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Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system. A head-mounted display was adopted to alleviate the inconvenience, and a virtual vision platform (VVP) is proposed in this study. The VVP can provide various critical data, including medical images, vital signs, and patient records, in three-dimensional virtual reality space so that users can access medical information simultaneously. An availability of the VVP was investigated based on various user evaluations by surgeons and novices, who executed the given tasks and answered questionnaires. The performances of the SV and VVP were not significantly different; however, the craniovertebral angle of the VVP was 16.35° higher on average than that of the SV. Survey results regarding the VVP were positive; participants indicated that the optimal number of displays was six, preferring the 2 × 3 array. Reflecting the tendencies, the VVP can be a neoconceptual candidate to be customized for medical use, which opens a new prospect in a next-generation surgical robot. [ABSTRACT FROM AUTHOR]

This study investigates the fuel atomization effect of alternating magnetic field (AMF)-based preprocessing equipment that promotes the atomization of automobile fuel. Two coils are connected to the output terminal of the preprocessing equipment and wound on an STS 316 L fuel pipe in the form of a solenoid. Positive sine waves and negative sine waves are output to the two coils, and the AMF in the solenoids causes atomization of the fuel flowing inside the pipe. A spray visualization experiment was conducted with fine particles sprayed from the injector, and the spray arrival distance, spray angle, and spray area were recorded. The information was analyzed to digitize the spray penetration, angle, and area at 2 ms after firing the injector. The fuel atomization effect of the AMF-based preprocessing equipment was also investigated Gasoline was used as the fuel, and the fuel atomization effect was analyzed using the number of windings around the STS 316 L fuel pipe, the frequency applied to the coil, the distance between the two solenoid coils, and the fuel injection pressure as variables. [ABSTRACT FROM AUTHOR]

Biotic ligand models (BLMs) and the sensitivities of indigenous species are used to assess the environmental risk considering the bioavailability of metals, such as nickel. However, the BLM‐based acute–to–chronic ratio (ACR) is required if the predicted no‐effect concentration (PNEC) cannot be derived from the chronic species sensitivity distribution (SSD). The applicability of the ACR approach for estimating BLM‐based PNEC for nickel from acute toxicity data was evaluated in the present study. The BLM‐based acute SSD for nickel was built using the sensitivities of 21 indigenous species and different taxon‐specific BLMs for each taxonomic group. To predict the acute sensitivity of invertebrates, the chronic crustacean nickel BLM with pH effect term, which can account for nickel toxicity at high pH levels, was used. This was used instead of the existing acute BLM for crustacean, which has too narrow a pH range to cover the pH dependency of toxicity. The final BLM‐based ACR of nickel, determined within a factor of 1.53 from the species‐specific acute and chronic sensitivities of the six species, was more reliable than the typical ACR estimated within a factor of 1.84. A linear relationship (r2 = 0.95) was observed between the PNECs using BLM‐based ACR and the PNECs derived from the BLM‐based chronic SSD of the European Union Risk Assessment Reports. In conclusion, the BLM‐based PNEC for nickel could be derived using the ACR approach, unlike when copper BLM was applied. The BLM‐based ACR for nickel is the first result calculated by directly comparing acute and chronic species sensitivities, and will contribute to the application of BLM‐based risk assessment in broader ecoregions. Environ Toxicol Chem 2023;42:914–927. © 2023 SETAC [ABSTRACT FROM AUTHOR]

