Fitting Parameters Research Articles

Physics-informed neural network for creep-fatigue life prediction of Inconel 617 and interpretation of influencing factors

Providing a comprehensive assessment of the creep-fatigue life of critical structures in nuclear power facilities is important for structural integrity and performance requirements during service. The data-driven modeling method can set aside traditional mechanical theory and use experimental data to drive creep-fatigue performance modeling directly, avoiding complex parameter fitting. A sufficiently robust purely data-driven model must be supported by a large amount of training data, which means significant time and financial costs. To address these issues, a physics-informed neural network (PINN) is proposed in this work to predict the creep-fatigue life of Inconel 617 at high temperatures, where the physical constraints are introduced to enhance the physical fundamentals. The prediction results show that introduced physical constraints promote the stability of prediction performance and analysis based on the strength of physical constraints provides valuable guidance for creep-fatigue modeling to select the suitable architecture for building PINN. The SHapley Additive exPlanations (SHAP) method and dependency analysis are utilized to reveal the intrinsic mechanism of the properties and structure of PINN.

End-Member Mixing Model for Methane Carbon Isotope Fractionation During Shale Gas Desorption and Its Application.

Unlike conventional natural gas reservoirs, shale gas development involves systematic changes in methane carbon isotopes that cannot be effectively described by existing isotope fractionation models and mechanisms. Therefore, based on fundamental theories such as Rayleigh fractionation, mass transfer flow, and mass conservation, this study established isotopic fractionation equations for methane in adsorbed and free gas. By considering adsorbed and free gases as two end-members and using an isotope mixing model, a fractionation model for methane carbon isotopes during shale gas desorption was constructed. This model quantifies the isotopic fractionation effects during shale gas desorption and elucidates the mechanism of methane carbon isotope fractionation. Using on-site desorbed gas content and isotope data, parameter fitting and model calculations were conducted to characterize methane carbon isotope variations throughout the process of shale core field desorption. The results show a pattern of "initially negative and then turning positive," consistent with those of physical simulation experiments. It was clarified that differences in mixing the two end-members and isotopic fractionation play key roles in the variation of methane carbon isotopic composition in shale gas. By applying the methane carbon isotope fractionation model, the contribution of adsorbed gas during shale gas production was explored. It was found that in the early stage of development, the adsorbed gas in Well JY 1 was negligible. After nearly seven years of development, the contribution of adsorbed gas in the later stage has only reached nearly 15%, indicating that the production contribution of adsorbed gas is still less than 0.3 million cubic feet per day. The open flow of Well JY 6-2 is more conducive to the production of adsorbed gas, but the production capacity is still mainly contributed by free gas, indicating that the shale gas production capacity in the later stage in the Jiaoshiba gas field is still primarily dominated by free gas.

Plasma, Urinary, Erythrocyte and Platelet Zinc Concentrations in Soccer Players.

Essential trace minerals are vital for general human health and athletic performance. Zinc (Zn) plays critical roles in several biochemical processes in exercise physiology, especially during intense physical exercise. This research aimed to analyze erythrocyte, platelet, plasma and urine Zn concentrations among men's and female soccer players over a sports season. A total of 22 male soccer players (20.61 ± 2.66 years; 71.50 ± 5.93 kg) and 24 female soccer players (23.37 ± 3.95 years; 59.58 ± 7.17 kg) participated in this longitudinal quasi-experimental study. Three assessments were carried out over the season: 1st evaluation: first week of training (August); 2nd assessment: middle of the season, between the end of the first and second round (January) and 3rd assessment: last week of training (May/June). In all evaluations extracellular (plasma and urine) and intracellular (erythrocytes and platelets) Zn concentrations were determined, as well as physical fitness and several blood parameters. Inductively coupled plasma mass spectrometry was used to measure Zn concentrations. Plasma and urinary concentrations were higher among male soccer players (p < 0.05) while erythrocyte and platelet Zn concentrations were higher in the female soccer players (p < 0.05). Additionally, variations in urinary and platelet Zn concentrations were observed over the season. The differences could be related to muscle mass, muscle damage or the specific sport's physical demands.

