LiNi0.6Co0.2Mn0.2O2 Cathode-Solid Electrolyte Interfacial Behavior Characterization Using Novel Method Adopting Microcavity Electrode.

Due to the limitations of organic liquid electrolytes, current development is towards high performance all-solid-state lithium batteries (ASSLBs). For high performance ASSLBs, the most crucial is the high ion-conducting solid electrolyte (SE), with a focus on interface analysis between SE and active materials. In the current study, we successfully synthesized the high ion-conductive argyrodite-type (Li6PS5Cl) solid electrolyte, which has 4.8 mS cm-1 conductivity at room temperature. Additionally, the present study suggests the quantitative analysis of interfaces in ASSLBs. The measured initial discharge capacity of a single particle confined in a microcavity electrode was 1.05 nAh for LiNi0.6Co0.2Mn0.2O2 (NCM622)-Li6PS5Cl solid electrolyte materials. The initial cycle result shows the irreversible nature of active material due to the formation of the solid electrolyte interphase (SEI) layer on the surface of the active particle; further second and third cycles demonstrate high reversibility and good stability. Furthermore, the electrochemical kinetic parameters were calculated through the Tafel plot analysis. From the Tafel plot, it is seen that asymmetry increases gradually at high discharge currents and depths, which rise asymmetricity due to the increasing of the conduction barrier. However, the electrochemical parameters confirm the increasing conduction barrier with increased charge transfer resistance.

Application of Machine Learning to Identify Clinically Meaningful Risk Group for Osteoporosis in Individuals Under the Recommended Age for Dual-Energy X-Ray Absorptiometry.

Dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis; it is generally recommended in men ≥ 70 and women ≥ 65 years old. Therefore, assessment of clinical risk factors for osteoporosis is very important in individuals under the recommended age for DXA. Here, we examine the diagnostic performance of machine learning-based prediction models for osteoporosis in individuals under the recommended age for DXA examination. Data of 2210 men aged 50-69 and 1099 women aged 50-64 obtained from the Korea National Health and Nutrition Examination Survey IV-V were analyzed. Extreme gradient boosting (XGBoost) was used to find relevant clinical features and applied to three machine learning models: XGBoost, logistic regression, and a multilayer perceptron. For the prediction of osteoporosis, the XGBoost model using the top 20 features extracted from XGBoost showed the most reliable performance with area under the receiver operating characteristic curve (AUROC) of 0.73 and 0.79 in men and women, respectively. We compared the diagnostic accuracy of the Shapley additive explanation values based on a risk-score model obtained from XGBoost and conventional osteoporosis risk assessment tools for prediction of osteoporosis using optimal cut-off values for each model. We observed that a cut-off risk score of ≥ 28 in men and ≥ 47 in women was optimal to classify a positive screening for osteoporosis (an AUROC of 0.86 in men and 0.91 in women). The XGBoost-based osteoporosis-prediction model outperformed conventional risk assessment tools. Therefore, machine learning-based prediction models are a more suitable option than conventional risk assessment methods for screening osteoporosis in individuals under the recommended age for DXA examination.

Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting.

BACKGROUND: Despite excellent prediction performance, noninterpretability has undermined the value of applying deep-learning algorithms in clinical practice. To overcome this limitation, attention mechanism has been introduced to clinical research as an explanatory modeling method. However, potential limitations of using this attractive method have not been clarified to clinical researchers. Furthermore, there has been a lack of introductory information explaining attention mechanisms to clinical researchers. OBJECTIVE: The aim of this study was to introduce the basic concepts and design approaches of attention mechanisms. In addition, we aimed to empirically assess the potential limitations of current attention mechanisms in terms of prediction and interpretability performance. METHODS: First, the basic concepts and several key considerations regarding attention mechanisms were identified. Second, four approaches to attention mechanisms were suggested according to a two-dimensional framework based on the degrees of freedom and uncertainty awareness. Third, the prediction performance, probability reliability, concentration of variable importance, consistency of attention results, and generalizability of attention results to conventional statistics were assessed in the diabetic classification modeling setting. Fourth, the potential limitations of attention mechanisms were considered. RESULTS: Prediction performance was very high for all models. Probability reliability was high in models with uncertainty awareness. Variable importance was concentrated in several variables when uncertainty awareness was not considered. The consistency of attention results was high when uncertainty awareness was considered. The generalizability of attention results to conventional statistics was poor regardless of the modeling approach. CONCLUSIONS: The attention mechanism is an attractive technique with potential to be very promising in the future. However, it may not yet be desirable to rely on this method to assess variable importance in clinical settings. Therefore, along with theoretical studies enhancing attention mechanisms, more empirical studies investigating potential limitations should be encouraged.

