Experimental Simulation Research Articles

Incidental nanoparticle characterisation in industrial settings to support risk assessment modelling.

Research on nanoparticle (NP) release and potential exposure can be assessed through experimental field campaigns, laboratory simulations, and prediction models. However, risk assessment models are typically designed for manufactured NP (MNP) and have not been adapted for incidental NP (INP) properties. A notable research gap is identifying NP sources and their chemical, physical, and toxicological properties, especially in real-world settings. This work aims to provide insights into the release and physico-chemical properties of INP while contributing to improving models for INP release. INP release was evaluated through a case study in a ceramic tile firing facility, where aerosol (10nm - 10μm) properties were determined. The Control Banding (CB) Nanotool model was applied to test outputs based on provided input parameters. RESULTS: demonstrate the constant generation and release of INP during tile firing, with NP concentrations up to 68711/cm³ and mean diameters of 37nm, with 95% smaller than 100nm. Particle morphology was mostly spherical, suggesting nucleation from precursor gases as the main formation mechanism. INP chemical composition was driven by primary ceramic components, while trace elements like Ni and Ti exhibited size-dependent patterns. In vitro cell viability tests indicated low to medium cytotoxicity of PM2 aerosols, decreasing human alveolar epithelial cell viability in a concentration-dependent manner. Applying the risk model with varying input parameters revealed that the risk level (RL) based on severity scores decreased when aerosol size distribution data were used, illustrating the model's sensitivity to input variables. We conclude on the need for comprehensive experimental datasets to support risk assessment models and achieve effective risk management strategies in real-world scenarios.

Trap density estimation for TDDB testing in SiC power MOSFETs

Abstract The trap density estimation of SiC power MOSFET in time-dependent dielectric breakdown (TDDB) was investigated by using experiment and TCAD simulation. The gate leakage current (IGSS) under negative voltage was measured and a current spike was found which was linearly shifted with TDDB stress. Based on four-state nonradiative multi phonon (NMP) defect capture model, the mechanism and modeling of IGSS current spike was studied. Based on TCAD simulation, the characteristics of gate oxygen donor traps including trap energy level, trap density, and position distribution were estimated, and the trap density model in TDDB experiment was proposed and verified. This study may contribute to the estimation of trap density in SiC MOSFET.

Experimental and molecular dynamics simulation investigation on the CaO-based gelling process of waste MgCl2-riched brine

ABSTRACT The alkaline gelling backfilling technique is an important potential method for processing the brine tailings in the salt ore potash industry. It involves complicated reactions and products, which have direct influences on the backfilling process. In this study, the gelling material of the potash tailings was experimentally determined to be a 318-phase crystal structure and a corresponding molecular dynamics model was developed. The effect of proportion and crystal cluster size on the properties of paste was theoretically predicted based on the Compass II force field. The qualitative and quantitative comparison with the experimental results indicates that the model can reproduce the properties of the gelling process reasonably. Besides, the molecular diffusion, hydrogen bond network and interaction were clarified further based on the calculation results. The result indicates that the number of Mg2+ has a significant effect on the molecular diffusion of the system. The interaction energy between short-chain clusters and the other components is smaller than the energy between long-chain clusters and the other components so that the long-chain clusters can be more stable than the short-chain structures. The related results are expected to provide support for the development of low-cost gelling reagents and optimisation of the gelling backfill process.

A generative adversarial network with multiscale and attention mechanisms for underwater image enhancement

Underwater images collected are often of low clarity and suffer from severe color distortion due to the marine environment and Illumination conditions. This directly impacts tasks such as marine ecological monitoring and underwater target detection, which rely on image processing. Therefore, enhancing Underwater images to improve their quality is necessary. A generative adversarial network with an encoder-decoder structure is proposed to improve the quality of Underwater images. The network consists of a generative network and an adversarial network. The generative network is responsible for enhancing the images, while the adversarial network determines whether the input is an enhanced image or a real high-quality image. In the generative network, we first design a residual convolution module to extract more texture and edge information from underwater images. Next, we design a multi-scale dilated convolution module to capture underwater features at different scales. Then, we design a feature fusion adaptive attention module to reduce the interference of redundant features and enhance the local perception capabilities. Finally, we construct the generative network using these modules along with conventional modules. In the adversarial network, we first design a multi-scale feature extraction module to improve the feature extraction ability. We then use the multi-scale feature extraction module along with conventional convolution modules to design the adversarial network. Additionally, we propose an improved loss function by introducing color loss into the conventional loss function. The improved loss function can better measure the color discrepancy between the enhanced image and the real image. It is useful to reduce color distortion in the enhanced images. In experimental simulations, the images enhanced by the proposed methods have the highest PSNR, SSIM, and UIQM values, indicating that the proposed method has superior Underwater image enhancement capabilities compared to other methods.