This study aimed to verify the relationship between ideal sleep duration and physical fitness in South Korean adults and older adults. The secondary analysis data of 2,832 adults (19–64 years) and 629 older adults (65 years and above), obtained from the 2015 National Fitness Survey, were analyzed. Participants completed various physical fitness tests (grip strength, sit-ups, standing long jump, 50 m dash, sit-and-reach, and 20 m shuttle run in adults; grip strength, sit-up, sit-to-stand, sit-and-reach, back-scratch, one-leg standing test, and 6-minute walk in older adults) and were divided into four groups based on self-reported sleep duration. Group differences in physical fitness variables were analyzed with one-way analysis of variance, and Pearson's correlation was used to verify the relationship between sleep duration and physical fitness; statistical significance was set at p <.05. In adult males, there was a positive correlation between explosive muscular strength (standing long jump) and sleep duration (p =.046), whereas in adult females, there was a negative correlation between cardiopulmonary endurance (20 m shuttle run) and sleep duration (p =.026). However, in older adults, there was no significant correlation between sleep duration and physical fitness (p >.05). Further, across the sample, there were no significant group differences in physical fitness variables. A positive correlation exists between sleep duration and muscular strength in adult males and a negative correlation between sleep duration and cardiorespiratory endurance in adult females. Designing sleep and exercise programs based on these results may improve physical fitness among adults and older adults. Future studies that include controlled variables that affect physical fitness and sleep patterns are warranted. [ABSTRACT FROM AUTHOR]

Carbon nanostructures (CNSs) have sparked significant interest in modern science and industry due to their distinct properties and various carbon hybridization states. This makes them appealing for applications, including developing new energy storage devices, filtering systems, catalysts, sensors, and biomedical scaffolds. However, traditional methods' high production costs have prompted researchers to investigate more cost-effective manufacturing techniques. Among these, self-propagating high-temperature synthesis (SHS) has emerged as a promising option, using various carbon-based precursors as feedstock. This manuscript is the first comprehensive attempt to summarize and review a variety of carbon nanostructures synthesized using the SHS method. The review begins with an overview of carbon nanostructure characteristics and fabrication techniques, then delves into the main features of the SHS synthesis method and its suitability for CNS fabrication. It then looks at current approaches for CNS synthesis using the SHS, categorizing them based on the type of carbon precursors used. This analysis includes combustion thermodynamics, adiabatic temperatures and equilibrium phases; experimentally measured combustion temperatures and wave velocities; and combustion reaction chemistry. Simultaneously, we discuss the morphology, phase composition, and other physical-chemical properties of CNS synthesized using SHS. A dedicated section investigates the performance of CNSs in energy storage devices, catalysis, waste adsorption, and polymer reinforcement. Finally, we discuss methodological challenges, opportunities, and future research directions, offering a comprehensive overview of the current state and potential areas for research in this field. [Display omitted] [ABSTRACT FROM AUTHOR]

The synthesis of group IV transition metals (TMs) such as Ti, Zr, and Hf from their respective oxides (TMO 2) poses significant challenges with current methods. There is an urgent need for an environmentally friendly and versatile approach to establish sustainable, efficient, and adaptable TM synthesis technology from TMO 2. In this study, the LN process (Lee-Nersisyan), a novel and straightforward approach that employs CaMg 2 as the reductant for TMO 2 powders is introduced. This innovative strategy involves the thermal processing of TMO 2 + k CaMg 2 mixtures (with k values ranging from 2 to 5 moles) in an argon atmosphere at temperatures between 800 and 1200 °C. The process yields high-purity TM powder with particle sizes in the range of 5–25 μm and oxygen content of less than 0.2 wt%. We apply density-functional theory (DFT) to elucidate the interaction energies and equilibrium interatomic distances between Ti-Ca, Ti-Mg, Ca-Mg, Mg-Mg, Ca-Ca, and Ti-Ti metallic pairs. Additionally, we discuss the growth behavior of TM particles through molecular dynamics simulation (MD) employing LAMMPS. Our LN process represents a promising solution for efficient and sustainable TM synthesis from TMO 2. • An innovative method for producing TM (Ti, Zr, Hf) powders from oxides is presented. • Utilizing CaMg 2 as reductant results TM particles with 0.2 wt% of oxygen is obtained. • Calciothermic reduction of TMO 2 is found to be the primary driving force of reaction. • The interaction energies and interatomic distances of M-M pairs are calculated by DFT. • TM particles sintering and growth behavior based on MD simulation is highlighted. [ABSTRACT FROM AUTHOR]

