bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding the Electrolyte/Electrode Interfacial Interactions for the Development of High-Performance Aqueous Redox-Enhanced Electrochemical Capacitors
Authors: Kim Sung Won , Cho Young Hun , Kim Dahye , Yoo Seung Joon 
Published: July 2022

Research in electrochemical energy storage is converging to target systems with battery-level energy density, and capacitor-level cycling stability and power density. One approach is to utilize redox-active electrolytes that add faradaic charge storage to increase energy density of supercapacitors. Aqueous redox-active electrolytes are simple to prepare and to up-scale; and, can be synergistically optimized to fully utilize the dynamic charge/discharge and storage properties of micro/mesoporous carbon based electrode systems. However, aqueous redox-enhanced electrochemical capacitors (redox ECs) have performed relatively poorly, primarily due to the cross-diffusion of soluble redox couples, reduced cycle life, and low operating voltages.
In this presentation, we show that these challenges can be met by the use of liquid-to-solid phase transitions of redox electrolyte molecules, and their reversible confinement in the pores ( > 2 nm) of high-surface-area electrodes. This approach is demonstrated by the use of bromide catholyte and modified hydrophobic cations (e.g., viologens and tetrabutylammonium) that together induce reversible solid-state complexation of Br2/Br3
–. This mechanism solves the cross-diffusion issue of redox ECs without using costly ion-selective membranes, and has the added benefit of stabilizing the reactive bromine generated during charging.
Using the concepts learned from this 1st generation configuration, we further developed high-performance aqueous-based redox ECs by (1) synthesizing differently functionalized/substituted viologen molecules and (2) optimizing the interfacial interactions between the viologens and hierarchically porous-structured carbon electrodes. In the second part of this presentation, we show systematic approaches to explore optimal parameters to improve the energy storage capacity of the device.
Application of Machine Learning Algorithms to Classify and Predict Corrosion Behavior of Stainless Steels in Lactic Acid
Authors: Hakimian Soroosh , Pourrahimi Shamim , Hof Lucas 
Published: July 2022

Corrosion of metals is a critical issue, which causes a considerable amount of loss in industries. There are numerous factors that can influence corrosion, including chemical composition and multiple environmental conditions. Hence, it is difficult to determine the relation between individual environmental factors and corrosion processes based on physics-based corrosion laws or to predict the corrosion life of materials. Predicting corrosion behavior of materials in any type of environment is important since testing materials in each different environment is time-consuming and expensive. Hence, analyzing corrosion data and, subsequently, predicting corrosion behavior needs advanced data mining methods. Recently, machine learning (ML) methods have been extensively used in materials research thanks to their powerful data mining capabilities. By learning from sample data and experience, it automates the searching for knowledge without reliance on predetermined equations. In corrosion research, machine learning algorithms such as random forest, support vector regression, and artificial neural networks have been used to study the corrosion behavior.
Stainless steels are widely used in corrosive environments. This research aims to develop classification methods for predicting stainless steel corrosion behavior in different concentrations of lactic acid and different temperatures. The Handbook of corrosion data was used to gather data on stainless steel corrosion in lactic acid. Outlier, repeated, and missing data were treated during a pre-processing step (Figure 1). Based on the ML results, we can properly predict the corrosion behavior of various grades of stainless steels, in different lactic acid concentration and test temperatures. The best training and testing accuracies are 98.73% and 90.00%, respectively, which are obtained by fitting the decision tree classifier. It is also concluded that the percentage of four essential elements (C, Cr, Ni, Mo) in stainless steel alloys, alongside acid concentration and temperature, can be used as the input data to predict the corrosion behavior. Receiver operating characteristic (ROC) curves indicate that stainless steels with poor corrosion behavior are correctly classified by support vector machine (SVM) multiclassification modeling. Therefore, the SVM algorithm is reliable for detecting stainless steels with poor corrosion behavior, which are highly risky for critical applications if chosen incorrectly.

