serhii , Omel'chuk Anatoliy 
Published: August 2024

According to the classic scheme of the FFC Cambridge process, oxides of refractory metals are reduced in the solid state. Before electrolysis, oxides are shaped into cylindrical preforms and annealed at high-temperatures (~1000 °С). Therefore, the purpose of this study is to establish the effect of heat treatment of the initial sample of tungsten trioxide on its electrochemical reduction on a solid cathode in a CaCl2–NaCl melt.
There are two types of tablets (preforms) were used for research: non-sintered (only compressed) WO3 and annealed at a temperature of 1000 °C. The surface of a pelletized tungsten trioxide sample without sintering is characterized by a uniform distribution of particles, shown in the SEM image (Fig. a). The particles agglomeration is characteristic for the sample WO3 after heat treatment (1000 °С) (Fig. b), which leads to a decrease in the area and an increase in the size of the voids. The unsintered sample has a larger surface area between the particles due to their dispersed state, which facilitates the penetration of the melt compared to the annealed sample.
Electrochemical reduction of pressed WO3 samples (d=0.8 cm, h=0.15±0.20 cm, m=0.45–0.50 g) was carried out under galvanostatic conditions (800 °С) with using steel as the cathode and graphite as the anode. It was established that preliminary heat treatment of WO3 does not contribute to its electrochemical reduction. After applying 1.20 A·h of electricity, the porous granules of unsintered WO3 were completely transformed into metallic tungsten (W), which is shown in Fig. c, line 1. However, sintered samples after electrolysis (quantity of electricity is 1.42 A·h) reduced only partially: W – 74.1%; CaWO4 – 25.9% (Fig. c, line. 2).
The results show that the reduction occurs more intensively in samples with more micropores, through which mass transfer occurs at the phase interface: tungsten compound to be reduced molten electrolyte mixture. That is why in the unsintered WO3 sample, the electrochemical reduction is faster. It can be assumed that the degree of reduction of WO3 on a solid electrode in CaCl2–NaCl also depends on its crystal structure. The annealed sample (tetragonal structure) is less susceptible to electrochemical reduction compared to the unannealed sample of tungsten trioxide (monoclinic structure). This is due to the fact that tetragonal WO3 (above 740 °C) is resistant to chemical attack, a poor conductor of electricity and has a low surface area. Monoclinic WO3 (from 17 to 330 °C) is more reactive, a better conductor of electricity and has a higher surface area.

Figure 1
Reconstructing the Pseudo-2-Dimensional (p2D) Model in Comsol Multiphysics Using Mathematical Interfaces
Authors: Mishra Lubhani , Jang Taejin , Garrick Taylor R. , Subramanian Venkat R. 
Published: August 2024

The classic ‘pseudo-2-dimensional’ (p2D) model of Newman and co-workers (also known as the DFN model), with its different efficient implementations, is the state-of-the-art physics-based electrochemical model for lithium-ion batteries. COMSOL Multiphysics (COMSOL) is a popular modeling software for multiphysics simulations, and its Battery Design Module for Li-ion batteries is widely used in the battery modeling community. This work presents a step-wise model development for reconstructing the detailed p2D model equations. This includes variable model parameters and weak-form implementation for the solid-phase diffusion, eliminating the need for non-local couplings and approximations for modeling the pseudo radial-dimension in the solid-phase.
To model and understand the effect of capacity degradation on lithium-ion cells, the battery models in COMSOL need to incorporate additional degradation mechanisms. In this work, we have followed the approach of equation-based modeling in COMSOL, which enables us to formulate the p2D model using the mathematical PDE interfaces. This approach provides us with a lot of flexibility in terms of battery degradation model formulation and helps develop a clearer understanding of the p2D model equation in light of the assumptions involved.
The steps in the model development involved writing the conservation equations in the generic mathematics interfaces coupled with the additional equations for the degradation mechanisms. The efforts were centered on developing cycling simulations based on Events for practically relevant operating conditions. This custom model was validated against the results from COMSOL’s Battery Interface, in-house codes, and baseline cycle data from the experiments and further developed as a physicochemical degradation model for automotive, large-format Lithium-ion batteries.
This equation-based COMSOL model with cycling works without the requirement for Battery Design Module, LiveLink and Application Builder and can be adopted for a variety of electrochemical modeling applications by the electrochemical modeling community.
Maximizing Energy Density of Full-Cell Storage Devices Using Topology Optimization
Authors: Roy Thomas , Li Hanyu , Brady Nicholas , Bucci Giovanna , Cross Nicholas R. , Ehlinger Victoria Marie , Lin Tiras Y , Salazar de Troya Miguel A , Tortorelli Daniel , Worsley Marcus Andre 
Published: August 2024