Disturbance-Triggered Instant Crystallization Activating Bioinspired Emissive Gels.

Many marine organisms feature sensitive sensory-perceptual systems to sense the surrounding environment and respond to disturbance with intense bioluminescence. However, it remains a great challenge to develop artificial materials that can sense external disturbance and simultaneously activate intense luminescence, although such materials are attractive for visual sensing and intelligent displays. Herein, we present a new class of bioinspired smart gels constructed by integrating hydrophilic polymeric networks, metastable supersaturated salt and fluorophores containing heterogenic atoms. Upon external disturbance, the composite gels undergo an instant and reversible soft-rigid state transition, simultaneously turning on intense fluorescence and activating ultralong afterglow emission with a maximum lifetime of 877.15 ms. The experimental results and molecular dynamics simulations reveal that the disturbance-induced luminescence mainly results from the geometrical confinement of aggregated fluorophores and enhanced molecular interactions to immensely suppress the non-radiative dissipation. Given their versatile and sensitive disturbance-responsiveness, dynamic interactive painting and 3D smart optical displays are demonstrated. This study paves a new avenue to achieve disturbance-sensing soft materials and promotes the development of smart visual sensors and interactive optical displays.

Adsorptive Separation, Interfacial Configuration, and Mechanism of Dimethyl Carbonate–Methanol Azeotrope onto α-Al2O3: Experimental and Molecular Simulations

Adsorptive Separation, Interfacial Configuration, and Mechanism of Dimethyl Carbonate–Methanol Azeotrope onto α-Al₂O₃: Experimental and Molecular Simulations

Numerical and experimental simulation of the hydrodynamic performance of hydrofoil for a virtual mooring buoy

ABSTRACT This study addresses the alteration of buoy dynamics through the attachment of rotatable hydrofoils, thereby enabling a virtual mooring capability. Buoys equipped with this configuration are referred to as Virtual Mooring Buoys (VMB). The aim of this study is to alter the dynamic properties of the buoy by installing a rotatable hydrofoil. The research focuses on the effects of both steady and unsteady dynamics of the rotatable hydrofoil. Therefore, in order to realise the virtual mooring function, CFD numerical simulation and scale model experiment are used to analyse the hydrodynamic performance of the buoy when it dives in the target waters at the maximum diving speed of 0.3 m/s. A hydrofoil with a high lift-to-drag ratio suitable for the VMB has ultimately been designed. The hydrofoil designed for the VMB in this study fulfils the functional requirements of buoy virtual mooring.

Numerical analysis of natural convective heat transfer with porous medium using JUPITER

ABSTRACT Japan Atomic Energy Agency (JAEA) has developed a numerical method with the JUPITER code with a porous medium model to calculate the thermal behavior in primary containment vessels (PCVs) of Fukushima Daiichi nuclear power stations of Tokyo Electric Power Company (1F). In this study, we performed an experiment and numerical simulation of the natural convective heat transfer with the porous medium and compared those results. In comparison of the temperature and velocity distributions between the experiment and simulation, the temperature distribution in the simulation was in good agreement with the distribution in the experiment except the temperature near the top and side surface of the porous medium. The velocity distribution also agreed qualitatively with the experimental result. In addition, we also performed the numerical simulations with various effective thermal conductivity models to discuss the effect of the conductivity based on the internal structure of porous media on the natural convective heat transfer. The result indicated that the temperature distribution in the porous medium and the velocity distribution of the natural convection were significantly different for each model, and the simulation result with the Kunii–Smith model was close to the experimental results.

