Unusual dynamics of tetrahedral liquids caused by the competition between dynamic heterogeneity and structural heterogeneity.

Tetrahedral liquids exhibit intriguing thermodynamic and transport properties because of the various ways tetrahedra can be packed and connected. Recently, an unusual temperature dependence of the stretching exponent ß in a model tetrahedral liquid ZnCl2 from Tm + 85 K to Tm + 35 K has been reported using neutron-spin echo spectroscopy. This discovery stands in sharp contrast to other glass-forming liquids. In this study, we conducted neural network force field driven molecular dynamic simulations of ZnCl2. We found a non-monotonic temperature dependence of ß from liquid to supercooled liquid temperatures. Further structural decomposition and dynamic analysis suggest that this unusual dynamic behavior is a result of the competition between the decrease in the diversity of tetrahedra motifs (structural heterogeneity) and the increase in glassy dynamic heterogeneity. This result may contribute to new understandings of the structural relaxation of other network liquids.

First-Principles Investigation of the Effects of UF4 and ThF4 Fuels on the Structural, Dynamic, and Thermodynamic Properties of LiF-NaF.

An in-depth understanding and characterization of molten salt properties are necessary for the optimized design, efficient operation, and safety assurance of molten salt reactors (MSRs). Investigating molten salt properties in experimental settings can be challenging and time-consuming due to the high temperatures of interest, the salt's corrosiveness, purity and composition control, and health and safety concerns. Therefore, it is beneficial to perform computational screening to assist in the ultimate experimental measurements. Herein, we used first-principles molecular dynamics simulations to calculate several thermophysical, structural, and dynamic properties of eutectic LiF-NaF with fuel additives UF4 and ThF4. We found that with the incorporation of uranium or thorium, a prepeak appears in the structure factor, indicative of a medium-range structural ordering. Furthermore, we explore the mechanism through which these structural changes enhance shear stress correlations, thereby increasing the salt's viscosity. This work highlights the importance of studying the atomic-scale structure of molten salts and how the addition of fuel elements can substantially affect it.

Interpretation of autoencoder-learned collective variables using Morse-Smale complex and sublevelset persistent homology: An application on molecular trajectories.

Dimensionality reduction often serves as the first step toward a minimalist understanding of physical systems as well as the accelerated simulations of them. In particular, neural network-based nonlinear dimensionality reduction methods, such as autoencoders, have shown promising outcomes in uncovering collective variables (CVs). However, the physical meaning of these CVs remains largely elusive. In this work, we constructed a framework that (1) determines the optimal number of CVs needed to capture the essential molecular motions using an ensemble of hierarchical autoencoders and (2) provides topology-based interpretations to the autoencoder-learned CVs with Morse-Smale complex and sublevelset persistent homology. This approach was exemplified using a series of n-alkanes and can be regarded as a general, explainable nonlinear dimensionality reduction method.

Comparative Studies of the Structural and Transport Properties of Molten Salt FLiNaK Using the Machine-Learned Neural Network and Reparametrized Classical Forcefields.

Despite surging interest in molten salt reactors and thermal storage systems, knowledge of the physicochemical properties of molten salts are still inadequate due to demanding experiments that require high temperature, impurity control, and corrosion mitigation. Therefore, the ability to predict these properties for molten salts from first-principles computations is urgently needed. Herein, we developed and compared a machine-learned neural network force field (NNFF) and a reparametrized rigid ion model (RIM) for a prototypical molten salt LiF-NaF-KF (FLiNaK). We found that NNFF was able to reproduce both the structural and transport properties of the molten salt with first-principles accuracy and classical-MD computational efficiency. Furthermore, the correlation between the local atomic structures and the dynamics was identified by comparing with RIMs, suggesting the significance of polarization of anions implicitly embedded in the NNFF. This work demonstrated a computational framework that can facilitate the screening of molten salts with different chemical compositions, impurities, and additives, and at different thermodynamic conditions suitable for the next-generation nuclear reactors and thermal energy storage facilities.

A Camera-Based Position Correction System for Autonomous Production Line Inspection.