Modelling white matter microstructure using diffusion OGSE MRI: Model and analysis choices

Alterations in white matter (WM) microstructure of the central nervous system have been shown to be pathophysiological presentations of various neurodegenerative disorders. Current methods for measuring such WM features require ex vivo tissue samples analyzed using electron microscopy. Magnetic Resonance Imaging (MRI) diffusion-weighted pulse sequences provide a non-invasive tool for estimating such microstructural features in vivo. The current project investigated the use of two methods of analysis, including the ROI-based (Region of Interest, RBA) and voxel-based analysis (VBA), as well as four mathematical models of WM microstructure, including the ActiveAx Frequency-Independent Extra-Axonal Diffusion (AAI), ActiveAx Frequency-Dependent Extra-Axonal Diffusion (AAD), AxCaliber Frequency-Independent Extra-Axonal Diffusion (ACI), and AxCaliber Frequency-Dependent Extra-Axonal Diffusion (ACD) models. Two mice samples imaged at 7 T and 15.2 T were analyzed. Both the AAI and AAD models provide a single value for each of the fit parameters, including mean effective axon diameter AxD¯, packing fraction fin, intra-cellular and Din and extra-cellular Dex diffusion coefficients, as well as the frequency dependence of Dex, βex for the AAD model. The ACI and ACD models provide this, in addition to a distribution of axon diameters for a chosen ROI. VBA extends this, providing a parameter value for each voxel within the selected ROI, at the cost of increased computational load and analysis time. Overall, RBA-ACD and VBA-AAD were found to be optimal for parameter fitting to physically relevant values in a reasonable time frame. A full comparison of each combination of RBA and VBA with AAI, AAD, ACI, and ACD is provided to give the reader sufficient information to make an informed decision of which model is best for their own experiments.

Impedimetric detection of chloride ions using two symmetric Ag/AgCl electrodes

The suitability of Ag/AgCl electrodes for specific detection of chloride ions in aqueous gels is investigated. In contrast to potentiometric sensors, we use an impedimetric approach and avoid the need for a separate reference electrode through a symmetric two-electrode configuration. As such, electrochemical impedance spectroscopy is employed to characterise the response of the Ag/AgCl electrodes in contact with gels at various hydration levels containing different types as well as concentrations of salt. In order to extract quantitative information related to e.g. the ion concentration and mobility from the impedance spectra, an equivalent circuit model is fitted to the data. Here, we develop a simple equivalent circuit model related to the physical parameters of the system, which can be reduced to a modified Randles circuit with only few circuit elements. Importantly, even though the equivalent circuit model contains the same elements as a Randles circuit, their physical origin is different. Systematic variations of ion mobility, ion concentration, and type of ions allow us to evaluate the relationship between those physical properties and the resulting fitting parameters of the modified Randles circuit. This finally enables us to determine chloride concentrations in complex liquids, containing other salts and long chain molecules.

Comparing the Upper Mesospheric Temperature Trend and the Response to Solar Activity Derived From the Daily Mean and Nocturnal Na Lidar Observations

AbstractOver the past decades, various experimental and numerical model studies have indicated cooling trend in the mesosphere and lower thermosphere (MLT), while the magnitude of the trend varies noticeably. Previous studies using the lidar observations derived the temperature trends and solar responses solely from the traditional nocturnal measurements. While these archived results are more or less in agreement with modeling studies, one of the main uncertainties in these studies is the potential biases induced by the trends of the diurnal tide forced in the lower atmosphere, and that of the in situ exothermal reactions involving the photolysis. In the MLT, the diurnal tide has significant seasonal variations, considerable amplitude and is one of the dominant dynamic sources. However, its potential effects in the trend studies have rarely been discussed. In this paper, we present and compare the long‐term temperature trends in the upper mesosphere utilizing the daily mean and nightly mean temperature profiles measured by a Sodium (Na) Doppler lidar at midlatitude. The system was operating routinely in full diurnal cycles between 2002 and 2017, obtaining a unique multi‐year temperature data set. A customized multi‐linear regression (MLR) model is applied to determine the linear trends and the other fitting parameters, such as ENSO and solar F10.7 responses in the upper mesosphere. This study indicates the daily mean cooling trend between 84 and 98 km is larger than that of nightly mean trend by ∼−1 K/decade, while differences in the solar response are within the fitting uncertainties.