Figure 1
(Digital Presentation) Multiscale Understanding of Local Structure-Dependent Hydrogen Incorporation in TiO2
Authors: Keilbart Nathan Daniel , Song Youngil , Zhu Yakun , Kweon Kyoung E. , Rodriguez Jennifer , Qiu Roger , Heo Tae Wook , Wood Brandon C. 
Published: July 2022

Hydrogen interaction with metal oxides is an important phenomenon that affects all vital areas of industry such as aerospace, transportation, and commercial applications. The metal oxide provides a naturally forming protective layer against dissolution of the underlying metal. It has been reported that hydrogen is still able to percolate through this protective layer traveling all the way to the metal to form a corrosive brittle metal hydride. There have been many studies looking to understand the interaction of hydrogen at the surface but an in depth look at the surface to bulk transition of hydrogen through a metal oxide for all representative phases of an oxide is minimally represented in the literature. For the focus of our study, we employed a multi-scale approach combined with experimental studies to explore the different phases of titania (TiO2) which includes rutile, anatase, and an amorphous phase. A thermodynamic analysis of the stability for hydrogen was considered using density functional theory (DFT) starting at the low energy surface facets to the bulk. For the amorphous phase, the binding energy was analyzed as a function of the hydrogen content and oxygen coordination environment. Temperature programmed desorption (TPD) experiments provided a direct comparison with theory. Additionally, NMR simulations validified the generated amorphous phase which agreed well with experimental data. The material properties computed using DFT were used in combination with experimental results to parameterize a mesoscale model. Several environmental conditions were analyzed consisting of grain size, temperature, and grain boundary properties. Further work investigated crystalline titanium (Ti) in the bulk and at several grain boundaries to elucidate possible initiation stages of hydride formation.
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
Effect of the Microstructure on the Corrosion and Fatigue Behavior of the Additively Manufactured M789 Stainless Steel
Authors: Yassine Sarah , Laliberté-Riverin Simon , Mena Morcillo Emmanuel , Cova Matteo , Brochu Myriam , Mauzeroll Janine 
Published: July 2022

Corrosion is one of the most destructive on the environment and also on our safety. Not only does the microstructure influence the severity of corrosion, but also does many other chemical and mechanical factors such as fatigue. For this reason, the ability to evaluate and further predict localized corrosion on materials subjected to stress has been a growing interest in an attempt to foresee the fatigue-corrosion damage before escalating into a material failure, years later.
A quantitative and qualitative investigation was conducted to assess the effect of the heterogeneous microstructure on the fatigue and corrosion resistance of the additively manufactured martensitic stainless steel, AMPO M789, employed in application where a high hardness and corrosion resistance is of need. We first examined quantitatively the inclusion content of polished surfaces and the roughness of net-shaped samples, in order to estimate the fatigue strength of the alloy with statistics of extreme values and linear fracture mechanic approach. We then exposed the material to electrochemical tests in synthesized seawater, in order to identify possible corrosion mechanisms and to further assess the pits initiation and their dimensions. Considering those results, we finally predicted a possible performance of M789 in high-cycle fatigue in presence of corrosive environment. We believe our work represents a first step towards studying the synergistic effect of localized corrosion and fatigue strength on the heterogeneity of this new additively manufactured alloy.
(Digital Presentation) Formation Mechanism and Defects of Electrodeposited Ca-P Coating on Biodegradable Mg-Zn-Ca Alloy
Authors: FU Shian-Hwu , Chu Peng-Wei 
Published: July 2022

Biodegradable implants are a new concept to substitute current nondegradable medical materials. The biodegradable implant can decompose in the patients’ body when the treatment is finished and be discharged through metabolism. Mg alloys, with their biocompatibility and high reactivity, meet the requirements of a biodegradable material. Also, Mg has a similar density to the human bones and the lowest elastic modulus among the structural metallic biomaterials, which can mitigate the stress shielding effect. For Mg-Zn-Ca alloys, besides the merits of Mg, Zn is related to bone metabolism and osteopathic phosphate activity, while Ca is the main element of bones that is essential for the synthesis of bone compounds. However, the degradation rate of Mg alloy implants needs to be controlled, or the implant may corrode too fast, causing local hydrogen accumulation and alkalization that hurts the patients. One of the effective ways to improve the alloy corrosion resistance is by coating. Ca-P coatings, such as brushite (dicalcium phosphate dihydrate, DCPD) and hydroxyapatite (HA), can mitigate the degradation rate of Mg alloys and show good biocompatibility and osseointegration. Electrodeposition is a simple way to synthesize coatings on metallic materials with an easy experimental set-up, and the process parameters are highly controllable.
Even though there are plenty of reports about the fabrication of Ca-P coatings on Mg alloys through electrodeposition, few of them focus on the film formation mechanism. Issues related to coating defect formation and inhomogeneity are rarely discussed. Therefore, this report focuses on the film formation mechanism and the relationship to the defects of the electrodeposited DCPD coating on a Mg-2.5wt%Zn-1wt%Ca alloy. By altering the deposition potential, the reduction reactions that proceed on the substrate are different. The involvement of H2O reduction affects the formation of DCPD and changes the microstructure of the coating. Combining electrochemical tests and microstructure analysis, the corrosion properties of the coatings are measured, and the results are linked to the difference in film microstructure and defects. With SEM/FIB cross-sectional observations, the formation mechanism of the coatings and the relationship to the process condition will be discussed. The degradation rate of Mg alloys can be tuned by understanding the DCPD film formation mechanism and the cause of the defects.
Incorporation of Functionalized Graphene and Its Derivates into Electrolyte: A Facile Approach to Improve the Electrochemical Performance of Lithium-Sulfur Batteries
Authors: Shah Vaidik , Joo Yong Lak 
Published: July 2022