• Clearance level of radioactive metal waste by electrolytic decontamination assessed. • LiCl-KCl eutectic-4 wt.% NiCl 2 with max. 8 wt.% CoCl 2 electrolyte at 500 °C used. • For Co conc.< 167 ppm, 3 decontamination cycles required to meet clearance level. • Electrolytic decontamination can be used to treat nuclear waste. Many countries are in the process of decommissioning their nuclear power plants. These power plants contain valuable metals, including Inconel alloys, which are often only contaminated on the surface. Metals are typically decontaminated using laser ablation. However, electrolytic decontamination is a simpler and less expensive solution. Our investigation aimed to develop a method for decontaminating Inconel alloy contaminated with Co to clearance level for recycling and reuse. This study focused on the electrolytic decontamination of Ni-0.2 wt.% Co alloy, identified as a potential replacement for the radioactively contaminated material. Molten LiCl-KCl-NiCl 2 -CoCl 2 (500 °C) was used as the electrolyte in the experiments and the potential recyclability of the used electrolyte was examined. In the absence of CoCl 2 in the electrolyte, the decontamination factor is 1.38E8 after the third cycle of electrolytic decontamination. Considering that the decontamination factor corresponding to the clearance level is 1.27E7, three cycles of electrolytic decontamination are considered to be sufficient. Additionally, an investigation of the possibility of recycling the used electrolyte showed that the solidified products formed after vacuum distillation were LiCl, KCl, and Co oxide with traces of Ni oxide. ICP analysis of the LiCl and KCl collected after vacuum distillation revealed that the concentration of Co was less than 0.1 ppm, which suggests that the used electrolyte can be recycled. These findings are expected to offer valuable insights into the electrolytic decontamination process, its limitations, and potential applications in the processing of nuclear waste. [Display omitted] [ABSTRACT FROM AUTHOR]

Efficient hydrogen production relies on cost-effective and durable non-noble metal electrocatalysts for the hydrogen evolution reaction (HER). In this work, a liquid metal-assisted solid-flame combustion synthesis (SF-CS) method was used to create transition metal carbide TMC) nanocrystals with exposed facets. The TMCs include TiC, VC, NbC, TaC, Ta 2 C, WC, and Mo 2 C. In acidic solutions, the TMC nanocrystals show enhanced conductivity and superior HER performance, as evidenced by their lower Tafel slope, higher current density, and decreased overpotential. In particular, Mo 2 C nanocrystals exhibit an overpotential of 95 mV and a Tafel slope of 26 mV dec−1. These findings highlight a noteworthy improvement over previously reported nanostructures in recent years in terms of the HER properties of Mo 2 C with particular facets. This improvement is attributed to the unique surface characteristics, such as energy and active sites, which are impacted by various factors and require more investigation to meet the intended goals. [Display omitted] [ABSTRACT FROM AUTHOR]

Age estimation is important in forensics, and numerous techniques have been investigated to estimate age based on various parts of the body. Among them, dental tissue is considered reliable for estimating age as it is less influenced by external factors. The advancement in deep learning has led to the development of automatic estimation of age using dental panoramic images. Typically, most of the medical datasets used for model learning are non-uniform in the feature space. This causes the model to be highly influenced by dense feature areas, resulting in adequate estimations; however, relatively poor estimations are observed in other areas. An effective solution to address this issue can be pre-dividing the data by age feature and training each regressor to estimate the age for individual features. In this study, we divide the data based on feature clusters obtained from unsupervised learning. The developed model comprises a classification head and multi-regression head, wherein the former predicts the cluster to which the data belong and the latter estimates the age within the predicted cluster. The visualization results show that the model can focus on a clinically meaningful area in each cluster for estimating age. The proposed model outperforms the models without feature clusters by focusing on the differences within the area. The performance improvement is particularly noticeable in the growth and aging periods. Furthermore, the model can adequately estimate the age even for samples with a high probability of classification error as they are located at the border of two feature clusters. • Most of the medical datasets are non-uniform in the feature space. • Non-uniformity causes distortion of the age estimation regressor. • Feature cluster-based age estimation model can resolve the distortion problem. • Classification head predicts the cluster in which the input image was included. • Regression head precisely estimates the age within the predicted cluster. [ABSTRACT FROM AUTHOR]