Typical porous electrodes are homogeneous, stochastic collections of micron-scale particles offering few opportunities for engineering higher performance. To leverage recent breakthroughs in advanced and additive manufacturing, we use topology optimization to design porous electrodes for electrochemical energy storage devices such as batteries and supercapacitors. Energy density is maximized, leading to non-trivial geometries that outperform monolithic electrodes. These geometries facilitate ionic transport and lead to better electrode utilization and energy efficiency. We consider simultaneous optimization of cathode and anode, which can lead to interdigitated/interpenetrating designs.
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was supported by the LLNL-LDRD program under project number 23-SI-002 and 20-ERD-019.
LLNL-ABS-857450
Degradation Studies of Fuel Cell Cathode Catalyst Layers Using Machine Learning
Authors: Sanumi Oluwafemi Joseph , Schneider Patrick , Batool Mariah , Ortiz Richard , Singh Rajveer , Gerteisen Dietmar , Beykal Burcu , Zamel Nada , Jankovic Jasna 
Published: August 2024

Fuel cells are a promising technology that has the potential to significantly revolutionize the energy sector by providing clean and sustainable sources of power for transportation, aerospace, and electricity applications. However, optimizing the performance of these devices remains a significant challenge as it largely depends on the catalyst layer in the electrodes of these devices, composed of carbon-supported Pt nanoparticle catalyst (Pt/C), perflurosulfonic acid (PFSA), ionomer binder, and a solvent. The catalyst layer design and operation require extensive knowledge of complex physical and chemical processes especially when achieving a low (<0.1 mgcm-2) Pt loading.
Furthermore, the degradation of fuel cells is usually associated with either mechanical and/or electrochemical processes of the catalyst layer during operations, ultimately resulting in performance loss. While combinations of factors are responsible for the degradation of the catalyst layer in the fuel cell electrodes, it is still challenging to evaluate which factor contributes the most to the degradation of the catalyst layer.
Machine learning algorithms, being powerful tools for predicting and optimizing the behavior of systems can be used to model complex processes, identify optimal operating compositions, and conditions, and reduce the laborious time and cost required for experimentation. They can be used to analyze data obtained from various experiments to identify patterns, trends, and anomalies. In this study, machine learning algorithms are employed for the prediction of catalyst layer degradation. Furthermore, an assessment of feature importance is conducted, elucidating the relative contributions of individual features to the degradation of catalyst layers. Leveraging on machine learning, we can develop an efficient, reliable, and cost-effective fuel cell system.
(Invited) Computational Framework and Insights Towards Electrolyte Design for Redox Flow Batteries
Authors: Mayes Maricris L. , Visayas Benjoe Rey B. , Abdulai Ahmed , Agar Ertan , Cappillino Patrick J. 
Published: August 2024

