Determination Of Thickness Research Articles

Derivative of Ac back surface recombination velocity as applied to n+/p/p+ silicon solar cell optimum base thickness determination: Effect of both temperature and frequency

Derivative of Ac back surface recombination velocity as applied to n+/p/p+ silicon solar cell optimum base thickness determination: Effect of both temperature and frequency

Effect of geometric distortion correction on thickness and volume measurements of cortical parcellations in 3D T1w gradient echo sequences.

Automated brain volumetric analysis based on high-resolution T1-weighted MRI datasets is a frequently used tool in neuroimaging for early detection, diagnosis, and monitoring of various neurological diseases. However, image distortions can corrupt and bias the analysis. The aim of this study was to explore the variability of brain volumetric analysis due to gradient distortions and to investigate the effect of distortion correction methods implemented on commercial scanners. 36 healthy volunteers underwent brain imaging using a 3T magnetic resonance imaging (MRI) scanner, including a high-resolution 3D T1-weighted sequence. For all participants, each T1-weighted image was reconstructed directly on the vendor workstation with (DC) and without (nDC) distortion correction. For each participant's set of DC and nDC images, FreeSurfer was used for the determination of regional cortical thickness and volume. Overall, significant differences were found in 12 cortical ROIs comparing the volumes of the DC and nDC data and in 19 cortical ROIs comparing the thickness of the DC and nDC data. The most pronounced differences for cortical thickness were found in the precentral gyrus, the lateral occipital and postcentral ROI (2.69, -2.91% and -2.79%, respectively) while cortical volumes differed most prominently in the paracentral, the pericalcarine and lateral occipital ROI (5.52%, -5.40% and -5.11%, respectively). Correcting for gradient non-linearities can have significant influence on volumetric analysis of cortical thickness and volume. Since the distortion correction is an automatic feature of the MR scanner, it should be stated by each study that applies volumetric analysis which images were used.

Peripapillary Retinal Nerve Fiber Layer Thickness and its Ocular and Systemic Determinants in an Elderly Population: A Population-Based Study.

To determine the distribution, ocular, and systemic determinants of peripapillary retinal nerve fiber layer thickness (pRNFLT) using spectral-domain optical coherence tomography (SD-OCT) in an elderly population. This report is a part of the Tehran Geriatric Eye Study, a population-based cross-sectional study conducted in Tehran, the capital of Iran. The study population was all residents aged 60 years and above in Tehran. The sampling was performed using a multi-stage stratified random cluster sampling method. All study participants underwent ocular examination (including measurement of visual acuity, objective and subjective refraction, and slit-lamp biomicroscopy), anterior segment imaging using Pentacam HR, and ocular biometry using IOLMaster 500. The OCT imaging was performed for a random subsample (1307 individuals) using Spectralis SD-OCT. Two thousand two hundred and forty-six eyes of 1189 individuals were analyzed for this report. Of these, 691 (58.1%) were female, and the mean age of the participants was 67.3 ± 5.9 years (60-94 years). The mean overall pRNFLT was 98.6 μ (95% confidence interval [CI]: 98.0-99.3). There was a statistically significant difference in pRNFLT between different quadrants; the highest and lowest mean pRNFLT was related to inferior and temporal quadrants, respectively (P < 0.001). The multiple generalized estimating equation model showed that older age (coefficient: -0.15 [95% CI: -0.24 to -0.06], P = 0.001), diabetes (coefficient: -1.69 [95% CI: -2.82 to -0.55], P = 0.004), and longer axial length (coefficient: -0.52 [95% CI: -0.83 to -0.22], P < 0.001) were significantly associated with a decreased overall pRNFLT. Higher body mass index was significantly related to an increased overall pRNFLT (coefficient: 0.19 [95% CI: 0.07 to 0.30], P = 0.002). The results of the present study can be used as a reference database for pRNFLT in the elderly population. Considering ocular and systemic determinants of pRNFLT is necessary for correct clinical interpretation of this parameter.

Thickness Determination of Ultrathin 2D Materials Empowered by Machine Learning Algorithms (Laser Photonics Rev. 17(4)/2023)

Intelligent Detection of 2D Materials The difference in thickness often makes 2D materials have different properties, which is of great significance for regulating the performance of optoelectronic devices. In article number 2200357, Jun Wang and co-workers review optical methods to determine the thickness of 2D materials and further discuss the solution of the fusion with machine learning algorithms and the opportunities and challenges towards future applications.