Risk Scoring System for Prognosis Estimation of Multivessel Disease Among Patients with ST-Segment Elevation Myocardial Infarction.

Multivessel disease (MVD) is an independent risk factor for poor prognosis in acute myocardial infarction patients. Although several global risk scoring systems (RSS) are in use in clinical practice, there is no dedicated RSS for MVD in ST-segment elevation myocardial infarction (STEMI). The primary objective of this study is to develop a novel RSS to estimate the prognosis of patients with MVD in STEMI.We used the Korean Acute Myocardial Infarction Registry (KAMIR) to identify 2,030 STEMI patients with MVD who underwent appropriate percutaneous coronary intervention (PCI). Their data were analyzed to develop a new RSS. The prognostic power of this RSS was validated with 2,556 STEMI patients with MVD in the Korean Working Group on Myocardial Infarction Registry (KORMI).Six prognostic factors related to all-cause death in STEMI patients with MVD were age, serum creatinine, Killip Class, lower body weight, decrease in left ventricular ejection fraction, and history of cerebrovascular disease. The RSS for all-cause death was constructed using these risk factors and their statistical weight. The RSS had appropriate performance (c-index: 0.72) in the KORMI validation cohort.We developed a novel RSS that estimates all-cause death in the year following discharge for patients with MVD in STEMI appropriately treated by PCI. This novel RSS was transformed into a simple linear risk score to yield a simplified estimate prognosis of MVD among STEMI patients.

Toward Achieving a High Ionic Conducting Halide Solid Electrolyte through Low-Cost Metal (Zr and Fe) and F Substitution and Their Admirable Performance in All-Solid-State Batteries.

Recently, the halide solid electrolyte (SE) system has been widely used in lithium solid-state batteries due to their specific properties, such as the high electrochemical stability window that prevents any side reaction with the electrode/electrolyte interface. Conspicuously, the halide SE possesses very low ionic conductivity values in the range (0.2-0.5) mS cm-1. In this work, we enhance the ionic conductivity of Li3YCl6 SE by the substitution of low-cost Fe and Zr elements on the Y-site and F on the Cl site, in which the electrolyte is prepared through high-energy ball milling without a heat treatment process. The structural analysis reveals that the prepared halide SEs showed the pure phase of the Li3YCl6 tetragonal crystal structure and were free from impurity phases. In the prepared composition, the Li2.4Y0.4Zr0.6Cl6 and Li2.4Y0.4Zr0.6Cl5.85F0.15 electrolyte exhibited a higher ionic conductivity of 2.05 and 1.45 mS cm-1, respectively, than Li3YCl6 (0.26 mS cm-1). Interestingly, the Li2.4Y0.4Zr0.6Cl5.85F0.15 electrolyte possesses a better electrochemical stability window of 1.29-3.9 V than Li2.4Y0.4Zr0.6Cl6 (2.1-3.79 V). Moreover, the electrochemical results revealed that the assembled solid-state battery using Li2.4Y0.4Zr0.6Cl6 and Li2.4Y0.4Zr0.6Cl5.85F0.15 electrolyte demonstrated the higher initial Coulombic efficiency of 84.7 and 87%, respectively, than Li3YCl6 of 82.6%. We consider Li2.4Y0.4Zr0.6Cl5.85F0.15 to be an important electrolyte candidate in all-solid-state batteries.