The impending global energy crisis and the severe environmental problems in the recent years has bolstered the research for sustainable electrochemical energy storage. With the lithium-ion technology reaching its theoretical limit, lithium sulfur (Li-S) batteries have garnered extensive attention owing to their ultrahigh theoretical capacity and the low-cost, Earth abundant and environmentally friendly nature of the sulfur cathode. Despite all the advantages, the development of Li-S technology is hindered by several inherent challenges namely, the large capacity drain due to the dissolution of the intermediate polysulfides in the electrolyte leading to polysulfide shuttling and low conductivity in the cell.
To overcome these issues, graphene and its derivatives have been extensively explored to mitigate polysulfide shuttling and enhance conductivity in the cell. However, at present, most of this research is limited to cathode modification which despite improving capacity retention is unable to curb the deleterious impact of polysulfide shuttling once the polysulfides dissolve in the electrolyte.
In this work, we have probed the impact of implementing graphene and its derivatives as additives to the Li-S electrolyte. We have extensively characterized various commercially available rGO and graphene samples to determine their morphology, functionalization, and other key ancillary properties such as defects and ionic resistivity and have studied their impact towards the electrochemical performance of the electrolyte-modified Li-S cell. Results showed a marked improvement in initial capacity of 1323 mAh/gs and a capacity retention of 83% at 0.2 C over 105 cycles with single layer graphene additive. Further, it was shown that increasing functionalization led to improved mitigation of polysulfide shuttling and about 20% higher capacity retention (than reference) over 105 cycles. The impact of different functional groups on graphene was also probed to compare their polysulfide shuttling mitigation capacity in the electrolyte.
Overall, we successfully achieved superior electrochemical performance of the Li-S cell simply by addition of graphene to the electrolyte. It was successfully shown that functionalized graphene dispersed in the electrolyte can enhance polysulfide encapsulation and improve the ionic conductivity in the cell.
(Digital Presentation) Self-Discharge in Electrochemical Capacitors: Beyond Conway’s Diagnostics
Authors: N V N Deeksha , Madabattula Ganesh , Kumar Sanjeev 
Published: July 2022

Electrochemical double-layer capacitors (EDLCs), also known as supercapacitors, are energy storage devices. Charge-discharge in EDLCs occurs through electrostatic interactions between electrodes and oppositely charged ions. Self-discharge is the gradual loss of energy when a charged EDLC is left in open circuit state. A comparatively higher rate of self-discharge (SD) than in the batteries prevents the use of EDLCs in stand-alone devices or those charged only infrequently. The process of self-discharge, to the best of our knowledge, is far from being fully understood. It manifests as decrease in open-circuit potential which, depending on the charging protocol, can be dominated by charge-redistribution [1-3] induced rapid decrease in the initial stages.
Conway [4] proposed model-based diagnostics to identify activation, diffusion, and leakage-resistance controlled mechanisms from voltage vs. time measurements. We show that for the limited voltage range window available, the method is subjective and ambiguous for real measurements. We have proposed and carried out an alternative set of extensive experiments to gain insights into the self-discharge process for an in-house capacitor with sulfuric acid as electrolyte and activated carbon as electrodes. We varied electrode thickness, the distance between the electrodes, and the state of mixing in the electrolyte. We also measured self-discharge of electrochemically isolated charged half-cells (Figures 1a,b). The results bring out interesting and unambiguous findings, independent of the degree of fit with the expected variations for various controlling mechanisms. They rule out leakage resistance as the cause of self-discharge and disprove the shuttle mechanism [2,3] (Figure 1c,d). The analysis of measurements with electrodes of different thicknesses establishes that activation process controlled self-discharge does not dominate either. And the predicted effect of charge-redistribution is in the opposite direction. The role of the complex interplay of diffusion and electrochemical reactions holds promise.

