Fundamental Data Research Articles

Microwave Drying of Sewage Sludge: Process Performance and Energy Consumption

The microwave drying of sewage sludge is characterized by its speed and safety. A novel method for identifying free and bound water is proposed in this study. Experiments were performed to investigate the process performance and energy consumption in a microwave drying unit. The results indicate that the microwave drying process can be described in three stages, i.e., the preheating stage, constant-rate stage, and decreasing-rate stage. The preheating and constant-rate stages mainly remove free water, while the decreasing-rate stage mainly removes bound water. The Linear model effectively describes the kinetic processes in the constant-rate stage, and the modified Page I model is suitable for describing the decreasing-rate stage. The energy conversion process in microwave drying is explored, revealing that heat efficiency and energy consumption are consistent with microwave power changes. The heat efficiency in the constant-rate drying stage ranges from 60.33% to 71.01%, lower than that in the preheating stage but higher than that in the decreasing-rate stage. Energy consumption in the constant-rate stage ranges from 3.84 kJ/g to 8.20 kJ/g, significantly lower than in the other two stages. These results provide fundamental data for the industrial application of microwave drying of sludge and contribute to the advancement of microwave drying technology.

Land Cover Changes and Driving Factors in the Source Regions of the Yangtze and Yellow Rivers over the Past 40 Years

As a climate-sensitive region of the Tibetan Plateau, the source regions of the Yangtze and Yellow Rivers (SRYYRs) urgently require an analysis of land cover change (LUCCs) over a long period, high temporal resolution, and high spatial resolution. This study utilizes nearly 40 years of land cover, the Normalized Difference Vegetation Index (NDVI), climate, and geomorphological data, applying methods including a land transfer matrix, slope trend analysis, correlation analysis, and landscape pattern indices to analyze the spatial and temporal changes, composition, layout, and quality of the local land cover and the factors. The findings reveal that (1) the land cover area change rate was 8.96% over the past 40 years, the unutilized land area decreased by 24.49%, and the grassland area increased by 6.37%. The changes were obvious at the junction of the two source regions and the southeast side of the source region of the Yellow River. (2) the landscape pattern was more centralized and diversified. The number of low-cover grassland patches increased by 12.92%. (3) The region is still dominated by medium- and low-cover vegetation, with the mean annual NDVI increasing at a rate of 0.006/10a, and the rate of change after 2000 is three times higher than previously. (4) The degree of land cover change is greater in the middle altitudes, semisunny aspects, steepest slopes, and middle-relief mountains. Additionally, 76.8% of the region’s vegetation growth is dominated by mean annual temperatures. This study provides fundamental data and theory for understanding LUCCs and the driving factors in alpine plateau regions.

Influence of Nd doping on the structural and electrochemical properties of uranium dioxide

The influence of the Nd-doping ratio and oxygen vacancies on the structural properties and electrochemical reactivity of U1-yNdyO2-x (x ≈ y/2, y = 0.02, 0.06, and 0.1) solid solutions was investigated using X-ray diffraction, Raman spectroscopy, and cyclic voltammetry. Defect structures induced by oxygen vacancies were observed with Raman spectroscopy, which showed that Raman modes shifted to higher wavenumbers with increasing doping ratio. This result is indicative of a crystal lattice disorder caused by oxygen vacancies. The wavenumber shifts and relative ratios of the Raman modes were indicators of the composition and oxygen vacancies, respectively, of the U1-yNdyO2-x solid solutions. The oxygen deficiencies of the solid solutions were estimated from the relation between the doping ratio and lattice parameter calculated from X-ray diffraction patterns. Cyclic voltammetry revealed that changes in the Nd content influenced the electrochemical reactivity of hypo-stoichiometric U1-yNdyO2-x, which increased with increasing Nd content. The results of this study provide fundamental data to improve the understanding of spent nuclear fuel.

