Determination Of Layer Thickness Research Articles

Full-parametric and joint inversion of multimode surface wave data for identifying glacial ice thickness and freezing extent in subglacial sediments via the hunger games search algorithm

Determining glacier ice thickness and the extent of freezing in subglacial sediments are crucial in glaciological studies. Noninvasive geophysical methods, such as multichannel analysis of surface waves, are typically used for these tasks. In this study, we introduce a novel metaheuristic called hunger games search (HGS), which simulates the hunger-driven instincts and behavioral decisions of animals for the full-parametric inversion of seismic surface waves. We apply HGS to determine layer thicknesses, densities, S-wave velocities, and primary wave velocities of different layers through the joint inversion of multimode Rayleigh wave dispersion curves (RWDCs). This marks the first study using HGS for the inversion of dispersion data. Sensitivity studies of model parameters prior to the inversion indicate the necessity of postinversion uncertainty evaluations to mitigate the effects of varying sensitivity levels. In addition, a parameter tuning study is carried out to maximize the performance of HGS. Compared with some swarm intelligence-based optimizers (particle swarm optimization, cuckoo search, gray wolf optimization, sparrow search optimization, and whale optimization algorithm), HGS outperforms in the inversion of synthetic multimode RWDCs with 10% uncertainties with respect to a simulated glacier structure. In real data applications, a data set acquired at Midtdalsbreen, an outlet of the Norwegian Hardangerjøkulen ice cap, is inverted using the tailored HGS metaheuristic. The results obtained are consistent with previous geophysical studies. Furthermore, our analysis reveals that the performance of HGS when dealing with real applications is not highly sensitive to the selection of layers within the range of five to eight. The accuracy of HGS is undoubtedly contaminated by a larger model space; however, additional depth information derived from colocated ground-penetrating radar data can be directly integrated into HGS to obtain satisfactory results again.

Determination of Optimal Layer Thickness by Controlling Feed Rate of Powder in the DED Process

Determination of Optimal Layer Thickness by Controlling Feed Rate of Powder in the DED Process

Machine learning enabled fast optical identification and characterization of 2D materials

Two-dimensional materials are a class of atomically thin materials with assorted electronic and quantum properties. Accurate identification of layer thickness, especially for a single monolayer, is crucial for their characterization. This characterization process, however, is often time-consuming, requiring highly skilled researchers and expensive equipment like atomic force microscopy. This project aims to streamline the identification process by using machine learning to analyze optical images and quickly determine layer thickness. In this paper, we evaluate the performance of three machine learning models - SegNet, 1D U-Net, and 2D U-Net- in accurately identifying monolayers in microscopic images. Additionally, we explore labeling and image processing techniques to determine the most effective and accessible method for identifying layer thickness in this class of materials.

Comparative Analysis of Layer Thickness Measurement Methods for Photovoltaic Modules: A Comprehensive Study

The increasing volume of end‐of‐life (EoL) photovoltaic (PV) modules poses a significant challenge, necessitating efficient and sustainable recycling processes. In the PVReValue project, it is aimed to develop a comprehensive methodology for the systematic separation and recycling of EoL PV modules. Central to this effort is the precise determination of layer thicknesses, critical for effective characterization, and separation of input material. In this study, a comparative analysis of various industrial‐applicable methods is conducted for measuring layer thicknesses in PV modules. Both destructive and nondestructive techniques are evaluated based on criteria such as time, cost, accuracy, and applicability. Methods such as optical coherence tomography (OCT), coaxial multicolor confocal measurement, and ultrasonic measurements are assessed alongside traditional approaches like calotte grinding and optical 3D microscopy. In these findings, it is indicated that while OCT and confocal measurement offer high accuracy and nondestructiveness, they are complex and costly. Conversely, calotte grinding, although less costly, provides only localized information and can be challenging for polymers. In this study, it is concluded with recommendations for the possible integration of these methods into an industrial recycling workflow, highlighting the potential for improving PV module recycling efficiency and sustainability.