The intraoperative estimated blood loss (EBL), an essential parameter for perioperative management, has been evaluated by manually weighing blood in gauze and suction bottles, a process both time-consuming and labor-intensive. As the novel EBL prediction platform, we developed an automated deep learning EBL prediction model, utilizing the patch-wise crumpled state (P-W CS) of gauze images with texture analysis. The proposed algorithm was developed using animal data obtained from a porcine experiment and validated on human intraoperative data prospectively collected from 102 laparoscopic gastric cancer surgeries. The EBL prediction model involves gauze area detection and subsequent EBL regression based on the detected areas, with each stage optimized through comparative model performance evaluations. The selected gauze detection model demonstrated a sensitivity of 96.5% and a specificity of 98.0%. Based on this detection model, the performance of EBL regression stage models was compared. Comparative evaluations revealed that our P-W CS-based model outperforms others, including one reliant on convolutional neural networks and another analyzing the gauze’s overall crumpled state. The P-W CS-based model achieved a mean absolute error (MAE) of 0.25 g and a mean absolute percentage error (MAPE) of 7.26% in EBL regression. Additionally, per-patient assessment yielded an MAE of 0.58 g, indicating errors < 1 g/patient. In conclusion, our algorithm provides an objective standard and streamlined approach for EBL estimation during surgery without the need for perioperative approximation and additional tasks by humans. The robust performance of the model across varied surgical conditions emphasizes its clinical potential for real-world application. [ABSTRACT FROM AUTHOR]

[Display omitted] • Catalyst membrane for continuous-flow reaction. • Hybrid based on layered double hydroxide and gold nanoparticles. • Porous nanocomposite catalyst for fast and convenient catalysis. • Hydrangea-like porous LDH for dense decoration of gold nanoparticle. • Reusable hybrid porous clay-gold catalyst developed. We synthesized the hybrid catalyst of a porous layered double hydroxide (LDH) decorated with ultrasmall Au nanoparticles. The porous LDH was prepared by coprecipitation and ethanol washing, producing thin LDH nanoplatelets. Thereafter, spray drying was carried out, affording a few hundred nm of the hydrangea-like structure of the LDH with an extensive open network and a Brunauer–Emmett–Teller surface area of 386 m2/g. Its sizable open frame and active hydroxide surface enabled the uniform decoration of average 4-nm-diameter Au nanoparticles (Au/P-LDH) through one-pot chemical reduction using NaBH 4 at room temperature. We applied the Au/P-LDH for the fabrication of catalyst membranes, which are potentially valuable for the continuous flow of catalytic reactions, e.g., the hydrogenation of p-nitrophenol. This study suggested that their porous structure and active Au nanoparticle catalyst allowed fast and mass catalytic functions by simple filtration without washing or catalyst recovery steps. We found no significant damage to the hybrid membrane during the reactions with a few liters; thus, we expect strong bindings of the Au nanoparticles on the surface of the porous LDH. [ABSTRACT FROM AUTHOR]

The hot corrosion behaviors of Inconel alloys with different Cr contents (Inconel 600, 601, and 690), which are used widely in nuclear plants, were investigated in molten LiCl–Li2O salts. The hot corrosion behaviors were studied by measuring the mass and attack depth changes, surface and cross-sectional morphologies and elemental distributions, and compositional changes at the subscale and substrate scale as well as the spalled oxide scale. At 288 h, the weight losses of Inconel 601 and Inconel 690 were approximately four and twelve times higher, respectively, than that of Inconel 600. The corrosion products of all tested alloys were Cr2O3, NiO, and FeCr2O4. Inconel 600, which exhibited a dense and continuous external corrosion layer and an internal corrosion layer with localized corrosion behavior, exhibited superior corrosion resistance compared with those of Inconel 601 and 690, which showed a spalled external corrosion layer and an internal corrosion layer with uniform corrosion behavior. Thus, the corrosion resistance of the Inconel alloys tested in the hot lithium molten salts in an oxidizing atmosphere is closely related to the contents of the primary alloying elements in the alloys. Of the various alloys analyzed in this study, Inconel 600 exhibited the highest corrosion resistance. Thus, a Cr content of 16.30 wt% or less, Ni content of at least 73.66 wt%, and Fe content considerably lower than 8.15 wt% can result in excellent corrosion resistance. [ABSTRACT FROM AUTHOR]