Measuring the thickness of thin sintered layers precisely

AbstractOptical thickness determination with miniaturized spectrometers is a commonly used technique that works quite well in many applications. For sintered materials however, this technique must be extended by the determination of the optical constants of the sintered material. This can be done using a combination of scanning electron microscopy and effective medium approaches. In this article, we demonstrate this for the optical thickness determination of sintered Al2O3 layers in quartz tubes.

Determination of minimum thickness of composite panels taking into account limits on stability and fatigue under postbuckling behavior

Determination of minimum thickness of composite panels taking into account limits on stability and fatigue under postbuckling behavior

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.

Hyperspectral core scanner: An effective mineral mapping tool for apatite in the Upper Zone, northern limb, Bushveld Complex

The technological advances in efficient, rapid, and non-destructive hyperspectral core logging systems for systematic mineral mapping have led to the discovery and exploitation of new mineral deposits Hyperspectral imaging in the long-wave infrared range has been recently used successfully to identify various phosphate-bearing minerals (monazite, xenotime, and britholite), with limited work on apatite associated with mafic-ultramafic layered intrusions. In this study we investigate the effectiveness of a hyperspectral imaging (HSI) system with long-wave infrared (LWIR) bandwidthsto identify apatite in the Upper Zone of the Bushveld Complex. The accuracy of the HSI results was validated by mineralogical and geochemical data. The two apatite-enriched zones detected by HSI suggesting widespread development of apatite throughout the uppermost 600 m of the Upper Zone. The lower apatite-enriched zone is approximately 40 m thick, while the upper apatite-enriched zone is about 23 m thick, consistent with previous thickness determinations by traditional logging and analytical methods. Spectral mixing observed in the response of apatite is ascribed either to the common association of apatite and olivine in these rocks, or to differences between the spatial resolution of the hyperspectral image and the size of apatite grains. The VNIR-SWIR wavelength region did not show prominent spectral features of apatite. Nonetheless, HSI in the LWIR range is effective in mapping apatite and should therefore be considered as an exploration tool. This research advances our knowledge of the reflectance spectroscopy of REE-bearing minerals, which makes it easier to detect, identify, and quantify REE-bearing silicate minerals by HSI.

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

Influence of the design constraints on the thickness optimization of glass panes to achieve lightweight insulating glass units in cruise ships

The increasing complexity and size in cruise ships demands for lightweight structures and practical but accurate design methods. Conventionally, the focus has been on the steel parts of the ship, as they make most of its weight. However, the proportions of other materials are increasing. Therefore, this study attempts to provide better understanding how one could reach the lightweight designs of insulating glass units (IGUs) in ships. These are windows where at least two glass panes are separated by a hermetically sealed cavity. They are thin-walled structures that benefit not only from the geometrically nonlinear behavior, but also from the load sharing. Considering these effects, their behavior is studied using the nonlinear Finite Element Method and Particle Swarm Optimization. Different design criteria are imposed on the thickness determination of the glass panes with different shapes. Rectangular, triangular, and circular shapes are considered. The results show that the triangular shapes have the least weight for a given area when the deflection criterion is the dominating one. When maximum principal stress is the thickness defining criterion, the shapes perform almost equally well. The ratio between the pane thicknesses had the most influence on the behavior of the IGU. As it increases, i.e., one pane is significantly thicker than the other, the load sharing percentage drops, but it provides the most lightweight solution. Closer it is to 1, more equally the structural stresses are divided between the panes, i.e., redundancy is achieved. Finally, it is possible to establish a simple but effective method for the thickness determination of these IGUs using the results of this study. However, more work is required, including numerical analysis and experimental testing.

Non-Coding RNA Investigations in Cutaneous Melanoma: A Step forward in Discovering Novel Biomarkers

Non-Coding RNA Investigations in Cutaneous Melanoma: A Step forward in Discovering Novel Biomarkers

Thickness determination of multiphase layer via combined Rietveld and Grazing Incidence X-ray diffraction approach

Plates of Maraging-300 steel with a chemically grown layer on the surface were prepared by heat treatment in superheated steam at different times. The grown coating on the martensitic substrate was characterized in terms of thickness, compositional and structural information by means of optical and scanning electron microscopies and by Grazing Incidence X-ray Diffraction (GIXRD). The results showed the presence of the hematite, magnetite, and austenite crystalline phases in two different sublayers composing the entire reacted layer: an austenite sublayer over the maraging substrate, and an oxide sublayer at the top, right above the austenite layer, composed of interspersed hematite and magnetite. Quantitative Phase Analysis by the Rietveld method under GIXRD was used to obtain the thicknesses of multiphase chemically reacted layers. The methodology and mathematical formalism are described. The comparison with estimated values from optical and scanning electron microscopy have shown a good agreement between the imaging and diffraction techniques, suggesting that the reacted layer increases progressively with the heat treatment time.