Impact of variation in input parameters on energy matrices of dual slope active solar desalting unit

AbstractThis article presents the relationship between energy matrices of a dual slope solar desalting unit integrated with a number (N) of photovoltaic thermal flat plate collectors (NPVTFPC‐DSU) and the effect of variation in mass flow rate and N for a sustainable solar distillation. Although extensive research has been done on dual slope active solar desalting unit, however, the area of the impact of variation in and N on energy matrices of NPVTFPC‐DSU remains still untouched. This study is an attempt to bridge the gap that has been highlighted. An exhaustive analysis is performed considering all types of climatic situations of New Delhi, India. MATLAB is used to calculate the overall energy, exergy, and energy matrices for NPVTFPC‐DSU based on fundamental equations and relevant data as inputs. Concludingly, it is evident that there is a decrease in the value of energy payback period and life cycle conversion efficiency as the value of increases at a given value of N for ≤ 0.10 kg s−1. For kg s−1, the values remain constant. For NPVTFPC‐DSU, 6 and 10 are the best and most favorable values of N as far as energy‐based energy matrices and exergy‐based energy matrices are concerned.

Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS

Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.

The Use of Cannabinoids in Pediatric Palliative Care-A Retrospective Single-Center Analysis.

This data analysis aimed to systematically analyze a pediatric patient population with a life-limiting disease who were administered cannabinoids. It was a retrospective single-center analysis of patients under supervision of the specialized outpatient pediatric palliative care (SOPPC) team at the Department of Pediatrics and Adolescent Medicine of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). Thirty-one patients with a primary diagnosis of neuropediatric, oncologic, metabolic, and cardiologic categories were included. The indications we identified were spasticity, pain, restlessness, anxiety, loss of appetite, epilepsy, and paresis. Certain aspects of quality of life were improved for 20 of 31 patients (64.5%). For nine patients (29%), no improvement was detected. No conclusions could be drawn for two patients (6.5%). Adverse events were reported for six of the thirty-one patients (19.4%). These were graded as mild, including symptoms such as restlessness, nausea, and behavioral issues. We detected no clinically relevant interactions with other medications. We collected fundamental data on the use of cannabinoids by pediatric palliative patients. Cannabinoids are now frequently administered in pediatric palliative care. They seem to be safe to use and should be considered an add-on therapy for other drug regimens.

Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework

Motor imagery brain-computer interfaces (MI-BCIs) have gained a lot of attention in recent years thanks to their potential to enhance rehabilitation and control of prosthetic devices for individuals with motor disabilities. However, accurate classification of motor imagery signals remains a challenging task due to the high inter-subject variability and non-stationarity in the electroencephalogram (EEG) data. In the context of MI-BCIs, with limited data availability, the acquisition of EEG data can be difficult. In this study, several data augmentation techniques have been compared with the proposed data augmentation technique adaptive cross-subject segment replacement (ACSSR). This technique, in conjunction with the proposed deep learning framework, allows for a combination of similar subject pairs to take advantage of one another and boost the classification performance of MI-BCIs. The proposed framework features a multi-domain feature extractor based on common spatial patterns with a sliding window and a parallel two-branch convolutional neural network. The performance of the proposed methodology has been evaluated on the multi-class BCI Competition IV Dataset 2a through repeated 10-fold cross-validation. Experimental results indicated that the implementation of the ACSSR method (80.47%) in the proposed framework has led to a considerable improvement in the classification performance compared to the classification without data augmentation (77.63%), and other fundamental data augmentation techniques used in the literature. The study contributes to the advancements for the development of effective MI-BCIs by showcasing the ability of the ACSSR method to address the challenges in motor imagery signal classification tasks.

A goal-aligned coordinate system for invasion games

Spatial locations of players and game devices are a fundamental data type in team-sports analytics. They are typically specified in Cartesian coordinates, but with varying conventions for the origin, orientation, and scaling. In invasion games such as football, basketball, or hockey, however, many markings are of fixed dimension even when the field of play is not, so that the game-specific meaning of locations does not scale uniformly. We propose an alternative coordinate system that accommodates variable field sizes by using the goals instead of a corner or the center of the field of play as frames of reference.