Relationship between patient outcomes and patterns of fragmented cancer care in older adults with gastric cancer: A nationwide cohort study in South Korea.

INTRODUCTION: Fragmented cancer care, defined as receipt of care from multiple hospitals, has been shown to be associated with poor patient outcomes and high expense. However, evidence regarding the effects of hospital choice by patients with cancer on overall survival are lacking. Thus, we investigated the relationship between patterns of fragmented care and five-year mortality in patients with gastric cancer. MATERIALS AND METHODS: Using the Korean National Health Insurance senior cohort of adults aged ≥60 years, we identified patients with gastric cancer who underwent gastrectomy during 2007-2014. We examined the distribution of the study population by five-year mortality, and used Kaplan-Meier survival curves/log-rank test and Cox proportional hazard model to compare five-year mortality with fragmented cancer care. RESULTS: Among the participants, 19.5% died within five years. There were more deaths among patients who received fragmented care, especially those who transferred to smaller hospitals (46.6%) than to larger ones (40.0%). The likelihood of five-year mortality was higher in patients who received fragmented cancer care upon moving from large to small hospitals than those who did not transfer hospitals (hazard ratio, 1.28; 95% confidence interval, 1.10-1.48, P = .001). Moreover, mortality was higher among patients treated in large hospitals or in the capital area for initial treatment, and this association was greater for patients from rural areas. DISCUSSION: Fragmentation of cancer care was associated with reduced survival, and the risk of mortality was higher among patients who moved from large to small hospitals.

Data Resource Profile: The Cancer Public Library Database in South Korea.

This paper provides a comprehensive overview of the Cancer Public Library Database (CPLD), established under the Korean Clinical Data Utilization for Research Excellence project (K-CURE). The CPLD links data from four major population-based public sources: the Korea National Cancer Incidence Database in the Korea Central Cancer Registry, cause-of-death data in Statistics Korea, the National Health Information Database in the National Health Insurance Service, and the National Health Insurance Research Database in the Health Insurance Review & Assessment Service. These databases are linked using an encrypted resident registration number. The CPLD, established in 2022 and updated annually, comprises 1,983,499 men and women newly diagnosed with cancer between 2012 and 2019. It contains data on cancer registration and death, demographics, medical claims, general health checkups, and national cancer screening. The most common cancers among men in the CPLD were stomach (16.1%), lung (14.0%), colorectal (13.3%), prostate (9.6%), and liver (9.3%) cancers. The most common cancers among women were thyroid (20.4%), breast (16.6%), colorectal (9.0%), stomach (7.8%), and lung (6.2%) cancers. Among them, 571,285 died between 2012 and 2020 owing to cancer (89.2%) or other causes (10.8%). Upon approval, the CPLD is accessible to researchers through the K-CURE portal. The CPLD is a unique resource for diverse cancer research to investigate medical use before a cancer diagnosis, during initial diagnosis and treatment, and long-term follow-up. This offers expanded insight into healthcare delivery across the cancer continuum, from screening to end-of-life care.

The Cancer Clinical Library Database (CCLD) from the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) Project.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea's cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction.

The knowledge discovery has been widely applied to mine significant knowledge from medical data. Nevertheless, previous studies have produced large numbers of imprecise patterns. To reduce the number of imprecise patterns, we need an approach that can discover interesting patterns that connote causality between antecedent and consequence in a pattern. In this paper, we propose association rule mining method that can discover interesting patterns that include medical knowledge in Korean acute myocardial infarction registry that consists of 1,247 young adults collected by 51 participating hospitals since 2005. Proposed method can remove imprecise patterns and discover target patterns that include associations between blood factors and disease history. The association that blood factors affect to disease history is defined as target pattern. In our experiments, the interestingness of a target pattern is evaluated in terms of statistical measures such as lift, leverage, and conviction. We discover medical knowledge that glucose, smoking, triglyceride total cholesterol, and creatinine are associated with diabetes and hypertension in Korean young adults with acute myocardial infarction.