The copper (Cu) biotic ligand model (BLM) has been used for ecological risk assessment by taking into account the bioavailability of Cu in freshwater. The Cu BLM requires data for many water chemistry variables, such as pH, major cations, and dissolved organic carbon, which can be difficult to obtain from water quality monitoring programs. To develop an optimized predicted no‐effect concentration (PNEC) estimation model based on an available monitoring dataset, we proposed an initial model that considers all BLM variables, a second model that requires variables excluding alkalinity, and a third model using electrical conductivity as a surrogate for the major cations and alkalinity. Furthermore, deep neural network (DNN) models have been used to predict the nonlinear relationships between the PNEC (outcome variable) and the required input variables (explanatory variables). The predictive capacity of DNN models was compared with the results of other existing PNEC estimation tools using a look‐up table and multiple linear and multivariate polynomial regression methods. Three DNN models, using different input variables, provided better predictions of the Cu PNECs compared with the existing tools for the following four test datasets: Korean, United States, Swedish, and Belgian freshwaters. Consequently, it is expected that Cu BLM–based risk assessment can be applied to various monitoring datasets, and that the most applicable model among the three different types of DNN models could be selected according to data availability for a given monitoring database. Environ Toxicol Chem 2023;42:2271–2283. © 2023 SETAC [ABSTRACT FROM AUTHOR]

Nanocomposites based on biomaterials are promising candidates for emerging green‐ electronics benefiting from environment‐friendly, renewable, biocompatible, and biodegradable resources for sustainable research and development. Especially, the application of biocomposites‐based memristor for simulating artificial synapses called bio‐memristor has further facilitated the progress of ecologically benign bioelectronics. In this study, the authors present that the environment‐friendly nanocomposites films, consisting of Ag nanoparticles and cellulose nanocrystal (CNC)‐based bio‐memristor with excellent bipolar resistive switching behavior can perform the artificial bio‐synaptic emulation with continuous resistance modulation for memory storage and neuromorphic computing applications. The bio‐memristor exhibits a large resistive switching (ION/OFF as high as ≈104 and ultralow SET/RESET voltage of ≈0.2 V) and reliable switching characteristics through the electrochemical formation/rupture of Ag metallic filaments within the nanocomposite layer. The device presents coexistence of digital and analog switching properties favorable for both nonvolatile digital memory and neuromorphic computing applications. By applying appropriate pulse stimulations to the device, the authors demonstrate biological synaptic functions, including long‐term potentiation/depression, spike‐rate‐dependent plasticity, excitatory post‐synaptic current, paired‐pulse facilitation, and paired‐pulse depression. Thus, this CNC‐based bio‐memristor as an effective artificial synaptic device is beneficial towards the realization of green‐electronics and bio‐inspired neuromorphic systems. [ABSTRACT FROM AUTHOR]