Bile acid profiles in adult patients with biliary atresia who achieve native liver survival after portoenterostomy

Bile acids have received increasing attention as a marker of the long-term prognosis and a potential therapeutic target in patients with biliary atresia, which is a progressive disease of the hepatobiliary system. A detailed analysis of serum and urinary bile acid compositions was conducted to assess the characteristics of bile acid profiles and the correlation between bile acid profiles and liver fibrosis markers in adult patients with biliary atresia who achieved bilirubin normalization. Serum total bile acids and glucuronide-conjugated (glyco- and tauro-) cholic acids (GCA and TCA) and chenodeoxycholic acids (GCDCA and TCDCA) were significantly higher in patients with biliary atresia than in healthy controls, whereas unconjugated CA and CDCA showed no significant difference. There were no significant differences in CA to CDCA ratios and glycine-to-taurine-conjugated ratios. Urinary glycocholic acid 3-sulfate (GCA-3S) was significantly higher in patients with biliary atresia. Serum GCDCA showed a strong positive correlation with Mac-2 binding protein glycosylation isomer (M2BPGi). These results demonstrate that bile acid congestion persists into adulthood in patients with biliary atresia, even after cholestasis has completely improved after Kasai portoenterostomy. These fundamental data on bile acid profiles also suggest the potential value of investigating bile acid profiles in patients with biliary atresia.

Multivariate simulation‐based forecasting for intraday power markets: Modeling cross‐product price effects

AbstractIntraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded products and the corresponding cross‐product effects cannot be ignored. We aim to fill this gap in the literature by using copulas to model the high‐dimensional intraday price return vector. We model the marginal distribution as a zero‐inflated Johnson's distribution with location, scale, and shape parameters that depend on market and fundamental data. The dependence structure is modeled using copulas, accounting for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days and allowing the dependence parameter to be time‐varying. We validate our approach in a simulation study for the German intraday electricity market and find that modeling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling on the trading activity and price distribution and interpret our results in light of the market efficiency hypothesis. The approach is directly applicable to other European electricity markets.

Exploring multistep bischofite waste pyrolysis: insights from advanced kinetic analysis and thermogravimetric techniques.

Pyrolysis technology is crucial for realizing waste bischofite resource utilization. However, previous studies overlooked the complexity of multistep pyrolysis, resulting in a lack of thorough knowledge of the pyrolysis behavior and kinetics. The pyrolysis products were characterized using XRD and FTIR to indicate the bischofite pyrolysis behavior. Additionally, the multistep kinetics was studied using the segmented single-step reaction (SSSR) and Fraser-Suzuki combined kinetic (FSCK) methods. The results show that the bischofite pyrolysis is divided into dehydration and hydrolysis. The former refers to removing crystalline water from MgCl2·nH2O (n = 4,6). At the same time, the latter is related to the removal of HCl, characterized by the strengthening of the Mg-O bond in the FTIR analysis and the emergence of MgOHCl·1.5H2O in the XRD examination. The two main stages are divided into three dehydration reactions (D-1, D-2, D-3) and three hydrolysis reactions (H-1, H-2, H-3) by DTG-DDTG or Fraser-Suzuki deconvolution. Compared with the SSSR method, the FSCK method has improved model repeatability for multistep kinetic parameters. Following Fraser-Suzuki deconvolution, the FSCK method creates almost the same activation energy results when using the Friedman (FR), Kissinger-Akahira-Sunose (KAS), and Vyazovkin (VZK). This work provides fundamental data to promote the maximizing waste bischofite resource utilization.

Real-Time Imaging of Single Retinal Cell Apoptosis in a Non-Human Primate Ocular Hypertension Model.