Design and Characterization of Porous Electrodes for Redox Flow Batteries

The commercialization of redox flow batteries as large-scale energy storage systems depends on lowering the capital and operating costs which depend on achieving maximum power density. Optimizing electrode design parameters and flow field-electrode combinations is critical to minimize losses and maximize active material utilization. Modeling the current drawn from an electrode offers a means to interpret electrode structure based on the electrolyte distribution driven by internal design. However, direct imaging methods (such as in-situ fluorescence microscopy and X-ray tomography) and numerical methods for current to potential prediction (3D Lattice Boltzmann modeling) are specific to individual electrode structures and possess limited predictable value to obtain an ideal electrode design. In this work, we present an electrode impedance spectroscopy (EIS)-based approach for determining electrode design parameters and devise a general method for determination of effective diffusion layer thickness. Using COMSOL modeling we verified the development of slug flow behavior in highly randomized porous electrode systems, indicating that the mass transport properties are dictated by inter-fiber distance. An analytical model to predict current as a function of applied overpotential is presented which allows us to vary structural parameters of the porous electrodes to study the influence of surface area, inter-fiber distances and diffusion layer thickness on the RFB performance. This model is a highly flexible tool applicable to any electrochemical system using a porous electrode and identifies critical variables affecting electrode design to characterize optimal electrode structure to minimize mass transport losses and optimize operational parameters.

Effect of incoming flow conditions on air lubrication regimes

Different air phase regimes are formed by controlled air injection in a spatially developing flat plate turbulent boundary layer (TBL). The air is introduced via a slot type injector without the use of a backward-facing step or cavitator upstream of the air injection position. The effect of different incoming liquid flow characteristics on the different regimes is investigated by varying both the liquid freestream velocity and the incoming TBL thickness. The latter is realized through changing the position of the air injection along the length of the water tunnel facility. That resulted in a downstream distance based Reynolds number from 1 to 5 million. Three different air phase regimes are identified under different air flow rates and freestream velocities: the bubbly regime, the transitional, and the air layer regime. The morphological differences of each one are described and quantitative analysis is performed based on the non-wetted area in each condition. The incoming TBL as well as the flow around the air layer are measured with planar particle image velocimetry. The latter enabled the determination of the air layer thickness. In addition, the ratio of the air layer to the incoming boundary layer thickness tair/δ is also calculated (≈ 0.04 – 0.5). This is a significant dimensionless parameter for scaling, which indicates the extent to which the air layer is embedded within the incoming TBL. Depending on the incoming flow conditions, a two or three branch air layer is formed. The length of the air layer is found to increase with increasing liquid freestream velocities. A good agreement between the air layer length and a half gravity wave predicted by the dispersion relation is found. An increase of the air layer length is observed with a decreasing incoming TBL thickness. This is attributed to a decrease in the local mean velocity at the air–water interface due to the TBL growth. Finally, increasing the incoming TBL thickness delays the onset of the air layer regime.

Quantitative Determination of 2D Layer Thickness of Atomically Thin Fe3GeTe2 in STEM

Quantitative Determination of 2D Layer Thickness of Atomically Thin Fe3GeTe2 in STEM

Mechanical performance simulation and optimal design of carbon fiber composite B-pillar

This study primarily concentrates on the layer optimization and structural refinement of carbon fiber composite B-pillars. In the initial phase, an initial layer configuration for the carbon fiber composite material B-pillar was devised, followed by comprehensive finite element numerical simulations. The weight of the B-pillar was effectively diminished by 44.9% compared to its metal counterpart, while maintaining consistent performance across diverse operational conditions. Subsequently, an advanced layer thickness optimization model was formulated, incorporating the innovative ‘super layer’ concept. Integration of simulation software, such as ISIGHT and ABAQUS, facilitated the determination of optimal layer thickness and ratio for the composite material. The layer order was systematically optimized through the application of a discrete particle swarm optimization algorithm based on exchange order, adeptly addressing issues related to discontinuous design variables and potential combinations. The incorporation of a ply drop-off structure laying scheme was derived, resulting in an impressive weight reduction of 57.5%. In the final phase, adherence to NCAP side collision testing standards enabled a comprehensive evaluation, where in various indicators including deformation mode, intrusion amount, and intrusion speed were employed. The results substantiate that the composite B-pillar exhibits equivalent side impact resistance to the original metal B-pillar, ensuring robust passenger protection.

Probabilistic models applied to concrete corrosion depth prediction under sulfuric acid environment

Accurate prediction of corrosion depth degradation law can provide a basis for the determination of the sacrificial layer thickness and the cover thickness of the concrete structural members in a corrosive environment. This study aims to explore a more accurate and safe method to predict corrosion depth. Six sets of concrete cylinder concrete specimens were adopted to perform an accelerated corrosion test in sulfuric acid solution with pH ≈ 0.95 for six different scheduled durations. 3D coordinates of a large number of points on the corroded cylindrical surface were obtained with the help of the 3D laser scanning technology. Then, a comparative study was carried out for the corrosion depth measured with the laser scanning method and vernier caliper method. The results prove that concrete corrosion depth predicted by the Gaussian random process model established by the laser scanning method was considered to possess higher credibility and better security.