Preparation of Metal-Oxide-Doped Li7P2S8Br0.25I0.75 Solid Electrolytes for All-Solid-State Lithium Batteries.

The all-solid-state lithium battery (ASSB) has received great attention due to its greater safety than the conventional lithium-ion battery (LIB). Sulfide-based inorganic solid electrolytes are an important component to fabricate the ASSB. But to attain a better performance, the ionic conductivity and electrochemical stability of the sulfide-based solid electrolytes need to be improved. In this work, we prepared the metal-oxide-doped/mixed Li7P2S8I0.75Br0.25 lithium superionic conductors by a dry ball-milling process. The high ionic conductivity was achieved by a low-temperature (200 °C) heat-treatment process. The metal-oxide-doped Li7P2S8I0.75Br0.25 solid electrolyte exhibited a higher ionic conductivity value of 7.3 mS cm-1 at room temperature than the bare Li7P2S8I0.75Br0.25 solid electrolyte. The structural characteristics of the prepared solid electrolytes were studied by solid NMR and laser Raman analysis. The electrochemical stability of the prepared solid electrolyte was studied by cyclic voltammetry and DC charge-discharge analysis. The addition of metal oxide increased the electrochemical stability and dry-air stability of the Li7P2S8I0.75Br0.25 solid electrolyte. The Ta2O5-doped Li7P2S8I0.75Br0.25 solid electrolyte was stable even after 300 charge-discharge DC cycles and also 100 h of dry-air exposure. Further, the Ta2O5-doped Li7P2S8I0.75Br0.25 solid electrolyte-based ASSB exhibited a high discharge capacity value of 184 mA h g-1 at 0.1 C rate with 66% initial cycle Coulombic efficiency.

Synthetic Tabular Data Based on Generative Adversarial Networks in Health Care: Generation and Validation Using the Divide-and-Conquer Strategy.

BACKGROUND: Synthetic data generation (SDG) based on generative adversarial networks (GANs) is used in health care, but research on preserving data with logical relationships with synthetic tabular data (STD) remains challenging. Filtering methods for SDG can lead to the loss of important information. OBJECTIVE: This study proposed a divide-and-conquer (DC) method to generate STD based on the GAN algorithm, while preserving data with logical relationships. METHODS: The proposed method was evaluated on data from the Korea Association for Lung Cancer Registry (KALC-R) and 2 benchmark data sets (breast cancer and diabetes). The DC-based SDG strategy comprises 3 steps: (1) We used 2 different partitioning methods (the class-specific criterion distinguished between survival and death groups, while the Cramer V criterion identified the highest correlation between columns in the original data); (2) the entire data set was divided into a number of subsets, which were then used as input for the conditional tabular generative adversarial network and the copula generative adversarial network to generate synthetic data; and (3) the generated synthetic data were consolidated into a single entity. For validation, we compared DC-based SDG and conditional sampling (CS)-based SDG through the performances of machine learning models. In addition, we generated imbalanced and balanced synthetic data for each of the 3 data sets and compared their performance using 4 classifiers: decision tree (DT), random forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LGBM) models. RESULTS: The synthetic data of the 3 diseases (non-small cell lung cancer [NSCLC], breast cancer, and diabetes) generated by our proposed model outperformed the 4 classifiers (DT, RF, XGBoost, and LGBM). The CS- versus DC-based model performances were compared using the mean area under the curve (SD) values: 74.87 (SD 0.77) versus 63.87 (SD 2.02) for NSCLC, 73.31 (SD 1.11) versus 67.96 (SD 2.15) for breast cancer, and 61.57 (SD 0.09) versus 60.08 (SD 0.17) for diabetes (DT); 85.61 (SD 0.29) versus 79.01 (SD 1.20) for NSCLC, 78.05 (SD 1.59) versus 73.48 (SD 4.73) for breast cancer, and 59.98 (SD 0.24) versus 58.55 (SD 0.17) for diabetes (RF); 85.20 (SD 0.82) versus 76.42 (SD 0.93) for NSCLC, 77.86 (SD 2.27) versus 68.32 (SD 2.37) for breast cancer, and 60.18 (SD 0.20) versus 58.98 (SD 0.29) for diabetes (XGBoost); and 85.14 (SD 0.77) versus 77.62 (SD 1.85) for NSCLC, 78.16 (SD 1.52) versus 70.02 (SD 2.17) for breast cancer, and 61.75 (SD 0.13) versus 61.12 (SD 0.23) for diabetes (LGBM). In addition, we found that balanced synthetic data performed better. CONCLUSIONS: This study is the first attempt to generate and validate STD based on a DC approach and shows improved performance using STD. The necessity for balanced SDG was also demonstrated.