Intensive and critical care nurses patient safety, care quality, professional self-efficacy, and missed nursing care: Structural equation model analysis.

Globally, nurses' patient safety, care quality, and missed nursing care are well documented. However, there is a paucity of studies on the mediating roles of care quality and professional self-efficacy, particularly among intensive and critical care unit (ICCU) nurses in developing countries like the Philippines. To test a model of the interrelationships of patient safety, care quality, professional self-efficacy, and missed nursing care among ICCU nurses. A cross-sectional, correlational design study was used. ICCU nurses (n = 335) were recruited via consecutive sampling from August to December 2023 and completed four validated self-report scales. Spearman Rho, structural equation modeling, mediation, and path analyses were utilized for data analysis. The emerging model demonstrated acceptable fit parameters. Patient safety positively influenced care quality (β = .34, p = .002) and professional self-efficacy (β = .18, p = .011), while negatively affecting missed nursing care (β = -.34, p = .003). Care quality positively and negatively influenced professional self-efficacy (β = .40, p = .003) and missed nursing care (β = -.13, p = .003), respectively. Professional self-efficacy indirectly impacted missed nursing care (β = -.32, p = .003). Care quality (β = -.10, p = .003) and professional self-efficacy (β = .13, p = .003) showed mediating effects between patient safety and missed nursing care. ICCU nurses' care quality and professional self-efficacy are essential mediating factors that can bolster patient safety practices, hence reducing missed nursing care. Therefore, healthcare organizations, nurse managers, and policymakers should cultivate care quality and self-efficacy by creating support programs and providing a positive practice environment. Nurses and nurse supervisors could directly observe missed nursing care in the ICCU to understand its underreported causes.

The development and evaluation of a parameter-free circuit-based model of bipolar membrane electrodialysis for process design and optimisation

Bipolar membrane electrodialysis (BPMED) is an emerging electromembrane technology which has the potential to replace existing pH manipulation process units among others and take advantage of the benefits posed by process electrification. The development of robust and flexible process models of BPMED for design and optimisation is paramount in derisking potential instillations and improving commercial viability. Herein, a circuit-based model of BPMED is presented which avoids reliance on empirical fitting parameters and training data. The resulting model is flexible enough that extension to account for added complexities may be readily adopted. The mass transfer and electrical resistance of six different domains (three membranes and three streams) were computed by applying fundamental laws such as Ohm’s law and Faraday’s first law. Acid-base reactions and their effect releasing current within the membranes were also considered. Furthermore, the stack model can be readily embedded in a broader process model. To this end, the stack model is applied to a recirculating-batch experiment using a delayed differential material balance to account for dead-time within the tubing and measurement flow-cells. Two orthogonal methods of experimental validation were conducted to assess the performance of the model over a range of concentrations and applied voltages. These involved running a recirculating-batch experiment and collecting current–voltage polarisation data, respectively, and both showed good agreement with the model predictions. Overall, a robust model of BPMED has been produced which is able to accurately predict system performance and will prove useful for the design and optimisation of industrial systems.

Computer modeling of a new type galactic cosmic rays simulator

A new type of a galactic cosmic rays (GCR) simulator, provided at the JINR Laboratory of Radiation Biology, is potentially capable of generating a complex radiation field with inclusions of a variety of ions with a wide energy range and with required abundance at the charged particle accelerators. This complex multicomponent radiation field simulates radiation environment inside a spacecraft during an interplanetary flight, for example, to Mars. The article provides an analytical description of the GCR simulator as well as a description of a specially developed software that enables selection of necessary parameters of a simulator model for creating relevant mixed radiation conditions. The software implements processing of data obtained with Monte Carlo-based FLUKA and PHITS programs, fitting and optimization of model parameters as well as data visualization tools.