• Fabricated porous structure in p-type Cu 0.07 Bi 0.5 Sb 1.5 Te 3 compounds by assisting PVA as pore former and studied TE properties. • Lattice thermal conductivity effectively decreased by ~ 38% due to intensified phonon scattering at microscale pores. • The maximum ZT of 1.342 at 400 K and η max of ~ 9.4% at ∆T = 200 K was obtained for the sample with porosity of 10.58%. • The hardness and compressive strength of porous bulks show significantly higher than commercial zone melting materials. [Display omitted] In the background of enhancing thermoelectric (TE) figure of merit of solid-state materials, the introduction of porous structure can significantly decrease lattice thermal conductivity by intensifying phonon scattering at the inhomogeneous microscale pores. Herein, we developed a porous structure in Cu 0.07 Bi 0.5 Sb 1.5 Te 3 compounds by utilizing PVA as the pore former, and systematically studied its effect on thermoelectric properties. The results revealed that the reduction in thermal conductivity (~28%) was greater than the decrease in electrical conductivity (~17%) with increasing porosity, indicating long-wavelength phonon scattering was enhanced at micro-scale pores. Consequently, a peak zT of 1.342 at 400 K, and a zT avg of 1.254 in the temperature range of 300–500 K and an η max of ~9.4% at ΔT = 200 K were achieved in the porous sample (10.58%), which all are higher than that of dense sample. The hardness and compressive strength of the porous samples were significantly higher than commercial zone melting ingots. The proposed methodology is a cost-effective and efficient way of producing high-performance TE devices with good mechanical stability for real life practical applications. [ABSTRACT FROM AUTHOR]

In this study, the isothermal and cyclic corrosion behaviours of Haynes 282 and Haynes 214 in a lithium molten salt were investigated at 650°C for 168 h and 7 thermal cycles, respectively; this was achieved by measuring the mass changes, surface and cross-sectional morphologies with their elemental distributions, and compositional changes in the subscale, substrate and spalled oxide scale. The weight loss of Haynes 282 comprising an external corrosion layer was approximately four times lower than that of Haynes 214 with a spalled external corrosion layer, depending on the effects of their alloying elements and the preservation, spallation and dissolution of the corrosion layer. After 168 h and 7 thermal cycles, corrosion products of Haynes 282 were Cr2O3 and CoCr2O4 and those of Haynes 214 were Cr2O3, LiFeO2 and FeCr2O4. In addition, the internal corrosion layers of Haynes 282 and Haynes 214 exhibited localised and uniform corrosion behaviours, respectively. [ABSTRACT FROM AUTHOR]

Thousands of radiation portal monitors (RPMs) have been deployed around the world to detect radioactive materials that are outside of regulatory control. The detection capability of the RPMs can be evaluated by conducting a field test with various radioactive materials. However, field tests are limited because the RPMs are operated 24 h a day. The Monte Carlo N-Particle (MCNP) transport code provides an alternative. To evaluate the performance of the RPM with MCNP-based simulation model, the simulation results should be experimentally validated and the measurement result for the validation should be corrected for dead time effect. In this study, an MCNP-based RPM simulation model was developed and the accuracy of the RPM simulation model was improved by correcting dead-time effect. Static and dynamic measurements were conducted and the dead-time correction for the measurement data was factored in. With the static measurement data, the simulation model was developed and the comparing results with the static measurement data show that it satisfied an average relative difference of less than 10%. The simulation model was validated with 17 dynamic measurement datasets and the validation results show that the average relative differences are also within 10% even though the experimental conditions of the static and dynamic measurements were different. • A simulation model of radiation portal monitor was precisely constructed. • Counting losses of measurement data were corrected. • The modified simulation model satisfies 10% of errors with static measurement data. • Validation of the model with dynamic measurement data satisfies 10% of errors. [ABSTRACT FROM AUTHOR]