To evaluate the feasibility of using DARC (detection of apoptosing retinal cells) technology as a biomarker for preclinical assessment of glaucomatous damage in a non-human primate (NHP) model of ocular hypertension (OHT). Elevated intraocular pressure (IOP) was induced by applying a laser to the trabecular meshwork in each eye of NHPs. Changes in DARC counts in the retina, identified as fluorescent-tagged annexin V (ANX776)-positive cells, were evaluated together with optic nerve damage, assessed using spectral domain-optical coherence tomography. The pharmacokinetic properties of ANX776 in both healthy and OHT model monkeys were also examined. Sustained elevation of IOP and subsequent thinning of the retinal nerve fiber layer thickness (RNFLT) around the optic nerve head were confirmed in the OHT model. Increases in DARC counts were also detected after IOP elevation. We identified a statistically significant relationship between cumulative DARC counts and reductions in RNFLT both globally and in each peripapillary sector. Intravenous administration of ANX776 increased blood annexin V in a dose-dependent manner, which was subsequently eliminated. This study revealed that DARC technology can effectively assess glaucomatous damage in an NHP OHT model. We obtained the fundamental data that could serve as a reference for developing preclinical models to evaluate the pharmacodynamics of neuroprotective agents using DARC technology in NHP OHT models. Our basic data in a monkey OHT model could be useful for future preclinical studies using DARC technology to estimate the pharmacodynamic response of neuroprotective agents.

Multiphase Equilibrium Relationships between Copper Matte and CaO-Al2O3-Bearing Iron Silicate Slags in Combined Smelting of WEEE and Copper Concentrates

Waste electrical and electronic equipment (WEEE) contains various valuable metals, making it a potential secondary resource for sustainable metal usage. Pyrometallurgical smelting is an efficient technique to recycle WEEE by extracting precious metals into copper matte and removing impurities into slags. The impact of WEEE impurities such as CaO and Al2O3 on the phase compositions of the smelting products attracts great attention for industrial metal recovery. This study clarified the impact of CaO and Al2O3 on the equilibrium phase compositions of copper matte and SiO2-saturated FeOx-SiO2-Al2O3-CaO slags. The high-temperature smelting experiments were taken at a controlled p(SO2) of 0.1 atm and 1300 °C, followed by quenching and electron probe microanalysis. The results showed that the copper and sulfur in the smelting system were highly deported into copper matte, and their distribution in matte was enhanced by increasing CaO and Al2O3 concentrations introduced by WEEE. The chemical copper dissolution in slags increased with increasing matte grade but decreased by adding CaO and Al2O3. The iron was preferentially concentrated in slags, and higher matte grades improved the iron distribution in slags. The current experimental results enrich fundamental thermodynamic data and help optimize WEEE smelting operations for efficient recovery of valuable metals.

Operator-derived particles and falling bacteria in biosafety cabinets

IntroductionTo ensure the sterility of cell products that cannot undergo conventional sterilization processes, it is imperative to establish and maintain a clean room environment, regulated through environmental monitoring, including particle counts. Nevertheless, the impact of particles generated by operators as potential contaminants remains uncertain. Thus, in this study, we conducted an accelerated test to assess the correlation between particles generated by operators and airborne bacteria, utilizing biosafety cabinets within a typical laboratory setting. These biosafety cabinets create a controlled environment with air conditioning and high-efficiency particulate air (HEPA) filters, offering fundamental data relevant to cell production. Materials and methodsWe conducted a simulation followed by real-time experiments involving human operations to explore the quantity of particles, particle sizes, and the percentage of bacteria within these particles. This investigation focused on conditions with heightened particle generation from operators within a biosafety cabinet. The experiment was conducted on operators wearing textile and non-woven dustless clothing within biosafety cabinets. It entailed tapping the upper arms for a duration of 2 min. ResultsObservations under biosafety cabinet-off conditions revealed the presence of various particles and falling bacteria in textile clothing. In contrast, no particles or falling bacteria were detected in operators wearing dustless clothing within biosafety cabinets. Notably, a correlation between 5 μm particles and colony-forming units in textile clothing was identified through this analysis. The ratio of falling bacteria to the total number of particles within the biosafety cabinet was 0.8 ± 0.5 % for textile clothing, while it was significantly lower at 0.04 ± 0.2 % for dustless clothing. ConclusionThis study demonstrated that the number of particles and falling bacteria varied depending on the type of clothing and that quantitative data could be used to identify risks and provide basic data for operator education and evidence-based control methods in aseptic manufacturing areas. Although, this study aims to serve as an accelerated test operating under worst-case conditions, the results need to make sure the study range in general research.

