Estimation Of Layer Thickness Research Articles

Laboratory observations for examining estimates of soil dry surface layer thickness with parsimonious models

ABSTRACT Soil dry surface layer (DSL) thickness is often considered a key parameter for land surface resistance to gas exchange. Commonly used, simple models for DSL thickness are typically empirical in nature and based on limited observational evidence. Laboratory experiments were performed to test soil condition and boundary effects on DSL formation. DSL thickness was analyzed in soil columns with varying texture, initial water content, and potential evaporation rate. DSLs formed to greater depth in fine-textured compared to coarse-textured soils, when beginning from similar initial water content. Based on experiments, we compared a simple but physically-based mass balance DSL model to an empirical DSL model from the literature. The mass balance model performed better than the empirical relative-wetness-based model, and is similar in structure to the current DSL parameterization in the Community Land Model. Results suggest soil resistance parameterizations can be improved by employing simple, but texture-dependent, physically-based DSL formulations.

Physicochemical Characteristics of Feldspar Deposits in Zango Daji, Kogi State Using Geological Methods

Six rock samples were randomly collected and photographs, locational coordinates and elevation points were taken with appropriate tools and devices. Portions of samples were prepared into thin sections and observed under hand lenses, microscope, tested for hardness and other diagnostic properties. Specific density was also determined. The remaining samples were subjected to geochemical and mineralogical analysis. An average percentage composition of 17.1958 mass % of K2O was obtained which is far greater than its Calcium and Sodium contents, indicating it is a K-feldspar. Geochemical result showed averages of Si2O, Al2O3, K2O, Na2O, CaO, and Fe2O3, contents of 59.54 mass %, 17.83 mass %, 17.19 mass %, 2.33 mass %, 0.08 mass % and 1.60 mass % respectively while mineralogical results revealed it is orthoclase in view of its high alumina content of 17.83 percent. The results showed that the sample specification meets the British International Standard specification. Furthermore, electrical resistivity tomography survey was conducted by setting up Schlumberger Electrode Array to determine the significant resistivity contrast between the feldspar and the compact bedrock in the profile. The resistivity data of eight points were processed into Resists curves after conversion and subjecting the data by inversion with WinResist software to obtain 1 - D and 2 – D maps. Together with results of chemical analyses and vertical electrical sounding, the location map of mineralogical composition of the Oxides were determined. Field and petrographic evidence showed a potential source of gemstones and niobium. The study also revealed the occurrence of feldspar deposit are hosted by granitic and pegmatitic intrusive. The quantum and reserve estimates of both overburden layer and target mineral thickness were determined to be 44.3 ± 2.18 million metric tons and 741,933 ± 132,205 m³ respectively.

Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer’s, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.

Simultaneous Estimation of Dust Content and Layer Thickness in Martian Polar Layered Deposits From SHARAD Radargrams Using Hidden Markov Model

Simultaneous Estimation of Dust Content and Layer Thickness in Martian Polar Layered Deposits From SHARAD Radargrams Using Hidden Markov Model

Working Regime Criteria for Microscale Electrohydrodynamic Conduction Pumps.

We investigated the microscale electrohydrodynamic (EHD) conduction pumps in a wide range of working regimes, from the saturation regime to the ohmic regime. We showed that the existing macro- and microscale theoretical models could not accurately predict the electric force of microscale EHD conduction pumps, especially for the cases of a strong diffusion effect. We clarified that the failure is caused by a rough estimate of the heterocharge layer thickness. We revised the expression of heterocharge layer thickness by considering the diffusion effect and developed a new theoretical model for the microscale EHD conduction pumps based on the revised expression of heterocharge layer thickness. The results showed that our model can accurately predict the dimensionless electric force of the microscale EHD conduction pumps even for the cases of a strong diffusion effect. Furthermore, we developed a working regime map of microscale EHD conduction pumps and found that the microscale EHD conduction pumps more easily fall into the saturation regime compared with the macroscale EHD conduction pumps due to the enhanced diffusion effect; in other words, the microscale EHD conduction pumps have a wider saturation regime. We showed that the conduction number C0 could not distinguish the working regime of the microscale EHD conduction pumps because it does not take the diffusion effect into account. By employing the revised expression of heterocharge layer thickness, we proposed a new dimensionless number, C0D to distinguish the working regimes of microscale EHD conduction pumps.

Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model

Accurately and effectively estimating of the barrier layer thickness (BLT) is essential for the research of ocean thermodynamics, ocean dynamics, and air-sea interaction. Artificial intelligence model provides an effective means for accurately estimating BLT from sea surface and gridded Argo data. The present study focuses on the application of a hybrid particle swarm optimization-based artificial neural networks model (PSO-ANN) for estimating the BLT in the Indian Ocean. The input variables of the hybrid model include sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH) and precipitation (P), and the output variable is the BLT value. Multivariate satellite and gridded Argo data collected from the Indian Ocean between January 2012 and December 2019 (i.e., a database consists 239,568 training datasets and 34,224 testing datasets) are provided to prepare the training and testing datasets for the model. The parameters of ANN model, such as network parameter, network weights, and dropout rates are optimized using the PSO algorithm to achieve the best estimation model. R-squared (R2) and root mean square error (RMSE) are used to evaluate the performance of the model. Two groups of comparative experiments (Case 1 and Case 2) on the performance of the PSO-ANN model demonstrate that the model in Case 2 can better capture the complex features of the BLT in the ocean region. The performance of the PSO-ANN model in Case 2 is further compared with the data-driven estimation models such as the traditional ANN model and the known multilinear regression model (MRM), as well as the CESM2-WACCM dynamic model from CMIP6. The comparison results show that the dynamic model has the worst performance among the four models. Moreover, the annual average RMSE value for the PSO-ANN model is 1.83 m, which is 12% and 84% lower than that of the traditional ANN and MRM, respectively. The R2 value for the model of 0.85 is improved by 4% and 40% compared to that of two models. Furthermore, three regions with significant seasonal fluctuations of the BLT in the Indian Ocean are selected to further evaluate the estimation accuracy of the hybrid model in 2019; the Southeast Arabian Sea (SEAS), the Bay of Bengal (BoB), and the Eastern Equatorial Indian Ocean (EEIO). As a result, the hybrid model is capable of reflecting seasonal variation trends in these regions, but there is room for improvement in the estimation accuracy. In addition, the correlation analysis between BLT and sea surface parameters indicates that there exist significant correlations between the BLT and SSS, P. The results of this study show that the proposed hybrid model can be used to estimate and analyze BLT in certain regions with complex ocean dynamics processes. Moreover, the model can be extended to estimate other key ocean parameters and provide effective technical support for studying the internal ocean parameters.

Improving Estimation of Layer Thickness and Identification of Slicer for 3D Printing Forensics.

This study emphasizes the significance of estimating the layer thickness and identifying slicer programs in the realm of 3D printing forensics. With the progress in 3D printing technology, precise estimation of the layer thickness has become crucial. However, previous research on layer thickness estimation has mainly treated the problem as a classification task, which is inadequate for continuous layer thickness parameters. Furthermore, previous studies have concentrated on hardware-based printer identification, but the identification of slicer programs through 3D objects is a vital aspect of the software domain and can provide valuable clues for 3D printing forensics. In this study, a regression-based approach utilizing a vision transformer model was proposed. Experiments conducted on the SI3DP++ dataset demonstrated that the proposed model could handle a broad range of data and outperform the current classification models. Additionally, this study proposed a new research direction by introducing slicer program identification, which significantly contributes to the field of 3D printing forensics.