Study of Power Equivalent Continuous Approximation Based on the Recent Consensus Recommendations for Brain Tumor Imaging with Pulsed Chemical Exchange Saturation Transfer at 3T.

The quantitative analysis of pulsed-chemical exchange saturation transfer (CEST) using a full model-based method is computationally challenging, as it involves dealing with varying RF values in pulsed saturation. A power equivalent continuous approximation of B1 power was usually applied to accelerate the analysis. In line with recent consensus recommendations from the CEST community for pulsed-CEST at 3T, particularly recommending a high RF saturation power (B1 = 2.0 µT) for the clinical application in brain tumors, this technical note investigated the feasibility of using average power (AP) as the continuous approximation. The simulated results revealed excellent performance of the AP continuous approximation in low saturation power scenarios, but discrepancies were observed in the z-spectra for the high saturation power cases. Cautions should be taken, or it may lead to inaccurate fitted parameters, and the difference can be more than 10% in the high saturation power cases.

Fitting parameters in children with cochlear implants and severe additional disabilities.

The aim of this study was to investigate whether the fitting data of children with Cochlear implants (CI) and severe additional disabilities (CAD) differ compared to children with CI but without additional disabilities (CnonAD). In this retrospective analysis, 76 CI cases (fitted ears) from CAD were included and matched with 76 cases from CnonAD. The subjective set values, such as pulse width (PW), threshold (T) and most comfortable (MC) levels were recorded. Additionally, the response threshold values by means of aided soundfield threshold (AST), the values of the intracochlear electrode impedances, the eCAP thresholds and the daily wearing time by using data logging were recorded and analysed. The T and MC levels for the Medel implants were significantly lower in the sample than in the control group. A similar trend was evident for Cochlear® implants. The sample showed a positive correlation between the eCAP thresholds and the T level and MC level in case of the Cochlear implants. The CAD group showed a significantly higher aided soundfield threshold. In contrast to the CnonAD (0%), there was a total of 18% in the CAD with a reduced daily wearing time. When these were excluded, eCAP thresholds were significantly higher in the CAD group. In addition, there was a trend for eCAP thresholds to be higher if the contralateral ear was not also fitted with a CI. The significantly higher eCAPs in CAD may be explained by different neural survival but also by different fittings and MC levels. The audiuologist's subjectivity must be considered as a potential bias but also as potentially valuable input variable contributing to the variance in the fitting parameters and outcomes. Better fitting of T and MC levels focusing on behavioural and developmental responses may lead to a longer wearing time. To support the audiologist and the fitting process, it is necessary to interpret the response thresholds in the aided soundfield threshold against the background of the medical diagnosis and observations from everyday life should be considered. To ensure optimal development of CAD children according to their individual abilities, a multi-professional, family-centred intervention programme including videoanalyses should be implemented at an early stage, ideally from the decision process for a cochlear implant.

Empirical formulas for dynamic effective suction head in the modified green-ampt model