Epitaxial silicon transition zone measurements by spreading resistance profiling and Fourier transform infrared reflectometry

AbstractSilicon epitaxy is an essential building block in the manufacturing of complementary metal‐oxide semiconductor (CMOS) devices. Accurate determination of epitaxial layer thickness is indispensable for a uniform and reproducible process. In this paper, we compare thickness values of the transition zone (TZ) in silicon epitaxial wafers obtained by two of Semilab's production‐compatible electrical and optical characterization techniques: Fourier‐transform infrared (FTIR) reflectometry and spreading resistance profiling (SRP). We demonstrate a high correlation between TZ thicknesses obtained from the optical modeling of FTIR reflectance spectra and SRP profiles. The dependence of TZ thickness change on the high‐temperature annealing steps is also examined. FTIR reflectometry thus offers a quick, contactless alternative for obtaining structural parameters of an epitaxial layer, and these values can be well matched to those given by SRP.

Highly reflective Mo/Be/Si multilayer mirrors with zero stress values for 13.5 nm wavelength

This study investigates mechanical stresses in Mo/Be/Si multilayer mirrors. We determined layer thicknesses (for Si∼1.0 nm, Mo∼2.8 nm and Be∼3.2 nm) in the period that simultaneously provided high reflection coefficients (R∼66–67%) at a wavelength of 13.5 nm with zero internal stress values. At the wavelength of 13.5 nm, the reflection coefficient of a stressless Mo/Be/Si multilayer mirror deposited on a micro-electro-mechanical system of micromirrors had a value of about 23% with a reflection from the witness of 67%. An interferometric method for determining the stress values in films, which is able to provide a measurement accuracy at the level of Δs ∼ ±0.24 MPa, is described in detail.

A semi-analytical model for predicting outflow concentration of vented turbidity currents with application in the Xiaolangdi reservoir

Turbidity current venting is an important way to reduce reservoir sedimentation. Existing methods to predict the outflow concentration of vented turbidity currents either depend on three-dimensional (3D) modelling with high computational cost or semi-empirical equation with a simple assumption of uniform velocity distribution. This study presents a semi-analytical model for predicting outflow concentration in turbidity current venting events, which accounts for the non-uniform and asymmetric velocity distribution in the withdrawal layer. Firstly, a 3D numerical model is established to facilitate the determination of withdrawal layer thickness and extraction of flow velocity within it, which is validated by simulating a group of venting experiments in the literature. Then, a velocity distribution equation for turbidity currents insusceptible to downstream disturbance is found to be applicable in the withdrawal layer, after relating the parameter controlling the velocity profile shape with the ratio of the outlet height to the layer thickness. Finally, the equation is derived for the relative concentration of outflow to the turbidity current concentration before the dam, whose solution is dependent on the proposed velocity distribution. Field measured data in the Xiaolangdi Reservoir and operation records of sediment release structures were collected and used to validate the model. A concept of nominal outlet width is proposed in order to apply the model in reservoirs with complex water releasing structures, which is calibrated along with the dimensionless parameter in the withdrawal layer thickness equation. The proposed model can help to optimize the design of sediment release structures and improve the venting operation.

Multiple solution solving in plasmon sensing by deep learning: determination of layer refractive index and thickness in one experiment: comment.

In a recent Letter [Opt. Lett.46, 5667 (2021)10.1364/OL.444442], Du et al. proposed a deep learning method for determining the refractive index (n) and thickness (d) of the surface layer on nanoparticles in a single-particle plasmon sensing experiment. This comment highlights the methodological issues arising in that Letter.