Non-linear enhancement of ultrafast X-ray diffraction through transient resonances

Diffraction-before-destruction imaging with ultrashort X-ray pulses can visualize non-equilibrium processes, such as chemical reactions, with sub-femtosecond precision in the native environment. Here, a nanospecimen diffracts a single X-ray flash before it disintegrates. The sample structure can be reconstructed from the coherent diffraction image (CDI). State-of-the-art X-ray snapshots lack high spatial resolution because of weak diffraction signal. Bleaching effects from photo-ionization significantly restrain image brightness scaling. We find that non-linear transient ion resonances can overcome this barrier if X-ray laser pulses are shorter than in most experiments. We compared snapshots from individual ≈ 100 nm Xe nanoparticles as a function of pulse duration and incoming X-ray fluence. Our experimental results and Monte Carlo simulations suggest that transient resonances can increase ionic scattering cross sections significantly beyond literature values. This provides a novel avenue towards substantial improvement of the spatial resolution in CDI in combination with sub-femtosecond temporal precision at the nanoscale.

Regulating Water Molecules via Bioinspired Covalent Organic Framework Membranes for Zn Metal Anodes.

The Zn metal anode in aqueous zinc-ion batteries (AZIBs) faces daunting challenges including undesired water-induced parasitic reactions and sluggish ion migration kinetics. Herein, we develop three-dimensional covalent organic framework (COF) membranes with bioinspired ion channels toward stabilized Zn anodes. These COFs, featured by zincophilic pyridine-N sites, enable effective regulation of water molecules at the anode-electrolyte interphase. Systematic experimental analysis and theoretical simulations reveal the optimized COF-320N membrane functions as ion pumps, accordingly facilitating Zn2+ transport and inhibiting direct contact between Zn anode and free water molecules. Consequently, the bio-inspired strategy achieves improved Zn2+ transference number (0.61), rapid de-solvation kinetics, and suppressed hydrogen evolution. The assembled Zn||MnO2 pouch cell integrated with COF-320N membrane exhibits favorable electrochemical performances. Such a bioinspired concept for optimizing Zn anodes opens new pathways in developing advanced energy storage devices.

Study of CO2-hydrate formation in contact with bulk nanobubbles: An investigation from experiment and molecular-dynamics simulations.

Nanobubbles (NBs) have been extensively investigated as a sustainable promoter for gas hydrate nucleation, which also contribute to the hydrate memory effect. However, less attention afforded to their effects on the hydrate-growth process, thus lacking a complete perspective of the overall effects from NBs on hydrate formation. We hypothesize that their effect on CO2 hydrate growth may vary depending on the properties of NBs. This study investigates CO2-hydrate nucleation and growth with a dual methodology. Laboratory experiments were conducted using bulk NBs generated either from CO2 hydrate dissociation or electric-field-based electrostriction in virgin water. Simultaneously, molecular dynamics simulations examined hydrate growth in contact with a single NB containing CO2 molecules. Experimental results indicate that NBs promote hydrate nucleation, with finer ones from electric fields leading to a slight promotion at the onset of hydrate growth, followed by inhibition. MD simulations reveal that while NBs can serve as a gas source for growth, denser NBs hinder the process due to stronger gas-water interactions and CO2 clustering. These results suggest that optimizing NB size and concentration is critical for maximizing gas-hydrate formation efficiency in industrial applications such as gas storage and carbon capture.

Operando Unveiling of Hydrogen Spillover Mechanisms on Tungsten Oxide Surfaces.

Hydrogen spillover is an important process in catalytic hydrogenation reactions, facilitating H2 activation and modulating surface chemistry of reducible oxide catalysts. This study focuses on the operando unveiling of platinum-induced hydrogen spillover on monoclinic tungsten trioxide (γ-WO3), employing ambient pressure X-ray photoelectron spectroscopy, density functional theory calculations and microkinetic modeling to investigate the dynamic evolution of surface states at varied temperatures. At room temperature, hydrogen spillover results in the formation of W5+ and hydrogen intermediates (hydroxyl species and adsorbed water), facilitated by Pt metal clusters. With increasing temperature, water desorption, reverse hydrogen spillover and surface-to-bulk diffusion of hydrogen atoms compete with each other, leading initially to reoxidation and then further reduction of W atoms in the near-surface. The combined experimental results and simulations provide a comprehensive understanding of the mechanisms underlying hydrogen interaction with reducible metal oxides, lending insights of relevance to the design of enhanced hydrogenation catalysts.