Meta-learning-based estimation of the barrier layer thickness in the tropical Indian Ocean

Accurately estimating the barrier layer thickness (BLT) is crucial for enhancing our understanding of the ocean’s role in climate variability on both regional and global scales. Here, we propose a meta-learning-based ensemble model to estimate the BLT using satellite observations in the tropical Indian Ocean. The results show that the meta-learning-based ensemble model outperforms the three individual models in terms of spatial distribution and accuracy, with significantly reduced root mean square errors in the Southeast Arabian Sea, Bay of Bengal, and eastern equatorial Indian Ocean. Furthermore, we found that sea surface salinity plays the most significant role in the estimation of BLT, highlighting the dominant influence of salinity stratification. These preliminary results provide an insight into the feasibility of predicting the BLT using satellite observations and have implications for studying the upper ocean dynamics using machine learning techniques.

Investigation of oxide growth during hard anodizing using in-situ and ex-situ EIS measurements

In this work, the oxide growth during soft and hard anodizing was investigated by a combination of in-situ and ex-situ EIS measurements. In-situ electrochemical impedance spectroscopy (EIS) measurements were found to provide accurate estimates of barrier layer thickness during soft anodizing but became inaccurate during the later stages of growth under hard anodizing conditions. This was attributed to the formation of a gradient in electrolyte composition, from the pore mouth to the pore base, caused by diffusion limitation. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) observations revealed that, in conditions where such a gradient was established, a rough internal pore morphology was present. The reduced availability of sulphate anions at the pore base, hinders the ejection of aluminium ions into the electrolyte, resulting in the formation of additional oxide or hydroxide at the pore base and the rough internal pore morphology.

Effect of ionic diffusion on microscale electrohydrodynamic conduction pumps

In this work, we investigate microscale electrohydrodynamic (EHD) conduction pumps of dielectric liquids in a wide working regime range from the Ohmic to the saturation regime. We show that the electric force of microscale EHD conduction pumps differs from that of macroscale EHD conduction pumps owing to the scale effect. We reveal that the scale effect of microscale EHD conduction pumps is triggered by the enhanced ionic diffusion. When the characteristic length of the system reduces from millimeter to micrometer, the maximum order of magnitude of α increases from O(10−5) to O(10−1), resulting in significantly enhanced ionic diffusion; however, the ionic diffusion can be neglected for macroscale EHD conduction pumps, where α denotes the ratio of ionic diffusion velocity to ionic migration velocity. We found that the existing macroscale theoretical models do not work for the microscale EHD conduction pumps due to the rough estimation of heterocharge layer thickness. We show that the heterocharge layer thickness of microscale EHD conduction pumps not only depends on ionic migration but also relies on ionic diffusion. By modifying the expression of the heterocharge layer thickness with respect to ionic migration and diffusion, we develop a theoretical model of dimensionless electric force for the microscale EHD conduction pumps.

Estimation of the Depletion Layer Thickness in Silicon Nanowire-Based Biosensors from Attomolar-Level Biomolecular Detection.

Silicon nanowire (SiNW) biosensors have attracted a lot of attention due to their superior sensitivity. Recently, the dependence of biomolecule detection sensitivity on the nanowire (NW) width, number, and doping density has been partially investigated. However, the primary reason for achieving ultrahigh sensitivity has not been elucidated thus far. In this study, we designed and fabricated SiNW biosensors with different widths (10.8-155 nm) by integrating a complementary metal-oxide-semiconductor process and electron beam lithography. We aimed to investigate the detection limit of SiNW biosensors and reveal the critical effect of the 10-nm-scaled SiNW width on the detection sensitivity. The sensing performance was evaluated by detecting antiovalbumin immunoglobulin G (IgG) with various concentrations (from 6 aM to 600 nM). The initial thickness of the depletion region of the SiNW and the changes in the depletion region due to biomolecule binding were calculated. The basis of this calculation are the resistance change ratios as functions of IgG concentrations using SiNWs with different widths. The calculation results reveal that the proportion of the depletion region over the entire SiNW channel is the essential reason for high-sensitivity detection. Therefore, this study is crucial for an indepth understanding on how to maximize the sensitivity of SiNW biosensors.