The Green-Ampt model, which is a widely used infiltration model, assumes a constant effective suction head during infiltration; however, the capillary pressure is dynamic and related to the saturation rate. One factor affecting the dynamic suction head is the dynamic contact angle, which is parameterized using different formulas. Previous studies applied a power-law formula to parameterize the effective suction head, which substantially improved the ability to describe the velocity-dependent suction head. However, other dynamic contact angle formulas have not been tested. In this study, we performed a series of 1-D downward infiltration experiments with 60-cm sand columns and constructed modified Green-Ampt models (MGAMs) based on four formulas of dynamic contact angle (power-law, Jiang’s law, Hoffman’s law, and Cox-Voinov law) and a linear approximation. These modified Green-Ampt models were named MGAM-PW, MGAM-Jiang, MGAM-Hoff, MGAM-CV, and MGAM-LN. After parameter optimization, all modified Green-Ampt models resulted in similar root-mean-square errors, which were lower than those of the classical Green-Ampt model. The modified Green-Ampt models yielded similar values for the equilibrium suction head, which were larger than those of the classical Green-Ampt model. Among the modified Green-Ampt models, MGAM-CV has an intrinsic limitation in the range of parameter values. MGAM-Hoff typically requires more fitting parameters than do the other methods. MGAM-LN can be solved more efficiently than other modified Green-Ampt models because of its analytical solution and small number of fitting parameters. We demonstrated that the data length and slight time delay of the measurement affected optimization of the equilibrium suction head. Moreover, the relationship between the capillary pressure and saturation rate based on MGAM-PW was similar to the findings in the literature.

The Impact of Body Composition and Physical Fitness on Parasympathetic Reactivation in Firefighters

Objective: Firefighting involves aerobic and anaerobic physical activities that cause heart rates to rise from submaximal to above maximal levels. These varying demands can occur with each call firefighters respond to during their shift, imposing both acute and cumulative cardiovascular loads. Heart rate is commonly used to measure cardiovascular responses during disasters, emergencies, firefighting, and firefighting simulations. There is substantial evidence suggesting that heart rate recovery (HRR) parameters are associated with body composition and aerobic fitness. Therefore, the primary aim of this study is to determine the relationship between body composition, physical fitness, and HRR parameters in firefighters. Method: This cross-sectional descriptive study was conducted among firefighters working in a metropolitan municipality. Using the G-Power 3.1 program, seventy-four firefighters (age = 32.61 ± 8.9 years, height = 1.76 ± 0.6 cm, weight = 83.9 ± 13 kg) volunteered to participate in this study. Body mass index (BMI), waist circumference (WC), and body fat percentage (BFP) were recorded for each subject. To determine aerobic fitness (VO2max), each participant performed a submaximal exercise test on a treadmill. HRR was calculated as the difference between peak heart rates post-exercise (HRmax) and heart rates at the first and second minutes of the recovery phase, recorded as HRR1 and HRR2, respectively. Results: The mean VO2max and BMI of the participants were 48.32 ± 9.18 ml/kg/min and 27.10 ± 3.49 kg/m², respectively. No significant relationship was found between the HRR1 and HRR2 parameters and the variables of BMI, WC, and BFP in firefighters (p&amp;gt;0.05). However, positive significant relationships were detected between HRR1 and HRR2 and VO2max (p&amp;lt;0.05). A statistically significant negative relationship was found between VO2max and the variables of BMI, WC, BFP, and weight (p&amp;lt;0.05). These findings indicate that higher aerobic capacity is associated with better heart rate recovery and lower body fat percentage. Conclusion: The results of this study demonstrate that cardiovascular autonomic function is significantly related to maximum aerobic fitness. However, no measure of body composition appears to affect the overall HRR response of the firefighters. This research provides important insights into how the aerobic capacity of firefighters affects their heart rate recovery responses. These findings offer a crucial foundation for improving the physical fitness levels and maintaining the cardiovascular health of firefighters.

German translation, cultural adaptation for Austria and validation of the Neurological Sleep Index - Multiple Sclerosis (NSI-MS).