Figure 1
(Invited) Photoluminescence Monitored Digital Photocorrosion of GaAs/AlGaAs Nanoheterostructures
Authors: Dubowski Jan J , Vermette Jonathan , St-Onge René 
Published: July 2022

Conventional etching techniques do not have the ability to achieve precision of monolayer etching of crystalline solid materials. In recent years, atomic layer etching (ALE) has emerged as a method addressing this challenge based on the application of self-limiting sequential reactions. Nevertheless, monitoring of the ALE process, important for real time information about the location of the etching front, is not trivial and could be an expensive task. We have investigated the innovative process of digital photocorrosion (DIP) of GaAs/AlGaAs nanoheterostructures, which is based on photoexcitation driven decomposition of these materials.1 The process is carried out in a flow cell filled with etchants designed for selective removal of the photocorrosion products. For instance, water dilutes relatively easily As and most of the Ga oxides, but dilution of Ga2O3 or Al oxides and hydroxides requires dedicated etchants. Under optimized conditions, DIP allows etching of (001) GaAs/AlGaAs nanoheterostructures with average rates approaching 1 Å/cycle. The process could be monitored in situ by measuring, e.g., open circuit potential2 or photoluminescence3 effect. The sensitivity of these effects to the surface states of a semiconductor allows for convenient marking of the location of a GaAs/AlGaAs interface when crossed by the etching front.
In this presentation, I will discuss mechanisms of DIP of GaAs/AlGaAs nanoheterostructures and the conditions for formation of stoichiometric surfaces. Examples of the DIP process applied for formation of an unusual density alkanethiol self-assembled monolayers and operation of optical devices for detecting electrically charged molecules immobilized in the vicinity of a semiconductor-electrolyte interface will also be discussed. Finally, I will also address the potential of this economically attractive invention for the research of other semiconductor materials.
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1. S. Aithal, N. Liu and J. J. Dubowski, Journal of Physics D: Applied Physics 50 (3), 035106 (2017).
2. S. Aithal and J. J. Dubowski, Appl Phys Lett 112 (15), 153102 (2018).
3. M. R. Aziziyan, H. Sharma and J. J. Dubowski, Acs Appl Mater Inter 11 (19), 17968-17978 (2019).
A Thermal Tanks-in-Series Model for Capacity Fade Studies in Lithium-Ion Batteries
Authors: Thiagarajan Raghav Sai , Kolluri Suryanarayana , Uppaluri Maitri , Preger Yuliya , Subramanian Venkat R. 
Published: July 2022

Lithium ion batteries are widely adopted due to their high energy density, low self-discharge rate and fast charging capabilities1. However, during their operation, capacity fade occurs as a result of various parasitic reactions causing loss of cyclable lithium ions. A dominant and widely studied fade mechanism is the formation of the Solid Electrolyte Interphase (SEI) layer caused by the reduction of electrolyte at the anode-electrolyte boundary. Temperature plays a crucial role on the electrochemical performance of individual cells, affecting the diffusivities of ions, kinetics of reactions and rate of degradation of the battery. Thermal battery models allow us to study the thermal behavior of cells and are often used for cell configuration optimization and thermal management system design2.
In literature, thermal models are incorporated with empirical and physics-based models to capture battery dynamics influenced by temperature. Empirical thermal models are simple and fast to solve but are applicable only for specific operating conditions and cannot capture electrochemical subtleties. Conversely, physics-based models use equations that incorporate transport phenomena and electrochemical reactions occurring in the cell. Common physics-based models are the Single Particle Model (SPM) and the Psuedo-2D (P2D) model. The P2D model is robust but computationally expensive3 and the SPM model does not capture electrolyte transport which has a non-linear temperature dependence.
The Thermal Tank-In-Series4,5 model is a systematically volume-averaged form of the P2D model with energy balance equations for the current collectors, cathode, separator and anode. This reduces the number of equations in the model and improves computational speed while maintaining accuracy. This model can be ideal to predict capacity fade during fast charging.
Extending on our previous work, we now couple the Thermal Tanks-in-Series model with the growth of the SEI layer for a single-cell sandwich. This model will facilitate understanding of diffusive, kinetic and temperature effects on the growth of the SEI layer and cell capacity fade.

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