Probabilistic determination and application of rock thickness resisting water inrush from karst cave

Water inrush disaster in karst tunnels is still a challenging topic, and the reasonable thickness of water-resistant rock mass is of great significance in its prevention. This paper proposes a method for determining the thickness of the water-resistant rock mass and its corresponding application flow. First, we encapsulated the frequently used model to calculate the thickness, i.e., the strength model, catastrophe model, and shear model, and anatomized the corresponding factors governing the thickness. Subsequently, the discreteness and uncertainty of rock mass were contemplated by introducing the rock mass homogeneity degree (RMHD). A corresponding calculating process for RMHD was proposed to quantify the distribution and the probability of rock mass parameters with the Weibull function. After that, the quantified probability parameters were brought into the three models to get the thickness and its corresponding probability. The accuracy and applicability of the proposed method are verified and discussed by two typical engineering cases. The results show that the calculation result of the strength model is more in line with the actual reserved value. The thickness obtained by considering the probability parameters of rock mass are well consistent with the actual reserved values. Finally, suggestions for practical application are put forward to distinguish water inrush risk levels and effectively guide the construction as part of safety management.

Determination of Optimum Outlet Slit Thickness and Outlet Angle for the Bladeless Fan Using the CFD Approach

Bladeless fans are more energy efficient, safer due to the hidden blades, easier to clean, and more adjustable than conventional fans. This paper investigates the influence of the airfoil’s outlet slit thickness on the discharge ratio by varying the outlet slit thickness of an Eppler 473 airfoil from 1.2 mm to 2 mm in intervals of 0.2 mm by using a k-omega SST turbulence model with an all y+ wall treatment used to numerically simulate in CFD. The computational results indicated that smaller slits showed higher discharge ratios. The airfoil with a 1.2 mm slit thickness showed a discharge ratio of 18.78, a 24% increase from the discharge ratio of the 2 mm slit. The effect of outlet angle on the pressure drop across the airfoil was also studied. Outlet angles were varied from 16° to 26° by an interval of 2°. The airfoil profile with a 24° outlet angle showed a maximum pressure difference of 965 Pa between the slit and leading edge. In contrast, the 16° outlet angle showed the least pressure difference of 355 Pa. Parameters such as average velocity (U), turbulent kinetic energy, the standard deviation of velocity, and outlet velocity magnitude are used to assess the performance of airfoil profiles used in bladeless fan.

Application of Geometric Simulation for Determination of Dynamic Undeformed Chip Thickness in Milling

Application of Geometric Simulation for Determination of Dynamic Undeformed Chip Thickness in Milling

Thickness Determination of Ultrathin 2D Materials Empowered by Machine Learning Algorithms

AbstractThe number of layers of 2D materials is of great significance for regulating the performance of nanoelectronic devices and optoelectronic devices, where the optimal thickness of the target sample should be determined before further physical research or device manufacturing steps. At present, a variety of different optical technologies have been proposed to determine the thickness of samples by using the relationship between the number of layers and optical properties, including optical contrast, optical imaging, Raman spectra, photoluminescence spectra, nonlinear spectra, near‐field optical imaging, ellipsometry spectra, and hyperspectral imaging. In the past decade, the rapidly growing number of 2D materials and their heterostructures has exceeded the capacity of traditional experimental and computational methods. In recent years, machine learning (ML) is emerging as a powerful tool to support such traditional methods, which brings new opportunities to tap the potential of optical technology in a more perspicacious way. The application of optical technology has greatly facilitated the acquisition of optical information of materials, while ML algorithms provide a fast, high‐throughput, and intelligent way to complete back‐end data processing and inference. A profound integration of conventional optical technologies and ML algorithms significantly helps 2D materials progress in basic studies and practical applications and further promotes industrial manufacturing.

Effect of Noise on Determining Ultrathin-Film Parameters from QCM-D Data with the Viscoelastic Model.

Quartz crystal microbalance with dissipation monitoring (QCM-D) is a well-established technique for studying soft films. It can provide gravimetric as well as nongravimetric information about a film, such as its thickness and mechanical properties. The interpretation of sets of overtone-normalized frequency shifts, ∆f/n, and overtone-normalized shifts in half-bandwidth, ΔΓ/n, provided by QCM-D relies on a model that, in general, contains five independent parameters that are needed to describe film thickness and frequency-dependent viscoelastic properties. Here, we examine how noise inherent in experimental data affects the determination of these parameters. There are certain conditions where noise prevents the reliable determination of film thickness and the loss tangent. On the other hand, we show that there are conditions where it is possible to determine all five parameters. We relate these conditions to the mathematical properties of the model in terms of simple conceptual diagrams that can help users understand the model's behavior. Finally, we present new open source software for QCM-D data analysis written in Python, PyQTM.