To translate, culturally adapt, and validate the Neurological Sleep Index - Multiple Sclerosis (NSI-MS) for use in Austrian German-speaking populations with multiple sclerosis (pwMS). Following established guidelines, the NSI-MS diurnal sleepiness (DS), non-restorative nocturnal sleep (NRNS), and fragmented nocturnal sleep (FNS) scales underwent forward-backward translation, with content and face validity, and cultural adaptation to Austria established. Construct validity was evaluated using Rasch analysis. Known-groups validity was examined, and comparisons were made with scales measuring MS fatigue, daytime sleepiness, sleep quality, anxiety, and depression. Reliability was assessed through Cronbach's alpha, Person Separation Index, Lin's concordance correlation coefficient, measurement error, and floor and ceiling effects. Data were merged with a historic English dataset for comparison between English/German language versions. The translation and cultural adaptation of the NSI-MS-G were successful. Pretesting involved 30 pwMS, while the validation included 400 pwMS with mild-to-severe disability. The DS, NRNS, and FNS scales exhibited good fit parameters, were unidimensional, and invariant. NSI-MS-G scales demonstrated excellent convergent and known-groups validity, internal consistency, person separation reliability, test-retest reliability, adequate measurement error, and low floor and ceiling effects. Pooling English and German datasets revealed that person estimates for the NRNS and FNS scales are equivalent across versions, unlike the DS scale. The NSI-MS-G demonstrates validity, reliability, and responsiveness in assessing DS, NRNS, and FNS in pwMS, generating interval-level data, and shows equivalence between its English and German versions. Register: German Clinical Trials Register (DRKS); URL: https://drks.de/search/en/trial/DRKS00025573; Identifier: DRKS00025573.

Dependence of photophysical properties on stereoisomers: The case of nonsymmetric 2-aryl(benz)imidazole-annulated derivatives

Nonsymmetric 2-aryl (benz)imidazole derivatives were previously synthesized via C-H/N-H annulation of imidazole derivatives from lapachol moieties with internal alkynes as an efficient tool to increase the π-system of lapimidazole derivatives. To investigate their photophysical properties, time-resolved and steady-state fluorescence measurements were carried out on dropcast film, powder, and diluted solution. Despite having the same number of single and double bonds, slight differences in their structures lead to distinct photophysical properties. The time-correlated single-photon counting measurements also demonstrated that the aspects of the temporal decay curves are dependent on the treatment of the samples during the measurements. The decay curves were relatively well fitted using an alternative method based on the exponentially modified Gaussian function, giving rise to a more complete set of fitting parameters. Finally, theoretical calculations corroborate that small structural changes in molecules can significantly alter their conformational dynamics, electronic spectra, and overall electronic behavior and stability.

Bayesian calibration and uncertainty quantification of a rate-dependent cohesive zone model for polymer interfaces

This study presents a rate-dependent cohesive zone model for the fracture of polymeric interfaces and performs a Bayesian calibration, an uncertainty quantification, and a sensitivity analysis for the model. The proposed cohesive zone model accounts for both reversible elastic and irreversible rate-dependent separation sliding deformation at the interface. The viscous dissipation due to the irreversible opening at the interface is modeled using elastic-viscoplastic kinematics that incorporates the effects of strain rate. Inverse calibration of parameters for such complex models through trial and error is challenging due to the large number of parameters of the model. Moreover, the calibrated parameter values are often non-unique and uncertain when the available experimental data is limited. To tackle this challenge, we employ a Bayesian calibration approach to identify parameters from experimental data, the resulting parameters significantly enhance the accuracy of the model. To quantify the uncertainty associated with the inverse parameter estimation, a modular Bayesian approach is employed to calibrate the unknown model parameters, accounting for the parameter uncertainty of the cohesive zone model. The advantages of the Bayesian calibration over a deterministic parameter fit are demonstrated. Further, to quantify the model uncertainties, such as incorrect assumptions or missing physics, a discrepancy function is introduced, which significantly improves the model’s prediction. Finally, the total uncertainty of the model is quantified in a predictive setting. A sensitivity analysis is performed to assess how changes in the input variables of the model affect the peak load, facilitating the identification of a concise set of highly influential parameters. The present approach can be used for calibration and uncertainty quantification for other complex computational mechanics models. It should also facilitate the designing of interface materials under uncertainty.