Visual inspection is an important task in manufacturing industries in order to evaluate the completeness and quality of manufactured products. An autonomous robot-guided inspection system was recently developed based on an offline programming (OLP) and RGB-D model system. This system allows a non-expert automatic optical inspection (AOI) engineer to easily perform inspections using scanned data. However, if there is a positioning error due to displacement or rotation of the object, this system cannot be used on a production line. In this study, we developed an automated position correction module to locate an object's position and correct the robot's pose and position based on the detected error values in terms of displacement or rotation. The proposed module comprised an automatic hand-eye calibration and the PnP algorithm. The automatic hand-eye calibration was performed using a calibration board to reduce manual error. After calibration, the PnP algorithm calculates the object position error using artificial marker images and compensates for the error to a new object on the production line. The position correction module then automatically maps the defined AOI target positions onto a new object, unless the target position changes. We performed experiments that showed that the robot-guided inspection system with the position correction module effectively performed the desired task. This smart innovative system provides a novel advancement by automating the AOI process on a production line to increase productivity.

Assessment of resident doses near nuclear power plants in Taiwan for epidemiology study.

Dose assessments were required for the epidemiological study of residents living near nuclear power plants. In the present work, environmental pathway models have been applied to estimate radiation doses to residents living near the nuclear power plants in Taiwan. Best estimates of doses were made for residents by their age groups in different compass sectors centered at the nuclear power plants. In each sector, radiation doses were assessed using the averaged environmental, consumption and lifestyle data. For epidemiological analyses of cancer risks in different organs or tissues, individual organ absorbed doses were assessed for both the airborne and waterborne effluent releases. Such assessments were performed based on the historic data, including measured effluent releases, detected meteorological parameters, and surveyed data on the production and consumption of local agricultural, fishery and livestock products, etc. Exposure pathways consisted of the external irradiations from air submersion, ground deposition and water immersion plus the internal irradiations from inhalation and ingestion. Age-dependent annual intakes and occupancy time were locally surveyed. Dose conversion coefficients were taken from published data after International Commission on Radiological Protection Publication 60. Annual doses and cumulated doses during residence were assessed and examined for their dependence on age, organ and compass sector.

Measurement of gamma-ray dose and neutron activation in BNCT beams using TLD-200.

In this work TLD-200 (CaF2:Dy) chips were used to measure the gamma-ray doses in a PMMA phantom exposed to the BNCT beam at Tsing Hua Open-pool Reactor (THOR). The self-irradiation component induced by the decay of Dy-165 was corrected. The neutron dose contamination was less than 0.3%. The Dy content in the TLD-200 chip was determined by using the modified absolute calibration method of NAA. The self-irradiation TL signal was also applied for the in situ calibration.

Decoupled Six-Axis Force-Moment Sensor with a Novel Strain Gauge Arrangement and Error Reduction Techniques.

In this study, a novel strain gauge arrangement and error reduction techniques were proposed to minimize crosstalk reading and simultaneously increase sensitivity on a decoupled six-axis force-moment (F/M) sensor. The calibration process that comprises the least squares method and error reduction techniques was implemented to obtain a robust decoupling matrix. A decoupling matrix is very crucial for minimizing error and crosstalk. A novel strain gauge arrangement that comprised double parallel strain gauges in the decoupled six-axis force-moment sensor was implemented to obtain high sensitivity. The experimental results revealed that the maximum calibration error, F/M sensor measurement error, and crosstalk readings were reduced to 3.91%, 1.78%, and 4.78%, respectively.

Autonomous Robot-Guided Inspection System Based on Offline Programming and RGB-D Model.

Automatic optical inspection (AOI) is a control process for precisely evaluating the completeness and quality of manufactured products with the help of visual information. Automatic optical inspection systems include cameras, light sources, and objects; AOI requires expert operators and time-consuming setup processes. In this study, a novel autonomous industrial robot-guided inspection system was hypothesized and developed to expedite and ease inspection process development. The developed platform is an intuitive and interactive system that does not require a physical object to test or an industrial robot; this allows nonexpert operators to perform object inspection planning by only using scanned data. The proposed system comprises an offline programming (OLP) platform and three-dimensional/two-dimensional (3D/2D) vision module. A robot program generated from the OLP platform is mapped to an industrial manipulator to scan a 3D point-cloud model of an object by using a laser triangulation sensor. After a reconstructed 3D model is aligned with a computer-aided design model on a common coordinate system, the OLP platform allows users to efficiently fine-tune the required inspection positions on the basis of the rendered images. The arranged inspection positions can be directed to an industrial manipulator on a production line to capture real images by using the corresponding 2D camera/lens setup for AOI tasks. This innovative system can be implemented in smart factories, which are easily manageable from multiple locations. Workers can save scanned data when new inspection positions are included based on cloud data. The present system provides a new direction to cloud-based manufacturing industries and maximizes the flexibility and efficiency of the AOI setup process to increase productivity.