Single-Larva RNA Sequencing Reveals That Red Sea Urchin Larvae Are Vulnerable to Co-Occurring Ocean Acidification and Hypoxia.

Anthropogenic carbon dioxide emissions have been increasing rapidly in recent years, driving pH and oxygen levels to record low concentrations in the oceans. Eastern boundary upwelling systems such as the California Current System (CCS) experience exacerbated ocean acidification and hypoxia (OAH) due to the physical and chemical properties of the transported deeper waters. Research efforts have significantly increased in recent years to investigate the deleterious effects of climate change on marine species, but have not focused on the impacts of simultaneous OAH stressor exposure. Additionally, few studies have explored the physiological impacts of these environmental stressors on the earliest life stages, which are more vulnerable and represent natural population bottlenecks in organismal life cycles. The physiological response of the ecologically and commercially important red sea urchin (Mesocentrotus franciscanus) was assessed by exposing larvae to a variety of OAH conditions, mimicking the range of ecologically relevant conditions encountered currently and in the near future along the CCS. Skeleton dissolution, larval development, and gene expression show a response with clearly delineated thresholds that were related to OAH severity. Skeletal dissolution and the induction of Acid-sensing Ion Channel 1A at pH 7.94/5.70 DO mg/L provide particularly sensitive markers of OAH, with dramatic shifts in larval morphology and gene expression detected at the pH/DO transition of 7.71/3.71-7.27/2.72 mg/L. Experimental simulations that describe physiological thresholds and establish molecular markers of OAH exposure will provide fishery management with the tools to predict patterns of larval recruitment and forecast population dynamics.

Steam Generator Maintenance Robot Design and Obstacle Avoidance Path Planning.

To solve the issue of inconvenient and dangerous manual operation during the installation and removal of the main pipe plugging plate in the steam generator in nuclear power plants, a ten-degree-of-freedom plugging robot was designed in the present study that includes a collaborative robotic arm coupled with a servo electric cylinder. By establishing a joint coordinate system for the robot model, a D-H parameter model for the plate plugging robot was established, and the forward and inverse kinematics were solved. The volume level approximate convex decomposition algorithm was used to fit the steam generator model with a convex packet, and an experimental simulation platform was constructed. Lastly, path planning was carried out by using the RRT algorithm, with the paths divided into three phases for analysis. The simulation results show that the path in the first stage is relatively smooth, and the parameter changes in each joint are relatively stable. The path in the second stage exhibits zigzagging, with the parameter change curve of joint 9 being particularly evident, and the path in the third stage still exhibits zigzagging-the parameter change curves of joints 3 and 10 are particularly evident. The results of the present study show that, although the paths show a certain degree of zigzagging in complex environments, the plate plugging robot is still able to automatically complete the plate plugging task while avoiding obstacles, which greatly reduces the risk posed to the operator when exposed to a high-radiation environment, in addition to having certain research and application value.

Synthesis and evaluation of amino acid ionic liquid for enhanced oil recovery: experimental and modeling simulation studies

Recovering the remaining oil after primary and secondary extraction methods poses a significant challenge. Enhanced oil recovery (EOR) techniques, which involve injecting fluids into reservoirs, aim to increase recovery rates. Ionic liquids, known for their adaptability, are emerging as promising agents in EOR, improving oil displacement by reshaping fluid properties and interacting with reservoir rocks. This study investigates the eco-friendly amino acid ionic liquid, AAIL [G0.5 C12][Pro], for EOR applications, focusing on its characterization and performance. Using pre-prepared quaternary ammonium salt PAMAM G0.5 C12 and proline, AAIL [G0.5 C12][Pro] was synthesized and confirmed via FTIR and 1H-NMR analyses. Rheological analysis identified 7 g of AAIL [G0.5 C12][Pro] as the optimal concentration for peak performance. Laboratory sand-pack displacement experiments demonstrated an 11% increase in oil recovery at this concentration. Further, a 3D reservoir model simulation validated the enhanced oil recovery potential of AAIL [G0.5 C12][Pro]. The study introduces the novel amino acid ionic liquid [G0.5 C12][Pro], which demonstrates superior effectiveness in enhancing oil recovery through significant wettability modification and interfacial tension reduction, underscoring its potential as an effective and environmentally friendly EOR agent compared to other ionic liquids and conventional methods.