Preparation of MoO3/MoS2/TiO2 composites for catalytic degradation of methylene blue.

Metastable hexagonal MoO3 microrods were grown from bulk MoS2 and used as support materials for MoS2 and TiO2 nanoparticles. The hybrid composites that consisted of MoO3, MoS2, and TiO2 were prepared at a low temperature using the one-step synthesis method. The crystallinity and morphology of the MoO3/MoS2/TiO2 composites that were prepared using HNO3 and titanium tetraisopropoxide were compared with those of the MoO3/MoS2 composites that were prepared without titanium tetraisopropoxide. Titanium isopropoxide facilitated the formation of the MoO3 microrods from the oxidation of the bulk MoS2. The desired MoO3/MoS2/TiO2 composites were obtained using 0.5 g of bulk MoS2, 3-4 ml of HNO3, and 0.367 ml of titanium tetraisopropoxide. The MoO3/MoS2/TiO2 composites that were treated with ultrasonic waves showed rapid degradation of the methylene blue solution (2 x 10(-4) M) in the dark and good photocatalytic ability under ultraviolet light irradiation. The decomposition of methylene blue depended on the composition of the composite.

Size and morphology controlled lithium manganese oxide on silica sphere with core-shell structure.

Core-shell structures were prepared from synthesized SiO2-LiMn2O4 with manganese oxide as shell on the silica core by a precipitation method, which allowed control of core structure in terms of thickness and particle size. X-ray diffraction (XRD), field emission-scanning electron microscopy (FE-SEM), field emission-transmission electron microscopy (FE-TEM), and energy dispersive spectroscopy (EDS) were used to characterize the SiO2-LiMn2O4. According to FE-SEM images, particle growth was controlled by controlling the amount of manganese precursor and the temperature. The synthesized core-shell structure was composed of silica, lithium silicate, Mn2O3, and the spinel phase of lithium manganese oxide. Electrochemical measurements show that the synthesized core-shell structure has poorer electrochemical performance than that of LiMn2O4.

Improving the electrochemical performance of cathode composites using different sized solid electrolytes for all solid-state lithium batteries.

We studied the efficiency of different particle-sized sulfide solid electrolyte-based cathode composites. First, we prepared the Li7P2S8I solid electrolytes with different particle sizes through a high energy ball milling process and solution method. The structural details of the prepared solid electrolytes were studied by powder X-ray diffraction. The surface morphologies and particle size of the electrolytes were studied by field emission electron microscopy. The ionic conductivity of the prepared solid electrolytes was studied by the electrochemical impedance spectroscopy technique. Finally, we have prepared a LiNi0.8Co0.1Mn0.1O2 (NCM 811) based cathode composite and studied the electrochemical performance of the fabricated all-solid-state lithium batteries. The mixed particle-sized solid electrolyte-based cathode composite exhibited higher specific capacitance (127.2 mA h g-1) than the uniform-sized solid electrolyte-based cathode composite (117.1 mA h g-1). The electrochemical analysis confirmed that the sulfide solid electrolytes with mixed particle size exhibited better electrochemical performance.

NiCo2S4 Bi-metal Sulfide Coating on LiNi0.6Co0.2Mn0.2O2 Cathode for High-Performance All-Solid-State Lithium Batteries.