Development of a Generalized Photothermal Measurement Model for the Layer Thickness Determination of Multi-Layered Coating Systems

In this article, a general model for 1D thermal wave interference is derived for multi-layered coating systems (with n∈N coating layers) applied on a thermally thick substrate. Such a model means the first step to building a non-contact photothermal measurement device that is able to determine the coating thickness of each layer. Test objects are to be illuminated on the surface using planar, sinusoidal excitation waves with fixed frequencies leading to the generation of thermal waves inside the object. Due to the multi-layered structure, each of these thermal waves is reflected and transmitted at layer interfaces. This process leads to infinitely many wave trains that need to be tracked to formulate the final surface temperature as a superposition of all waves. A mathematical and physical formulation of thermal wave interference is needed to model this process and relate the dependencies of the layer thicknesses, the materials, and the frequencies to the phase angle data, which then can be measured using, e.g., an infrared camera. In practice, the thermal properties of the layers might be unknown, which makes the process even more difficult. This article presents a concept to determine the thermal properties in advance. Finally, numerical experiments are presented that demonstrate the feasibility of the introduced layer thickness determination process.

A45 FIBER-FREE DIET REDUCES BACTEROIDES ABUNDANCE AND PREVENTS MUCUS DEGRADATION IN MICE COLONIZED WITH MICROBIOTA FROM PATIENTS WITH GENERALIZED ANXIETY DISORDER

Abstract Background Generalized anxiety disorder (GAD) is a debilitating condition with a lifetime prevalence of 4-7% worldwide. We have previously found that compared to healthy controls, GAD patients had lower reported fiber intake, increased gastrointestinal symptoms; and enrichment of Bacteroides genus as well as carbohydrate metabolism pathways (as determined by PICRUSt2, correlated to Bacteroides abundance). Bacteroides are known for its ability to degrade a wide variety of host polysaccharides, such as the intestinal mucus, which could lead to local and systemic inflammation. In this regard, GAD patients had higher C-reactive protein (CRP) compared to healthy controls (p=0.049). Purpose To investigate whether a fiber-free diet could decrease Bacteroides abundance and prevent damage of the mucus layer reducing anxiety-behavior in mice with GAD microbiota. Method Two germ-free NIH Swiss mouse breeding pairs were colonized with GAD microbiota using patients’ stool samples and kept on either fiber-free or 10 % inulin (fiber) diet. Offspring were weaned at week 3 and psychometric tests were performed at 10 weeks of age. After sacrifice, samples for histology (mucus layer thickness determination), blood (CRP ELISA determination) and stool (Illumina 16S rRNA gene sequencing) were collected. The microbiota data was analyzed following the pipelines of dada2 and by mean comparisons, correlation, AncomBC using R software (v.1.2.1335). Multiple comparison results were corrected allowing 5% of FDR. Result(s) Beta diversity analysis showed that parent and offspring’s (n=24 fiber-supplemented and n=14 fiber-free groups) microbiota was similar to the GAD donor. The most differentially abundant bacterial taxon was Bacteroides uniformis, which was decreased in the fiber-free group (p.adj= 0.003). Furthermore, fiber-free diet reduced the overall Bacteroides abundance by half compared to the donor and fiber-supplemented mice group. This led to a thickening (p.adj=0.027) of the mucus layer, increasing from 30 µm (fiber-supplemented diet) to 60 µm in the fiber-free diet group. B. uniformis was negatively correlated to the mucus layer thickness (R= -0.81; p.adj=0.26), although not statistically significant, likely due to a low n number (n=4). We only found a statistical trend for higher CRP levels and anxiety-like behavior in the fiber-supplemented group. This might be because fiber supplementation has several beneficial effects that can counteract the proposed increase in anxiety-like behavior fromBacteroides. Despite that, we found a significant correlation between B. uniformis and time mice spent in the dark (indicative of anxiety-like behavior) in the light preference test. Conclusion(s) Our data suggests that Bacteroides abundance, specifically Bacteroides uniformis, contributes to the degradation of the mucus layer and potentially triggers low grade gut inflammation and anxiety-like behavior. Please acknowledge all funding agencies by checking the applicable boxes below CIHR Disclosure of Interest None Declared

A new simultaneous membrane thickness and catalyst loading measurement for fuel cell proton-exchange assemblies by IR transmission

We present a study of mid-infrared (mid-IR) transmission through typical proton-exchange fuel cell and water electrolyzer catalyst-coated membrane materials, and the determination of membrane layer thickness, and catalyst layer loading, from the power of the transmitted beam. Measured membrane thicknesses lay in the range of 25–125 μm, and catalyst areal loadings range from 0.05 to 0.35 mg/cm2. We show that the transmission spectrum correlated to membrane thickness and catalyst loading values separately, and also used a mathematical model to calculate both quantities simultaneously, from a single measurement of transmission. Measurements were carried out using a Fourier-transform infrared (FTIR) spectrometer, in specular transmission mode, in the wavelength range of 3–13 μm (3333–770 cm−1). We discuss the potential application of this method to the development of roll-to-roll manufacturing full-area-scanning quality inspection of catalyst-coated membrane (CCM) materials.