Achieving a fully renewable energy future requires the development of grid-scale energy storage technologies, and non-aqueous redox flow batteries (NRFBs) hold considerable potential.1 However, the redox-active materials (RAMs) currently in consideration as lead candidates for NRFBs exhibit shortcomings, including low solubility, impaired transport properties, and low reduction potentials. Utilizing theory and computation is invaluable in predicting the structure-property relationship of RAM as NRFB active materials. The numerous components within NRFB electrolytes, spanning solvents, active materials, and supporting salts, suggest that material discovery could experience a substantial acceleration through computational and machine-learning approaches.
This presentation will provide a progress update on our efforts towards a computational framework involving quantum mechanical (QM), molecular dynamics (MD), and machine learning methods to predict fundamental chemical and physicochemical properties of redox-active materials for energy storage systems. We used an extremely stable, bio-inspired vanadium(4+) bis-hydroxyiminodiacetic acid (VBH) as the scaffold to explore the large parameter space of NRFB systems.2–4 First, we show our framework based on density functional theory (DFT) for calculating solubilities, incorporating both lattice and solvation energies, that can be calibrated and used with synthetic verifications. We found that lattice free energy, which has previously been neglected, has a significant role in tuning electrolyte solubility. Second, we describe our MD simulations on the concentration and composition dependence of transport properties, such as viscosity, using DFT-derived force field bonded parameters for the novel [VBH]2- anion to understand the interactions between the ions and the solvent. Third, we demonstrate that QM molecular design could finely adjust the reduction potential. These fundamental investigations would offer insights into electrolyte design and how molecular properties dictate the macroscopic properties that contribute to specific performance aspects of advanced battery electrolytes.
1 Y. Huang, S. Gu, Y. Yan and S. F. Y. Li, Current Opinion in Chemical Engineering, 2015, 8, 105–113.
2 H. Huang, R. Howland, E. Agar, M. Nourani, J. A. Golen and P. J. Cappillino, J. Mater. Chem. A, 2017, 5, 11586–11591.
3 S. K. Pahari, T. C. Gokoglan, B. R. B. Visayas, J. Woehl, J. A. Golen, R. Howland, M. L. Mayes, E. Agar and P. J. Cappillino, RSC Adv., 2021, 11, 5432–5443.
4 B. R. B. Visayas, S. K. Pahari, T. C. Gokoglan, J. A. Golen, E. Agar, P. J. Cappillino and M. L. Mayes, Chem. Sci., 2021, 12, 15892–15907.
Accelerating Battery Research through Atomistic and Multi-Scale Computational Chemistry
Authors: Goumans Theodorus P.M. , Onofrio Nicolas 
Published: August 2024

Computational screening and advanced insights from simulations will help you develop new and improved battery materials quicker and with less resources. We will discuss our efforts to help researchers to do that with the Amsterdam Modeling Suite (AMS). The AMS framework separates the atomistic engines (DFT, DFTB, ReaxFF, ML potentials) from the driver, which enables the exploration of potential energy surfaces (PESs), mechanical, and electronic properties at several levels of theory. The central AMS driver supports advanced PES explorations, molecular dynamics (MD) and Grand Canonical Monte Carlo (GCMC), based on energies and forces from the engines.
With this driver-engine set up, you can calculate and predict relevant processes and properties for battery materials. We will discuss recent developments and examples using:

DFT and GW with COSMO-RS for accurate electrode and redox potentials

(e)ReaxFF for discharge processes including diffusion, discharge profiles, electrolyte degradation, and solid-electrolyte interface formation

polarizable force fields for accurate viscosities and charge mobilities

pre-parametrized and on-the-fly machine learning potentials for diffusion barriers, intercalation potentials, and reactive processes

kinetic Monte Carlo to describe mesoscale processes such as dendrite formation

We will also discuss where we envision future ML and multi-scale developments to accelerate materials discovery, particularly focusing on battery applications.