NiCo2S4 nanoparticles (NPs) were dry coated on LiNi0.6Co0.2Mn0.2O2 (NCM622) cathode using a resonant acoustic coating technique to produce all-solid-state lithium batteries. The NiCo2S4 coating improved the electrochemical properties of the NCM622 cathode. In addition, NiCo2S4 eliminated the space-charge layer and the cathode showed an excellent affinity with the interface with a sulfide-based solid electrolyte as an inert material. X-ray diffraction patterns of NCM622 coated with NiCo2S4 showed the same peak separations and lattice parameters as those of bare NCM622. Field-emission scanning electron microscopy and electron dispersive spectroscopy mapping analyses showed that 0.3 wt% NiCo2S4-coated NCM622 had an evenly modified surface with NiCo2S4 NPs. X-ray photoelectron spectroscopy (XPS) revealed that the surface of 0.3 wt% NiCo2S4-coated NCM622 had two different S 2p peaks, a Co-S peak, and Ni and Co peaks, compared to those of bare NCM622. Electrochemical studies with electrochemical impedance spectroscopy and galvanostatic charge-discharge cycle performances showed that NiCo2S4-coated NCM622 retained a higher specific capacity over multiple cycles than bare NCM622. Especially, 0.3 wt% NiCo2S4-coated NCM622 exhibited a capacity retention of 60.6% at a current density of 15 mA/g for 20 cycles, compared to only 37.3% for bare NCM622. Finally, interfacial XPS and transmission electron microscopy-electron energy loss spectroscopy analyses confirmed the stable state of 0.3 wt% NiCo2S4-coated NCM622 with minimal side reactions.

Risk Factors of Undiagnosed Diabetes Mellitus among Korean Adults: A National Cross-Sectional Study Using the KNHANES Data.

In this cross-sectional study, we investigated the baseline risk factors of diabetes mellitus (DM) in patients with undiagnosed DM (UDM). We utilized the Korean National Health and Nutrition Examination Survey (KNHANES) 2010-2017 data. Data regarding the participants' demographic characteristics, health status, health determinants, healthcare accessibility, and laboratory tests were gathered to explore the differences between the DM, UDM, and without-DM groups. Among the 64,759 individuals who participated in the KNHANES 2010-2017, 32,611 individuals aged ≥20 years with fasting plasma glucose levels of <100 or ≥126 mg/dL were selected. The odds ratios (ORs) regarding family history of diabetes and the performance of national health and cancer screening tests were lower in the UDM group than in the DM group (adjusted OR: 0.54; 95% confidence interval (CI): 0.43, 0.66; adjusted OR: 0.74; 95% CI: 0.62, 0.89; adjusted OR: 0.71; 95% CI: 0.60, 0.85). The ORs of hypertension and obesity were higher in the UDM group than in the DM group (adjusted OR: 1.32; 95% CI: 1.06, 1.64; adjusted OR: 1.80; 95% CI: 1.37, 2.36, respectively). Patients with UDM were more likely to be exposed to DM-related risk factors than those with and without DM. Public health interventions to prevent UDM development are necessary.

FeS-biochar and Zn(0)-biochar for remediation of redox-reactive contaminants.

To enhance the removal of redox-reactive contaminants, biochars including FeS and Zn(0) were developed via pyrolysis. These biochars significantly promoted the removal of 2,4-dichlorophenol (DCP) by means of sorption and reduction. Compared to direct reduction with FeS and Zn(0), the formation of reduction intermediates and product was enhanced from 21% and 22% of initial DCP concentration to 41% and 52%, respectively. 2,4-Dinitrotoluene (DNT), chromate (CrO4 2-) and selenate (SeO4 2-) were also reductively transformed to reduction products (e.g., 2,4-diaminotoluene [DAT], Cr3+, and selenite [SeO3 2-]) after they sorbed onto the biochars including FeS and Zn(0). Mass recovery as DAT, Cr3+ and selenite was 4-20%, 1-3%, and 10-30% under the given conditions. Electrochemical and X-ray analyses confirmed the reduction capability of the biochars including FeS and Zn(0). Fe and S in the FeS-biochar did not effectively promote the reductive transformation of the contaminants. Contrastingly, the stronger reducer Zn(0) yielded faster reductive transformation of contaminants over the Zn(0)-containing biochar, while not releasing high concentrations of Zn2+ into the aqueous phase. Our results suggest that biochars including Zn(0) may be suitable as dual sorbents/reductants to remediate redox-reactive contaminants in natural environments.