Determination of layer morphology of rough layers in organic light emitting diodes by X‐ray reflectivity

AbstractX‐ray reflectivity (XRR) has been proven to be a useful tool to investigate thin layers as well as buried interfaces in stacks built of very thin layers. Nevertheless, x‐ray reflectivity measurements are limited by the roughness of the layers and interfaces as the roughness destroys the interference structure, the so‐called Kiessig fringes. As investigations of thin layers in organic light emitting devices (OLEDs) are still subject of research and development, the focus of this paper is the investigation of a layer of indium tin oxide (ITO) which serves as transparent anode material in OLEDs. Due to the fabrication process, ITO shows rough surface structures, so‐called spikes, hindering the determination of the ITO layer thickness and roughness in XRR measurements. In this paper, it is theoretically and experimentally proven that a smoothing layer on the ITO enables the determination of the buried ITO layer thickness and roughness as well as the density of the spikes. Furthermore, a sputtered aluminum layer (e.g. cathode material) showing spikes in atomic force microscopy covered with a smoothing layer reveals Kiessig fringes allowing the determination of the density of buried spikes. In general, it is shown that a smoothing layer on a rough surface enhances the sensitivity of x‐ray reflectivity measurements.

Automatic asphalt layer interface detection and thickness determination from ground-penetrating radar data

Ground-penetrating radar (GPR) has been widely used in asphalt pavement layer thickness measurement. It requires the detection of pavement layer interfaces, which could be accomplished via manual inspection. This may be subjective and cause inaccurate thickness measurements due to the field data noise. Existing layer interface detection methods for thickness determination usually require customized programming and/or manual inputs for execution, which could hinder real-time applications. This article proposes an automatic method for asphalt pavement layer interface detection and thickness determination using GPR. The effects of in-situ noise levels on thickness measurement accuracy were discussed. The Canny edge detector was used to estimate the two-way travel time (TWTT) range. The wavelet transform was used for noise cancellation. Layer interface reflections were identified using the matched filter method for thickness determination. Vibration effect during the continuous GPR survey was mitigated using the empirical mode decomposition (EMD) method. A simulation study was conducted. The proposed method provided more accurate measurements than manual inspection for thickness values from 10.16 cm (4 in.) to 2.54 cm (1 in.). GPR field test was performed, and results were compared to field core thickness measurements. Results show that GPR-measured thickness errors are 6.5 % and 1.6 % using manual inspection and the proposed method, respectively. The proposed method is automatic and can be implemented using existing programming functions, which may enable real-time asphalt pavement thickness determination during GPR surveys.

Phase-sensitive terahertz upconversion detection.

Nonlinear frequency conversion provides an elegant method to detect photons in a spectral range which differs from the pump wavelength, making it highly attractive for photons with inherently low energy. Aside from the intensity of the light, represented by the number of photons, their phase provides important information and enables a plethora of applications. We present a phase-sensitive measurement method in the terahertz spectral range by only detecting visible light. Using the optical interference of frequency-converted photons and leftover pump photons of the involved ultrashort pulses, fast determination of layer-thicknesses is demonstrated. The new method enables phase-resolved detection of terahertz pulses using standard sCMOS equipment while achieving sample measurement times of less than one second with a precision error of less than 0.6%.

Determining alloyed layer thickness in electric discharge alloying by inverse estimation of energy distribution

Electric discharge alloying (EDA) is an important surface modification process, however, accurate determination of alloyed layer thickness is essential for effective utilization of this process in the industry. The spark energy distribution among the tool and workpiece controls alloyed layer thickness. In this work, a novel method is developed for accurate and quick determination of alloyed layer thickness through inverse estimation of energy distribution factor. A nonlinear transient finite element method based direct numerical model is developed for the analysis of EDA. The inverse estimation of the energy distribution factor is carried out by reducing the difference between actual and direct model computed data. For this purpose, an integrated finite element method–artificial neural network based method is developed. Physical experiments at the shop floor were also performed, which suggested that the inversely obtained energy distribution factor is sufficiently accurate in estimating the alloyed layer thickness at changed operating conditions.