A simple surface modification strategy to generate lithophilic sites in carbon-coated Si nanoparticles significantly enhances the capacity as well as cycling stability. [Display omitted] • A simple surface treatment process generates lithophilic surface groups on carbon-coated Si nanoparticles. • The surface-functionalized electrode of Si-C-AT shows a higher diffusion coefficient than Si-C. • The Si-C-AT shows a reversible capacity of 1575 mA h g−1 after 200 cycles at a current rate of 0.5C. • Functional groups on the Si-C-AT assist in accumulating the lithium-ions in excess on the carbon surface. Silicon nanoparticles (Si NPs) are potential anode materials for next-generation lithium-ion batteries due to their natural abundance, low discharge potential, and high theoretical capacity. Carbon coating improves the capacity of the Si NPs, protects them from disintegration during the lithiation/delithiation process, and provides an additional conductive matrix. Surface modification as a tool leading to capacity enhancement in carbon-coated Si NPs has rarely been explored. It is a straightforward process that involves the surface functionalization of carbon-coated Si NPs (Si-C) with the oxidizing acidic mixture. The surface-treated Si-C functionalized with carboxyl and hydroxyl groups in excess on the carbon surface of Si-C NPs. As a result of surface modification, the acid-treated Si-C (Si-C-AT) displayed a higher reversible capacity of 1575 mA h g−1 after 200 cycles compared to the Si-C (1261 mA h g−1) and bare Si (961 mA h g−1). This high capacity of Si-C-AT is a cumulative outcome of the functional groups and the disordered structure of the carbon shell. The functional groups act as lithophilic sites, and the disordered carbon shell facilitates the insertion-desertion of lithium ions. The simple surface modification strategy proposed in the present study significantly enhances the cyclability of the Si-C-AT NPs and has great potential for application in carbon-coated materials for lithium-ion batteries. [ABSTRACT FROM AUTHOR]

The importance of ZT in thermoelectric (TE) materials is critical for green power generation and cooling applications. Therefore, tremendous efforts were underway to improve ZT, in which we proposed a layer structured approach by successively arranging of coarse Bi 0.5 Sb 1.5 Te 3 (BST) and fine BST/0.07Cu microstructure materials and investigated their transportation mechanism. The dual bulk microstructure was well-bonded and confirmed by analyzing the microstructure through scanning electron microscope (SEM), electron backscatter diffraction analysis (EBSD) and simulated the electrical potential and temperature distribution during spark plasma sintering with COMSOL software. The results indicate that the highest power factor of 3.8 mW/m-K2 was attained in the layer structured sample, which is significantly higher than other samples due to its synergistically optimized electrical conductivity and Seebeck coefficient. Through the varied microstructure, the κ was decreased at 400 K and consequently the ZT was improved to 1.25 at 400 K for the layer structure samples. Therefore, it is suggested that layered arrangement of different microstructure material could improve the thermoelectric properties of materials. [Display omitted] • Novel fabrication of hierarchical structured material with successive arrangement of two different microstructures. • Successful consolidation of well-bonded hierarchical material and confirming by simulating results from COMSOL software. • Highest power factor of 3.8 * 10−3 W/m-K2 obtained by transportation of charge carriers through two different energy levels. • Hierarchical structured sample show highest figure of merit of 1.25 at 400 K which is highest among all reported samples. [ABSTRACT FROM AUTHOR]

Spent nuclear fuel is electro-reduced in a chemically aggressive atmosphere wherein oxygen is generated at the anode in the presence of lithium molten salt containing a strong basic oxide, Li 2 O. Herein, the effect of the Li 2 O concentration on the hot corrosion behaviour of Haynes 263 and Inconel X-750 was investigated by immersion in molten LiCl–Li 2 O salt (1, 3, and 5 wt% Li 2 O) for 72–288 h at 650 ℃ under oxidizing atmosphere. For Li 2 O concentrations of approximately 3%, the weight loss and the products formed by the basic fluxing mechanism differed due to the dependence on the O2− concentration. • Effect of Li2O concentration on corrosion of Ni-based alloys was investigated. • Corrosion occurred by basic fluxing with O2 − ; products varied with ≥ 3 wt% Li2O. • Dense and continuous Cr-rich oxide layer prevented the inward diffusion of oxygen. • Ni-rich layer hindered outward diffusion of the alloying elements in the base metal. • Excess Fe appeared to be deleterious to the overall corrosion resistance. [ABSTRACT FROM AUTHOR]