Numerical Simulations of the Adverse Effects of Rain on Airfoil And Rotor Aerodynamic Characteristics

There is a significant interest in improving the performance of rotors under adverse operating conditions. However, there is a very limited understanding of the performance implications on two-dimensional (2D) airfoils and rotor blades under adverse effects of rainfall. Furthermore, the fundamental physical phenomena causing the loss in performance are not clearly understood. In this study, low-fidelity models are first developed to rapidly estimate the water layer formation on 2D airfoils and assess the resulting impact on lift and drag characteristics. The low-fidelity simulations are also useful to obtain quick estimates of water layer thickness as a function of liquid water content and droplet diameter. Subsequently, computational fluid dynamics studies for 2D airfoils and a small-scale rotor in hover are done to obtain more accurate estimates of the effects of rain on airfoil performance and match test data where available. Higher fidelity parametric studies for various airfoils were conducted by varying angles of attack, the liquid water content in the rain droplets, and the droplet diameters to capture trends in performance degradation. The resulting trends match the trends from the test data reasonably well. The higher fidelity airfoil loads are subsequently used within a classical combined blade element-momentum model to assess the loss of performance attributable to rain for a small-scale rotor. The present studies indicate a significant loss in thrust production, a rise in the power requirement, and a reduction in the figure of merit for small-scale rotors caused by rain.

Using a 2D Profilometer to Determine Volume and Thickness of Stockpiles and Ground Layers of Roads

Construction materials and related management, handling, and storage provisions account for a large portion of road construction expenses. For that reason, improved material flow monitoring techniques can achieve significant cost and time savings, as well as better quality control. This study assessed the performance of a solid-state pulsed time-of-flight laser lidar profilometer in measuring the volume of soil stockpiles and road layer thicknesses. The 3D (X, Y, Z, and intensity) image calculation was based on the analysis of multiple combined point clouds measured with an excavator-integrated profilometer. Error analysis confirmed the accuracy of road layer thickness estimation within one centimeter and an error level of approximately 3% when measuring soil stockpile volumes. In conjunction with a theoretical model of the superstructure, this 3D measurement technique can help contractors and supervisors ensure road quality.

Polyacrylonitrile-Derived Carbon Nanocoating for Long-Life High-Power Phosphate Electrodes

Carbon-coating proved an efficient and reliable strategy to increase the power capabilities and lifetime of phosphate-based positive electrode materials for Li-ion batteries. In this work, we provide a systematic study on the influence of polyacrylonitrile-(PAN)-derived carbon coating on electrochemical properties of the nanosized Li-rich Li1+δ(Fe0.5Mn0.5)1−δPO4 (Li-rich LFMP) cathode material, as well as the characterization of carbon-coated composites by means of Raman spectroscopy for the determination of carbon graphitization degree, DF-STEM and STEM-EELS for the estimation of carbon layer thickness, uniformity and compositional homogeneity of the conductive layer respectively, and impedance spectroscopy for the determination of charge transfer resistances of the resulted composite electrodes in Li-based cells. Using PAN as a carbon coating precursor enables significantly enhancing the cycling stability of Li-rich LFMP/C compared to those conventionally obtained with the glucose precursor: up to 40% at high current loads of 5–10C retaining about 78 ± 2% of capacity after 1000 cycles. Varying the PAN-derived carbon content in the composites allows controlling the electrochemical response of the material triggering either a high-capacity or a high-power performance.