Assessment of tissue-air ratios in epoxy resin and PMMA phantoms for radiation dosimetry: findings from experimental measurements and Monte Carlo simulations.

This study assesses radiation doses in multi-slice computed tomography (CT) using epoxy resin and PMMA phantoms, focusing on the relationship between TAR (tissue air ratio) and kilovoltage peak (kVp). The research was conducted using a Hitachi Supria 16-slice CT scanner. An epoxy resin phantom was fabricated from commercially available materials, to simulate human tissue. The phantom contained four peripheral inserts and one central insert for dose measurement, with optically stimulated luminescent dosimeters positioned at various depths (2 to 10cm). Monte Carlo simulations were executed using the Geant4 Application for Tomographic Emission toolkit (GATE) to model photon transport, with the x-ray spectrum generated using SpekPy software. A non-linear fitting model was developed to describe the TAR-kVp relationship across different depths for epoxy resin and PMMA. Results indicated that TAR values were higher at low depths (2cm) and decreased with increasing depth, reflecting the x-ray beam's attenuation. For instance, at 80 kVp and 2cm depth, the experimental TAR for PMMA was 1.102 ± 0.011, closely matching the MC simulation value of 1.110 ± 0.036, resulting in a small difference of 0.7%. At a depth of 10cm, the experimental TAR for PMMA decreased to 0.245 ± 0.006, while the MC TAR was 0.248 ± 0.016, with a relative difference of 1.2%. Similar trends were observed for epoxy resin, where the experimental TAR ranged from 1.070 ± 0.014 at 2cm to 0.235 ± 0.009 at 10cm, while MC simulation values ranged from 1.080 ± 0.038 to 0.238 ± 0.017. Bland-Altman analysis confirmed these results, with mean differences of 0.008 for PMMA and 0.006 for epoxy resin, indicating high agreement between the experimental and simulated TAR values. This study highlights the importance of phantom material selection in dose assessment and the implications of TAR in dose correction within the context of diagnostic radiology.

Design and Analysis of a Liftfan for eVTOL Aircraft

Abstract Ducted liftfans provide greater hovering efficiency to eVTOL aircraft than open propellers of equal disk area. Liftfans are designed with minimised length to limit propulsor weight added and drag incurred during forward flight. Three challenges posed by the liftfan propulsor configuration are addressed in this paper. First, instead of a single-row propeller, liftfans require the design of a fully integrated stage. To maximise the hovering figure of merit, a non-dimensional measure of power consumption, the preliminary and 3D design variables of the rotor and splittered-diffuser stator rows are optimised simultaneously using 3D CFD. The resulting prototype liftfan design is validated through experimental testing. Second, despite efficiency improvements during hover, liftfans sustain additional inlet distortion and drag during forward flight due to their surrounding nacelles. A low-order model is developed to investigate the maximum range for different fan designs with varying diffuser area ratios and forward flight tilt angles. Design selections are validated using full annulus 3D CFD and experimental wind tunnel tests. Third, as the electric motors of liftfans are mounted within the hub, fan-driven active cooling is necessary to prevent overheating. The stagnation pressure losses incurred by cooling airflow must be minimised without impeding heat transfer. Low-order models are developed to predict motor heat transfer and loss and to guide the design of a mixed-flow cooling fan. Experimental testing and 3D CFD simulations confirm that the addition of forward sweep and adoption of a high blade count can reduce cooling fan losses.

A Bio-Inspired Visual Neural Model for Robustly and Steadily Detecting Motion Directions of Translating Objects Against Variable Contrast in the Figure-Ground and Noise Interference.