Simulating the Effects of Ammonia Crossover and Alternative Ligands in Thermally Regenerative Flow Batteries

There is a significant amount of low-temperature heat (< 100 °C) available globally from various sources such as geothermal energy, industrial processes, and thermal power plants. Electrochemical and membrane-based methods for harnessing this heat are becoming of greater interest due to their ability to utilize the low temperature energy sources and because they can be sized modularly to fit the power, energy, and spatial requirements of the application. The all-aqueous thermally regenerative ammonia battery (Cuaq-TRAB) is a leading technology in the waste heat to electrical power production space due to recent results showing operational power densities of 25 mW cm-2 with an estimated thermal energy efficiency of 7%, outperforming most technologies in this space. Previous research on the Cuaq-TRAB has shown that the battery suffers from the crossover of ammonia through the membrane which adversely impacts battery performance.To investigate these impacts, we developed a simple numerical model that simulates discharge curves using one fitting parameter to account for parasitic crossover instead of modeling species transport through the membrane. The model was able to replicate experimental data from Cuaq-TRABs with different membrane materials and for multiple applied current densities with < 5% error for average power and energy capacity. Results showed that there is a 20% loss in Cuaq-TRAB energy capacity due to ammonia crossover when compared to a discharge with no parasitic crossover losses. The model was then extended to demonstrate the impact of different changes to the electrolyte chemistry on the resultant power and energy capacities for Cuaq-TRABs. Using this model, we estimate that if chloride was the ligand for the positive electrolyte instead of bromide, it would decrease the power and energy capacity of the battery by 30%. However, considering that an economic analysis showed that chloride is 80% cheaper than bromide, it may be beneficial to consider the use of chloride ligands in this technology despite its worse electrochemical performance.

Simplified Physics-Based Battery Model for Stationary Energy-Storage Applications

To perform safe battery operation and avoid unnecessary degradation, battery models are needed. How complex, or close to reality the model should be, depends on the purpose of the model and the computational power available. The possibility for accurate parameterization before model implementation is another crucial aspect for the validity. Physics-based models have the advantage to solve for internal battery states, which includes important information to avoid accelerated degradation and to evaluate state of health. But the parameterization effort can be extensive and the computational cost too high to solve for in real time. Identifying the most essential processes and if possible, simplify the model is therefore motivated.Parameter sensitivity is a powerful tool to investigate how much certain parameters and the related processes effect the output signal, i.e. battery voltage. Under normal operating conditions the cells are rarely completely discharged, and as a result the diffusion processes show low sensitivity during operation [1]. In this work we therefore explore the utilization of a physics-based model solving for the electrolyte dynamics, rather than the solid phase diffusion commonly applied in the so-called single particle model. By neglecting the diffusion processes, the second dimension in the pseudo-two-dimensional (P2D) model can be ignored and the computational complexity simplified. The parameterization strategy and validation of the model is further discussed.We evaluate our model by comparing simulations with experimental data from batteries subjected to stationary energy storage applications. The cells are commercial 18650-type NMC/Graphite cells with 2.6 Ah capacity. Different service cycles have different needs in terms of current and voltage, see examples in Figure 1. Capturing these behaviours with a physics-based model might pose different challenges and will further affect the degradation of the cells [2]. A wide range of models exist in literature to capture degradation mechanisms during battery operation [3]. To keep the computational and parameterization effort as low as possible, our methodology is not to include more physics, but instead update the parameters to the processes already included in the model [4]. We explore this strategy comparing the behaviour from different types of services, as well as batteries where the application changes, to see the model response and analyse the battery state of health, see Figure 2. Electrochemical techniques such as differential voltage analysis and electrochemical impedance spectroscopy is included to support our conclusions.Our results highlight the value of application considerations when designing battery models, rather than only focusing on physical extreme points. We present an alternative physics-based model with the ability to capture crucial degradation phenomena and validate it against operational data. We believe our findings can be of value for smarter battery operating strategies and a greater understanding of application dependant lithium-ion battery degradation.Figure 1. Some of the application duty cycles part of the experimental study. a) Current profile peak shaving b) Current profile frequency regulation c) Voltage response peak shaving d) Voltage response frequency regulation.Figure 2. Degradation trends from the experimental study. Purple cell initially performing PS following application change to FR_high cycling a) Capacity evolution of the cycled cells b) tortuosity parameter evolution obtained from model parameter fitting c) Measured impedance response during cell reference performance tests d) Calculated charge transfer resistance from measured impedance data.