Transcriptome identification of differential mammary genes of Kazakh horses during early pregnancy

Kazakh mares have attracted widespread attention with their outstanding lactation traits. Lactation is a complex dynamic process regulated by multiple factors. The extensive application of transcriptome sequencing technology enables researchers to further explore this biological issue. This study selected three pregnant and three non-pregnant Kazakh mares as the research subject. Their mammary glands were taken for transcriptome sequencing. The results show that there are 9 lncRNAs and 122 mRNAs differentially expressed between the two groups. GO enrichment analysis shows that there are 175 molecular functions, 59 cellular components, and 555 biological processes, including cellular hormone metabolic process, hormone catabolic process, and I-kappaB kinase/NF-kappaB signaling. KEGG enrichment analysis exhibits that these differential genes are mainly enriched in the NF-kappa B signaling pathway, steroid hormone biosynthesis, breast cancer, ECM-receptor interaction, and MAPK signaling pathway. WNT4, DPP4, and NFKBIA are key nodes regulating breast activation. Conclusions: Through the comparative analysis of the transcriptome data of mammary tissues of pregnant and non-pregnant mares, relevant differentially expressed genes are screened and analyzed. This study provides valuable fundamental data for investigating candidate genes related to the lactation regulation and mammogenesis of Kazakh horses.

핵심역량별 비교과 로드맵 도출을 위한 비교과 프로그램 현황 및 성과 분석

Objectives This study was conducted with the aim of providing fundamental data to support the development of a extra-curricular roadmap for the six core competencies at H University.
 Methods As part of the research project, an analysis of extra-curricular program operations over the past two years was conducted, including an analysis of extra-curricular program operation status by type of activity and an analysis of extra-curricular achievements by core competency. Firstly, a comprehensive analysis was performed, including a student perception survey, to assess the operational status by domain based on the types of activities in extra-curricular programs. Secondly, for the analysis of extra-curricular achievements by core competency, paired samples t-tests (pre-post) and independent samples t-tests (participation group, Extra-participation group) were conducted for each core competency program using the statistical software SPSS version 26.
 Results First, in extra-curricular activities, the operation rate of special lectures/workshops was significantly high, and it was also found that these were the types of activities in which students most wanted to participate. On the other hand, while the extra-curricular operation rate in the “understanding” domain was high, students' most de-sired activity phase was found to be in the “competency development” domain programs. Second, regarding the Extra-curricular operational outcomes by core competency, it was statistically significant that students demon-strated improved competencies in knowledge and information cultivation programs, humanities competency cul-tivation programs, communication competency cultivation programs, and leadership competency cultivation programs.
 Conclusions Drawing on the findings of this study, foundational evidence has been generated for the establish-ment of a core competency-specific extra-curricular roadmap for enrolled students. Concurrently, deliberations were conducted concerning prospective development avenues in response to H University's Extra-curricular ed-ucation curriculum operation status and performance outcomes.

Smart Log Analyzer for Anomaly Detection in Distributed Systems

Abstract—This research work outlines a novel approach to fortify the stability and security of distributed systems through the implementation of an AI-enabled smart log analyzer. The escalating proliferation of distributed systems has led to an unprecedented surge in the volume of generated log data, which holds crucial insights into system performance, security, and dependability. However, the substantial challenges associated with managing this data—such as its overwhelming volume, diverse nature, and real-time processing requirements—have posed significant hurdles. The proposed AI-enabled smart log analyzer, detailed in this report, harnesses the power of advanced machine learning and natural language processing techniques to address these challenges effectively. The methodology is struc- tured into three fundamental phases, namely, Data Preprocessing, Anomaly Detection, and Clustering. The Data Preprocessing phase encompasses the collection, parsing, filtering, and feature extraction of log data. Anomaly Detection integrates machine learning models to discern various anomalies, encompassing irregular access patterns, log flooding, error messages, suspicious content, and outliers in resource requests. The Clustering phase categorizes log entries into meaningful groups based on attributes such as log level, component, event, error code, and resource usage, facilitating a comprehensive understanding of system behavior. This holistic approach holds the promise of significantly enhancing system stability and security. Index Terms—Anomaly Detection, Clustering, Data Prepro- cessing, Distributed Systems, Log analyzer, System security, System stability