Screening Model for Estimating Undiagnosed Diabetes among People with a Family History of Diabetes Mellitus: A KNHANES-Based Study.

A screening model for estimating undiagnosed diabetes mellitus (UDM) is important for early medical care. There is minimal research and a serious lack of screening models for people with a family history of diabetes (FHD), especially one which incorporates gender characteristics. Therefore, the primary objective of our study was to develop a screening model for estimating UDM among people with FHD and enable its validation. We used data from the Korean National Health and Nutrition Examination Survey (KNHANES). KNAHNES (2010-2016) was used as a developmental cohort (n = 5939) and was then evaluated in a validation cohort (n = 1047) KNHANES (2017). We developed the screening model for UDM in male (SMM), female (SMF), and male and female combined (SMP) with FHD using backward stepwise logistic regression analysis. The SMM and SMF showed an appropriate performance (area under curve (AUC) = 76.2% and 77.9%) compared with SMP (AUC = 72.9%) in the validation cohort. Consequently, simple screening models were developed and validated, for the estimation of UDM among patients in the FHD group, which is expected to reduce the burden on the national health care system.

Synthesis of Ag/NiO Honeycomb Structured Nanoarrays as the Electrode Material for High Performance Asymmetric Supercapacitor Devices.

Metallic silver nickel oxide honeycomb nanoarrays were synthesized via a surfactant-assisted hydrothermal route. The crystal structure of the Ag/NiO nanoarrays was confirmed by X-ray diffraction. X-ray photoelectron spectroscopy confirmed the valance state of the nickel, oxygen, and metallic silver. The morphological studies and energy dispersive X-ray spectroscopy revealed the honeycomb structured nanoarrays and the elemental distribution of the prepared sample, respectively. The three-electrode measurements showed that the Ag/NiO nanoarray is a suitable electrode material for supercapacitor applications, which delivers the maximum specific capacity of 824 C g-1 at a specific current of 2.5 A g-1. An Ag/NiO positive electrode-based asymmetric device was fabricated and tested. The asymmetric device yielded a high specific cell capacity of 204 C g-1 at a specific current of 2.5 A g-1 as well as a maximum energy density of 63.75 W h kg-1 at a power density of 2812.5 W kg-1. These results are comparable to those of (NiMH) metal hydride batteries.

In situ preparation of MgCo2O4 nanosheets on Ni-foam as a binder-free electrode for high performance hybrid supercapacitors.

A binder-free, MgCo2O4 nanosheet-like architecture was prepared on Ni-foam using a hydrothermal method. MgCo2O4/Ni-foam was characterized by X-ray diffraction, field emission scanning electron microscopy (FESEM), and transmission electron microscopy techniques. The FESEM image revealed a nanosheet array-like architecture. The MgCo2O4 nanosheets grown on Ni-foam exhibited the maximum specific capacity of 947 C g-1 at a specific current of 2 A g-1. Approximately 96% of the specific capacity was retained from the maximum specific capacity after 5000 continuous charge-discharge cycles. This hybrid device exhibited a maximum specific capacity of 52 C g-1 at a specific current of 0.5 A g-1, and also exhibited a maximum specific energy of 12.99 W h kg-1 at a specific power of 448.7 W kg-1. These results confirmed that the binder-free MgCo2O4 nanosheets grown on Ni-foam are a suitable positive electrode material for hybrid supercapacitors.