Modeling of Electric Double Layer to Analyze Reaction Kinetics

Electric double layers (EDLs) play a fundamental role in various electrochemical processes such as colloid dispersion, surface charging, and charge-transfer reactions. Increasingly, the role of EDLs on reaction kinetics is being studied (1). Despite the extensive history of EDL modeling, there remain challenges in accurate prediction of its structure. The characteristic length of EDL (nanometer scale) exceeds the grasp of typical ab-initio molecular-dynamic simulations in order to capture a whole picture of the statistical equilibrium. While continuum models offer a means to estimate the thermodynamic equilibrium of EDLs with substantially lower computational cost than molecular dynamics, state–of-the-art continuum models require parameter fitting(2) due to the lack of appropriate expressions for microscopic interactions.Herein, we propose a predictive multiscale continuum model of EDL that eliminates the empirical fitting and relies more on physical relations. This model computes the distribution of the electrostatic potential, electron density, and species’ concentrations by minimizing the total grand potential of the system. The grand potential is calculated from entropic energy, electrostatic energy, electron energy, solute-solute interactions, and electrode-solute interactions. These energy expressions include not only the terms from previous work(2), but also the microscopic interactions that are newly introduced in this work: polarization of hydration shells, electrostatic interaction in parallel plane toward the electrode, and ion-size-dependent entropy. The parameters that identify the electrode and electrolyte materials are obtained from independent experiments such as vacuum work function, bulk modulus, X-ray diffraction, and concentration-dependent dielectric constant. The developed model reproduces the trends in the experimental differential capacitance with multiple electrode and electrolyte materials (Ag (111)-NaF, Ag (111)-NaClO4, and Hg-NaF), which verifies the accuracy and predictiveness of the model in a system without significant specific electrode-electrolyte interactions. The difference in the capacitance between mercury and silver electrode was attributed to the difference in the electron stability in the metal that is parameterized from the material’s bulk modulus. Finally, extension of the model to systems with specific electrode-electrolyte interactions (e.g., Pt (111)-NaClO4) and the analysis of reaction kinetics will be introduced. Overall, the model framework and findings will provide insights into the EDL structures and guidance on how to tailor electrode-electrolyte interface for improved reaction kinetics. Acknowledgements: This work was partially supported by the Hydrogen and Fuel Cell Technologies Office (HFTO), Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy (DOE) through the Million Mile Fuel Cell Truck (M2FCT) consortia, technology managers G. Kleen and D. Papageorgopoulos, under contract number DE-AC02- 05CH11231, as well as by a CRADA with Toyota Central R&D Labs., Inc. and by the Center for Ionomer-based Water Electrolysis (CIWE), a DOE Energy Earthshot Research Center. Reference: S. J. Shin, D. H. Kim, G. Bae, S. Ringe, H. Choi, H. K. Lim, C. H. Choi and H. Kim, Nat Commun, 13, 174 (2022).J. Huang, J Chem Theory Comput, 19, 1003 (2023).

(Invited) Designer 2D Materials and Machine-Learning Assisted Characterization

2D materials offer enormous opportunities to build designer structures with widely tunable properties. The precise atomic engineering and quick characterization approach are critical to advance the application of designer 2D materials. This talk will introduce new optical features of designer 2D materials [1-4] with a focus on atomic substitution in monolayer WS2 [1]. The engineering of 2D materials presents unique opportunities for optoelectronic device and quantum information platform. When designing optoelectronic devices of 2D materials, spectroscopic permittivity of 2D material is a key parameter. While ellipsometry has been used to measure permittivity, it requires simple device structure, non-trivial parameter fitting, and special setup. This talk will also present a new machine-learning assisted approach to measure permittivity of 2D materials embedded in complex device structures without model fitting, which can facilitate a quick, accurate, and in-situ characterization of 2D and other thin film materials [5].