Estimation of AMSU-A and MHS Antenna Emission from MetOp-A End-of-Life Deep Space View Test

A unique End-of-Life (EOL) Deep Space View Test (DSVT) was performed on 27 November 2021 for the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) onboard the first EUMETSAT MetOp-A satellite in the deorbiting process. The purpose of this test is to recalibrate the antenna sidelobe, to derive antenna emission, and to quantify the in-orbit asymmetric scan biases of AMSU-A and MHS to, ultimately, improve Near Real-Time (NRT) products for MetOp-B and -C and the entire Fundamental Climate Data Records (FCDR). In this study, MetOp-A AMSU-A and MHS EOL DSVT data on 27 November 2021 have been analyzed. The deep space scene antenna temperatures were first applied for the antenna pattern correction; then, the antenna reflector channel emissivity values were derived from the corrected temperatures. For the MHS, the observed scan-angle-dependent brightness temperatures (BTs) for all channels were well behaved after the antenna pattern correction, except for channel 1. The derived antenna reflector emissivity values from this test are 0.0016, 0.0036, 0.0036, and 0.0019 for channels 1, 3, 4, and 5, respectively. For AMSU-A, the deep space view counts were not homogeneous during the test period, exhibiting large variations in the along-track and cross-track directions, mainly due to the instrument temperature’s rapid change during the test period. The large relative noise in the deep space view observations negatively impacted the data quality and limits the value of this test. The large relative noise may contribute to the different emissivity values derived from the same frequency for channels 9 to 14. We also found unexpected scan-angle-dependent BT after antenna pattern correction for quasi-vertical (QV) channels 1 and 2 when compared to the emission model. Further investigation using a simulation confirmed that channels 1 and 2 are QV channels, as designed.

Achieving Intensity Distributions of 6 February 2023 Kahramanmaraş (Türkiye) Earthquakes from Peak Ground Acceleration Records

On 6 February 2023, two large earthquakes struck southern Türkiye on the same day, resulting in a considerable loss of life and property damage over a large region that included 11 cities. After these disasters, there was a requirement to define the soil-related intensity distribution, aside from manufacturing defects caused by buildings. The modified Mercalli intensity (MMI) scale results in the same intensity value (XI) when decimal values are not mathematically considered, even though the fundamental data in the AFAD and USGS sources differ. In this study, an equation based on the MMI–PGA relationship was obtained and tested with ten previously developed equations to calculate the earthquake intensity. Seven of these selected equations, depending on the earthquake magnitude, were calculated comparatively. The equation most compatible with the earthquakes that occurred on 6 February 2023 was obtained in this study. In addition, it was decided that three similar equations could also be used. Intensity distribution maps were created according to the calculated MMI values. In this way, it has been observed that different earthquake intensity values are more sensitive, reliable, objective, and sustainable.

Molecular dynamics simulation of microstructure and thermophysical properties of LiCl–CaCl2 eutectic molten salt

Abstract Chloride molten salt systems are widely used as electrolytes for molten salt electrolysis because of their relatively low eutectic temperatures and good thermal stability, but there is a serious lack of data on the thermophysical properties of chloride molten salts at elevated temperatures, whereas the nature of the electrolyte is very important for the electrolysis process. In this paper, the variation of the microstructure and thermophysical properties of the binary mixed LiCl–CaCl2 molten salt system with temperature and composition is calculated using molecular dynamics (MD) simulations based on the BMH potential. The microscopic conformations observed in LiCl–CaCl2 molten salts are mainly irregular, distorted tetrahedra and octahedra, which dynamically coexist, as analyzed by the radial distribution function, coordination number and angular distribution function. In addition, the effects of temperature and composition on the density, ionic self-diffusion coefficient, shear viscosity, and ionic conductivity of the molten salts were investigated, and the relationships of the thermophysical properties of LiCl–CaCl2 molten salts with temperature and composition were obtained, which provide fundamental thermophysical data for the molten salt electrolytes.