Figure 1
Rate-Dependent Formation Processes in Li-O2 Batteries: Phase-Field Modeling with the Marcus Kinetics
Authors: Zhang Penghao , Bai Peng 
Published: August 2024

Lithium-oxygen batteries (Li-O2) present a compelling prospect for the next generation of batteries owing to their exceptionally high theoretical energy density.[1] Yet, the performance of Li-O2 batteries is significantly limited by the current-dependent morphology of insulating oxides.[2-3] Existing mathematical models using Butler-Volmer kinetics exhibit uncertainties and inaccuracies.[4-5] In this study, we propose the integration of Marcus kinetics with the existing phase-field model to resolve a few outstanding inconsistencies between theoretical predictions and experimental discoveries. Notably, utilizing the Marcus kinetics enables a quantitative assessment of the role of solvation energy in current-dependet particle growth as opposed to film growth. Experimental results further verify the new theoretical predictions. This research offers valuable insights for the future design of Li-O2 batteries.
[1] Bruce, P. G., Freunberger, S. A., Hardwick, L. J., & Tarascon, J.-M. (2012). Nucleation and growth of lithium peroxide in the Li-O2 battery. Nature Materials, 11, 19–29.
[2] Read, J. (2002). Characterization of the Lithium/Oxygen Organic Electrolyte Battery. Journal of The Electrochemical Society, 149, A1190-A1195.
[3] Kraytsberg, A., & Ein-Eli, Y. (2011). Review on Li–air batteries—Opportunities, limitations and perspective. Journal of Power Sources, 196, 886-893.
[4] Lau, S., & Archer, L. A. (2015). Nucleation and growth of lithium peroxide in the Li-O2 battery. Nano Letters, 15(9), 6108–6114.
[5] Horstmann, B., Gallant, B., Mitchell, R., Bessler, W. G., Shao-Horn, Y., & Bazant, M. Z. (2013). Rate-dependent morphology of Li2O2 growth in Li-O2 batteries. Journal of Physical Chemistry Letters, 4(24), 4227–4232.
(Invited) Operando Visualization of the Electrolyte Distribution in Vanadium Redox Flow Electrodes
Authors: Zeis Roswitha 
Published: August 2024

Electrolyte transport losses are among the main factors that limit the performance of Vanadium Redox Flow Batteries (VRFBs)1. The electrolyte must percolate into the electrode and reach the reaction sites with minimum resistance to reduce such losses. The optimized electrolyte flow ensures that the reactants and products are transported to and away from the reaction sites freely and decreases the pumping losses of the electrochemical cell, increasing the overall efficiency of the battery system.
The electrolyte distribution in a porous electrode is crucial for the stability and performance of the electrode. The presence of gas bubbles in the electrode reduces the electrochemically active surface, resulting in cell potential fluctuations. In addition, the gas bubbles cause poor contact between the electrolyte and the electrode, and the ionic conductivity could be locally blocked2. These effects lead to inhomogeneous potential distribution (spikes), accelerating the electrode material's corrosion. Gas bubbles are very common in VRFBs, and they result from parasitic side reactions, such as carbon corrosion, hydrogen evolution (HER), or incomplete electrode wetting3.
Synchrotron X-ray imaging is a powerful tool for tracking these bubbles in porous carbon materials. We have designed an experimental setup resembling an operating VRFB cell to image the flow through the porous carbon electrode. With its modular structure, the setup offers excellent flexibility to evaluate different designs and operational aspects of VRFBs4, 5. The findings of our investigations over the years will be discussed in this presentation.

References

J. Kim and H. Park, Journal of Power Sources, 2022, 545, 231904.

K. Krause, C. Lee, J. K. Lee, K. F. Fahy, H. W. Shafaque, P. J. Kim, P. Shrestha and A. Bazylak, ACS Sustainable Chemistry & Engineering, 2021, 9, 5570-5579.

M.-A. Goulet, M. Skyllas-Kazacos and E. Kjeang, Carbon, 2016, 101, 390-398.

L. Eifert, N. Bevilacqua, K. Köble, K. Fahy, L. Xiao, M. Li, K. Duan, A. Bazylak, P.-C. Sui and R. Zeis, ChemSusChem, 2020, 13, 3154-3165.

K. Köble, L. Eifert, N. Bevilacqua, K. F. Fahy, A. Bazylak and R. Zeis, Journal of Power Sources, 2021, 492, 229660.

Figure 1