Development of a Road Pavement Structure Diagnostic Procedure Based on the Virtual Inertial Point Method

Falling weight deflectometers (FWD) are utilised worldwide to analyse the condition and the load-bearing capacity of road pavement structures. One of the FWD measurement results, the deflection bowl, may provide surplus information that is suitable for better road pavement structure diagnostics, based on the novel approach presented in this paper. This study presents a computational method that can calculate the layer thicknesses from the deflection data recorded by the non-destructive FWD device. The motivation for this research is that FWD and GPR equipment are often not available at the same time. However, the back-calculation of the pavement layer moduli from the deflections requires knowledge of the exact thicknesses. The developed method is based on the inertia point principle and provides not only the total pavement thickness but also the total asphalt thickness at each FWD drop point. From 25,200 linear elastic layered pavement models, 350 virtual inertia points could be identified. To describe the relationship between the structural model characteristics of the pavement (thickness and subgrade modulus) and the virtual inertia points, we chose the Gaussian process regression, a widely used method in machine learning. In addition to the thicknesses, the point of inertia can also be used to calculate the bearing modulus of the subgrade with high accuracy. Based on the data from the experimental road section, the radius value of the inertia point rc is not sensitive to the stiffness of the layers that compose the pavement structure, depending only on the total pavement thickness and the bearing capacity of the subgrade. The calculation was compared with the AASHTO (1993) procedure, and very similar values for the subgrade-bearing capacity were obtained. Moreover, in the near future, the method can be further developed to provide an estimation of layer thicknesses, together with a deflection measurement, especially adapted to continuous deflection measurement devices (Curviameter and Rolling Wheel Deflectometer).

Analytical estimation of thermomechanical distortion and interface layer thickness for gas metal arc lap joining of dissimilar sheets

Analytical estimation of thermomechanical distortion and interface layer thickness for gas metal arc lap joining of dissimilar sheets

Two-layer analytical model for estimation of layer thickness and flow using Diffuse Correlation Spectroscopy.

Diffuse correlation spectroscopy (DCS) has been widely explored for its ability to measure cerebral blood flow (CBF), however, mostly under the assumption that the human head is homogenous. In addition to CBF, knowledge of extracerebral layers, such as skull thickness, can be informative and crucial for patient with brain complications such as traumatic brain injuries. To bridge the gap, this study explored the feasibility of simultaneously extracting skull thickness and flow in the cortex layer using DCS. We validated a two-layer analytical model that assumed the skull as top layer with a finite thickness and the brain cortex as bottom layer with semi-infinite geometry. The model fitted for thickness of the top layer and flow of the bottom layer, while assumed other parameters as constant. The accuracy of the two-layer model was tested against the conventional single-layer model using measurements from custom made two-layer phantoms mimicking skull and brain. We found that the fitted top layer thickness at each source detector (SD) distance is correlated with the expected thickness. For the fitted bottom layer flow, the two-layer model fits relatively consistent flow across all top layer thicknesses. In comparison, the conventional one-layer model increasingly underestimates the bottom layer flow as top layer thickness increases. The overall accuracy of estimating first layer thickness and flow depends on the SD distance in relationship to first layer thickness. Lastly, we quantified the influence of uncertainties in the optical properties of each layer. We found that uncertainties in the optical properties only mildly influence the fitted thickness and flow. In this work we demonstrate the feasibility of simultaneously extracting of layer thickness and flow using a two-layer DCS model. Findings from this work may introduce a robust and cost-effective approach towards simultaneous bedside assessment of skull thickness and cerebral blood flow.

Ground Penetrating Radar applied to asphaltic pavements – a study in a controlled site

ABSTRACT. The major challenges in road infrastructure studies are related to the evaluation of the road pavement quality in a practical, non-destructive, and low-cost way. Information about the thickness of the layers is fundamental to pavement repairing. In this study, the applicability of the ground penetrating radar (GPR) method in the delimitation of the pavement layer is tested at the Shallow Geophysical Test Site Applied to Engineering of Universidade Estadual de Goiás (UEG). The ground penetrating radar presents satisfactory results, identifying the top and bottom of the first five layers by the 270, 400, and 900 MHz antennas. The 2 GHz antenna was able to only delimit the top and bottom of the Hot Rolled Asphalt (HRA). The geomembrane with geotextile set and the concrete layer were not identified in the radargrams. The resolution of each antenna is 9.8 cm for 270 MHz; 6.0 cm for 400 MHz; 2.9 for 900 MHz; and 1.6 cm for 1600 MHz. Concerning layer thickness estimates, the antennas had an efficiency percentage of 83 % (270 and 1600 MHz), 83.29 % (400 MHz), and 84.40 % (900 MHz). Layer thickness estimates obtained by GPR antennas were compared with true thickness using a paired t-test (α= 0.05). No significant differences in layer thickness were observed for the GPR antennas versus the actual pavement thickness.Keywords: GPR; geosynthetics; road investigation; shallow geophysical test site; non-destructive testing.