Excessive methylmercury (MeHg) accumulation in dietary fish is a global concern due to its harmful effects on human health, however, environmental factors affecting MeHg accumulation in reservoir ecosystems are not clearly known. In this study, we aim to identify the main sources of MeHg in the water column and the critical factors related to MeHg concentration and methylation rate constant (k m) in sediment and total Hg concentration in fish using five-year (2016–2020) monitoring data of the five artificial reservoirs. The preliminary mass budgets constructed using the measurement and online data showed that sediment transport dominated over runoff in the long residence time reservoirs (400–475 days), while runoff dominated over sediment transport in the short residence time reservoirs (10 days). Whereas the sediment k m showed a comparable variation with the algal biomass, the sediment MeHg concentration and the length-normalized Hg concentration in the barbel steed and bluegill increased in the longer residence time reservoirs with lower algal biomass. As MeHg accumulation in sediment and fish tends to increase in the slowly overturning reservoirs, the hydraulic residence time should be carefully managed to meet the best protection of human health from chronic Hg exposure by fish consumption. [Display omitted] • MeHg mass budgets were estimated using the Hg data from five artificial reservoirs. • Sediment was the main source of MeHg in the long residence time reservoirs. • Runoff was the main source of MeHg in the short residence time reservoirs. • MeHg in sediment and fish were higher in the long residence time reservoirs. • Turnover period should be carefully managed to lower MeHg in reservoir fish. [ABSTRACT FROM AUTHOR]

• Consumer exposure to permethrin via the use of home insecticide spray was assessed. • Biomonitoring and mathematical modeling were conducted for 27 consumers. • The levels of metabolites increased significantly with the application of home insecticides. • The total amount of excreted metabolites was lower than estimated by the exposure model. Pyrethroids have been widely used as an active ingredient in home insecticide products since the 1960 s. Although their occurrence in indoor environments has been studied, the contribution of home insecticide application to the aggregate exposure to pyrethroids is not well known. The objective of this study was to estimate the consumer exposure to permethrin, a representative pyrethroid, via the use of home insecticide spray during the summer season using biomonitoring and personal exposure modeling. Exposure to permethrin was assessed by analyzing its urinary metabolites, 3-phenoxybenzoic acid (3-PBA) and cis / trans -3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropan carboxylic acid (cis / trans -DCCA), for a group of consumers (n = 27). The levels of metabolites were also compared with those predicted by a screening exposure model considering personal exposure parameters. The levels of metabolites in 15 participants increased significantly (p < 0.05) with the application of home insecticide products, thereby suggesting that the heavy use of home insecticides during summer could be an important exposure route of permethrin in addition to other sources, such as food consumption. The total amount of excreted 3-PBA and cis / trans -DCCA was lower than the amount estimated by the exposure model for most participants by a factor of 0.9–861.0. These differences could be attributed to the rapid loss of permethrin after application, including sorption to indoor surfaces, reaction with indoor substances, individual biological variations, and ventilation during application. However, the screening exposure model used for the initial safety assessment of biocidal products generally performed well because it did not underestimate the personal exposure to permethrin during the application of home insecticide spray. [ABSTRACT FROM AUTHOR]

Commercially available low-cost air quality sensors have low accuracy. The improved accuracy of low-cost PM2.5 sensors allows the use of low-cost sensor systems to reasonably investigate PM2.5 emissions from industrial activities or to accurately estimate individual exposure to PM2.5. In this work, we developed a new PM2.5 calibration model (HybridLSTM) by combining a deep neural network (DNN) optimized in calibration problems and a long short-term memory (LSTM) neural network optimized in time-dependent characteristics to improve the performance of conventional calibration algorithms of low-cost PM sensors. The PM2.5 concentrations, temperature and humidity by low-cost sensors and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmarks (multiple linear regression model (MLR), DNN model) and low-cost sensor results. The gravimetric measurements were used as reference data to evaluate sensor accuracy. For root-mean-square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41–60% of error when compared with the raw data of low-cost sensors, reduced 30–51% of error when compared with the MLR model and reduced 8–40% of error when compared with the MLR model. R2 of HybridLSTM, DNN, MLR and raw data were 93, 90, 80 and 59%, respectively. HybridLSTM showed the state-of-the-art calibration performance for a low-cost PM sensor. In other words, the proposed ML model has state-of-the-art calibration performance among the tested calibration algorithms. [ABSTRACT FROM AUTHOR]