DESIGN AND PRODUCTION OF LEAD RUBBER BEARINGS FOR EARTHQUAKE ABSORBERS

Lead Rubber Bearing (LRB) is a passive type of base isolation that can be used as a damper for earthquake vibrations on bridges and buildings. The components that make up the LRB are several layers of rubber laminated to steel plates to increase vertical rigidity. LRB is also reinforced with lead cores to dissipate earthquake energy that enters the bridge structure or buildings. This paper will explain the design and production of LRB and test the results of the analysis. In this design, the properties of the materials used in the LRB are explained, the function of each material, the relationship between the design and the need to absorb earthquake loads on bridge structures or buildings. It also explains the determination of elastomeric thickness and number of layers, size of insulator, elastomer thickness and number of layers, number of rubber layers, total height, vertical and horizontal stability checks. The production method is very important to get a quality LRB product. Production starts from material selection, vulcanization process and quality control. The research was carried out successfully by studying the literature and technical analysis by carrying out several simulations with various dimensions to get better performance. Several developments were made to get better LRB performance. The damping of structures without base isolation is generally +/- 5%. For the need for good performance against earthquake loads, the attenuation can be increased to 20–30%. This is very useful for reducing earthquake loads, when a decrease in building acceleration cannot be obtained in tall buildings with base isolation. Likewise with the dimensions of the lead core. This research is still limited to theoretical analysis and prototyping, because no experiments have been carried out in the laboratory or applied to a construction project

Getting Cartilage Thickness Measurements Right: A Systematic Inter-Method Comparison Using MRI Data from the Osteoarthritis Initiative.

Magnetic resonance imaging is the standard imaging modality to assess articular cartilage. As the imaging surrogate of degenerative joint disease, cartilage thickness is commonly quantified after tissue segmentation. In lack of a standard method, this study systematically compared five methods for automatic cartilage thickness measurements across the knee joint and as a function of region and sub-region: 3D mesh normals (3D-MN), 3D nearest neighbors (3D-NN), 3D ray tracing (3D-RT), 2D centerline normals (2D-CN), and 2D surface normals (2D-SN). Based on the manually segmented femoral and tibial cartilage of 507 human knee joints, mean cartilage thickness was computed for the entire femorotibial joint, 4 joint regions, and 20 subregions using these methods. Inter-method comparisons of mean cartilage thickness and computation times were performed by one-way analysis of variance (ANOVA), Bland-Altman analyses and Lin's concordance correlation coefficient (CCC). Mean inter-method differences in cartilage thickness were significant in nearly all subregions (P < 0.001). By trend, mean differences were smallest between 3D-MN and 2D-SN in most (sub)regions, which is also reflected by highest quantitative inter-method agreement and CCCs. 3D-RT was prone to severe overestimation of up to 2.5 mm. 3D-MN, 3D-NN, and 2D-SN required mean processing times of ≤5.3 s per joint and were thus similarly efficient, whereas the time demand of 2D-CN and 3D-RT was much larger at 133 ± 29 and 351 ± 10 s per joint (P < 0.001). In automatic cartilage thickness determination, quantification accuracy and computational burden are largely affected by the underlying method. Mesh and surface normals or nearest neighbor searches should be used because they accurately capture variable geometries while being time-efficient.

Online Thickness Determination with Position Averaged Convergent Beam Electron Diffraction using Convolutional Neural Networks

Abstract Position averaged convergent beam electron diffraction (PACBED) is one of the most convenient and precise thickness determination techniques available in a scanning transmission electron microscope. The thickness is determined by finding the best match of the recorded PACBED pattern with a series of simulated diffraction patterns by visual inspection. The automatization of this process can be enhanced by convolutional neural networks (CNNs), making the method fast and easy to apply. However, the simulation of a synthetic dataset and the training of the CNNs carry a high computational cost. With the aim to simplify this process, we propose to build a server-based database of pretrained CNN models that is accessed by the user via a web service directly from the data acquisition and analysis software. We demonstrate a working prototype comprised of a shared CNN database containing three material systems. By this, the microscope operator can determine the specimen thickness by PACBED within a few seconds in a reproducible way during a microscope session, without any prior knowledge about machine learning or multislice modeling. Furthermore, the service is integrated into other software and workflows through the API.