(1) Background: At present, the bio-inspired visual neural models have made significant achievements in detecting the motion direction of the translating object. Variable contrast in the figure-ground and environmental noise interference, however, have a strong influence on the existing model. The responses of the lobula plate tangential cell (LPTC) neurons of Drosophila are robust and stable in the face of variable contrast in the figure-ground and environmental noise interference, which provides an excellent paradigm for addressing these challenges. (2) Methods: To resolve these challenges, we propose a bio-inspired visual neural model, which consists of four stages. Firstly, the photoreceptors (R1-R6) are utilized to perceive the change in luminance. Secondly, the change in luminance is divided into parallel ON and OFF pathways based on the lamina monopolar cell (LMC), and the spatial denoising and the spatio-temporal lateral inhibition (LI) mechanisms can suppress environmental noise and improve motion boundaries, respectively. Thirdly, the non-linear instantaneous feedback mechanism in divisive contrast normalization is adopted to reduce local contrast sensitivity; further, the parallel ON and OFF contrast pathways are activated. Finally, the parallel motion and contrast pathways converge on the LPTC in the lobula complex. (3) Results: By comparing numerous experimental simulations with state-of-the-art (SotA) bio-inspired models, we can draw four conclusions. Firstly, the effectiveness of the contrast neural computation and the spatial denoising mechanism is verified by the ablation study. Secondly, this model can robustly detect the motion direction of the translating object against variable contrast in the figure-ground and environmental noise interference. Specifically, the average detection success rate of the proposed bio-inspired model under the pure and real-world complex noise datasets was increased by 5.38% and 5.30%. Thirdly, this model can effectively reduce the fluctuation in this model response against variable contrast in the figure-ground and environmental noise interference, which shows the stability of this model; specifically, the average inter-quartile range of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was reduced by 38.77% and 47.84%, respectively. The average decline ratio of the sum of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was 57.03% and 67.47%, respectively. Finally, the robustness and stability of this model are further verified by comparing other early visual pre-processing mechanisms and engineering denoising methods. (4) Conclusions: This model can robustly and steadily detect the motion direction of the translating object under variable contrast in the figure-ground and environmental noise interference.

Multi-AUV sediment plume estimation using Bayesian optimization

Sediment plumes created by dredging or mining activities have an impact on the ecosystem in a much larger area than the mining or dredging area itself. It is therefore important and sometimes mandatory to monitor the developing plume to quantify the impact on the ecosystem including its spatial-temporal evolution. To this end, a Bayesian Optimization (BO)-based approach is proposed for plume monitoring using autonomous underwater vehicles (AUVs), which are used as a sensor network. Their paths are updated based on the BO, and additionally, a split-path method and the traveling salesman problem are utilized to account for the distances the AUVs have to travel and to increase the efficiency. To address the time variance of the plume, a sliding-window approach is used in the BO and the dynamics of the plume are modeled by a drift and decay rate of the suspended particulate matter (SPM) concentration measurements. Simulation results with SPM data from a simulation of a dredge experiment in the Pacific Ocean show that the method is able to monitor the plume over space and time with good overall estimation error.

The Temporal and Spatial Evolution of Flow Heterogeneity During Water Flooding for an Artificial Core Plate Model

In the process of reservoir water flooding development, the characteristics of underground seepage field have changed, resulting in increasingly complex oil–water distribution. The original understanding of reservoir physical property parameters based on the initial stage of development is insufficient to guide reservoir development efforts in the extra-high water cut stage. To deeply investigate the spatio-temporal evolution of heterogeneity in the internal seepage field of layered reservoirs during water flooding development, water–oil displacement experimental simulations were conducted based on layered, normally graded models. By combining CT scanning technology and two-phase seepage theory, the variation patterns of heterogeneity in the seepage field of medium-to-high permeability, normally graded reservoirs were analyzed. The results indicate that the effectiveness of water flooding development is doubly constrained by differences in oil–water seepage capacities and the heterogeneity of the seepage field. During the development process, both the reservoir’s flow capacity and the heterogeneity of the seepage field are in a state of continuous change. Influenced by the extra resistance brought about by multiphase flow, the reservoir’s flow capacity drops to 41.6% of the absolute permeability in the extra-high water cut stage. Based on differences in the variation amplitudes of oil–water-phase permeabilities, changes in the heterogeneity of the internal seepage field of the reservoir can be broadly divided into periods of drastic change and relative stability. During the drastic change stage, the fluctuation amplitude of the water-phase permeability variation coefficient is 114.5 times that of the relative stable phase, while the fluctuation amplitude of the oil-phase permeability variation coefficient is 5.2 times that of the stable stage. This study reveals the dynamic changes in reservoir seepage characteristics during the water injection process, providing guidance for water injection development in layered reservoirs.