Estimating melt fraction in silicic systems using Bayesian inversion of magnetotelluric data

The location, volume and physical states of magma reservoirs are primary controls on the eruptive behavior of volcanic systems. Fundamental to understanding and monitoring these systems is the ability to identify reservoir size and physical properties, in particular melt fraction which plays an important role in the rheology and stability of a magmatic system. Large silicic volcanic eruptions in the geological record suggest that extensive pockets of melt-rich silicic magma must exist in the subsurface but such melt pockets have not been detected by geophysics. This has led to the question of whether the reservoirs that feed large volcanic eruptions are only melt-rich for a short time and thus would only be detected by geophysics shortly prior to an eruption. Magnetotelluric data measure the electrical resistivity of the subsurface and are sensitive to subsurface fluids and partial melts making it a powerful tool for imaging subvolcanic magma reservoirs. This study examines the ability for magnetotelluric data to accurately estimate melt fraction using both stochastic Bayesian inversion and deterministic regularized inversion. Results from synthetic modelling indicate that magnetotelluric data are best able to predict the melt fraction for the thick melt-rich layer using both inversion methods, though both methods under-estimate the true amount of melt. In addition, magnetotelluric data can accurately detect changes in melt fraction from crystal-rich mush (0.1 melt fraction) to melt-rich magma (0.9 melt fraction) for thick layers. Thickness is a key parameter which provides a method to assess the total volume of melt present, but it is difficult to estimate using smooth regularized inversions. Fixed-dimension Bayesian inversions provide estimates of layer thickness and their uncertainties and it is shown that estimates of total volume of melt are more accurate for both thin (0.2 km) and thick (1 km) layers than individual estimates of melt fraction or thickness alone. The melt fraction of the thin melt-rich layer is under-estimated using both the deterministic inversion method and the most probable solution of the Bayesian inversion. The Bayesian distribution of solutions has long tails which includes the true solution and better represents the uncertainty of the modelled parameters. As we consider past large melt-rich caldera eruptions at Taupo (New Zealand), Toba (Indonesia), Yellowstone and Long Valley (USA), our analysis suggests that thin melt-rich, potentially eruptible zones involved in large (e.g. >500 km3) eruptions could be systematically misinterpreted as thicker non-eruptible crystal-rich mush zones. Testing the sensitivity of MT to detect melt accumulation in active magmatic systems has important implications for volcanic hazard assessment and the use of Bayesian inversions allows for a better understanding of the range of possible interpretations.

Estimation of titanium oxide layer thickness on thermally oxidized NiTi alloy based on color variations

AbstractThis paper studies the estimation of titanium oxide (TiO2) thickness on a thermally oxidized nickel titanium (NiTi) alloy based on its surface color. The NiTi plate specimens were subjected to oxidation at temperatures from 450 °C to 900 °C for different duration to induce the formation of oxide layers of different thicknesses. The oxide thickness of the specimens oxidized from 650 °C to 900 °C was measured by means of scanning electron microscopy. A fitting curve was generated from the measured thicknesses, and further extrapolated to predict the oxide thickness for specimens heated at lower temperature of 450 °C to 600 °C. The oxide thickness estimation was done using thin‐film optical interference, with reference to the observed color of the oxidized surface. The thin‐film optical interference approach showed accurate measurement of oxide thickness as compared to the extrapolated data, highlighting the applicability of this measurement technique.