Fast Analytical Method Research Articles

Microscaled Multi-frequency Eddy Current Analysis for High Throughput Characterization of Steel Micro-samples

In the present study microscaled samples were investigated using multi-frequency eddy current analysis with high spatial resolution. Multi-frequency eddy current analyses performed using different exciting frequencies have shown that the local signal distribution depends on the geometry of the samples. Due to this fact, the positioning of the sensor may influence the material state qualification. The investigation of several heat treatment conditions of steel 100Cr6 (AISI 52100) in spheroidized carbide, bainitic, martensitic quenched, and tempered states showed that the material states can be assessed and differentiated from each other. The recorded eddy current signals were analysed and discussed. It was shown that, with increased frequency, the differences of the values obtained for different material conditions increased, allowing a robust identifaction of the different states. The best differentiation was obtained for 10 MHz. The frequency-dependent position error was estimated. The position error decreased from 70% at10 kHz to 6% at 10 MHz. The study shows that this fast analysis method is qualified for fast screening of microscaled samples for high-throughput material development.

Characterization and Quantification of Arsenic Species in Foodstuffs of Plant Origin by HPLC/ICP-MS.

Arsenic is a well-known carcinogenic, mutagenic and toxic element and occurs in the environment both as inorganic arsenic (iAs) and organoarsenical compounds (oAsCs). Since the toxicity of arsenic compounds depends on their chemical form, the identification and determination of arsenic species are essential. Recently, the European Food Safety Authority, following the European Commission request, published a report on chronic dietary exposure to iAs and recommended the development and validation of analytical methods with adequate sensitivity and refined extraction procedures for this determination. Moreover, the authority called upon new arsenic speciation data for complex food matrices such as seaweeds, grains and grain-based products. Looking at this context, an optimized, sensitive and fast analytical method using high performance liquid chromatography followed by inductively coupled plasma-mass spectrometry (HPLC/ICP-MS) was developed for the determination of iAs (sum of arsenite-AsIII and arsenate-AsV) and the most relevant oAsCs, arsenobetaine, dimethylarsinic acid and monomethylarsonic acid. The method was validated with satisfactory results in terms of linearity, sensitivity, selectivity, precision, recovery, uncertainty, ruggedness and matrix effect, and then successfully applied for the analysis of several matrices, i.e., processed and unprocessed cereal and cereal products, fruits, vegetables, legumes, seaweeds, nuts and seeds. The results obtained indicate that not only seaweed and rice matrices but also many cereals, legumes and plant-based foods for infants and young children contain significant concentrations of iAs and oAsCs. These findings contribute to the data collection necessary to assess the role of these matrices in the total arsenic exposure and if specific maximum limits have to be established.

A Fluorescence Turn On-off-on Method for Sensitive Detection of Sn2+ and Glycine Using Waste Eggshell Membrane Derived Carbon Nanodots as Probe.

Changes in Sn2+ and glycine levels are relevant to many important physiological procedures in human health. However, investigation of their physiological functions is limited because few versatile methods towards Sn2+ and glycine detection have been developed. In this work, a fluorescence turn on-off-on strategy was firstly constructed for rapid and sensitive detection of Sn2+ and glycine through the specific binding between Sn2+ and glycine. Carbon nanodots (CDs) with a quantum yield of 19.5% were synthesized by utilizing inner film of waste eggshell as carbon source and employed as fluorescent probe. In the presence of Sn2+, the fluorescence of CDs was quenched by Sn2+ via the primary inner filter effect (IFE). However, the binding between Sn2+ and glycine prevented the IFE between Sn2+ and CDs, resulting in fluorescence recovery of CDs. Under optimized conditions, the fluorescent response of CDs displayed good linear relationships with the concentrations of Sn2+ in the range of 10-200µM and 200-5000µM, and the limit of detection (LOD) was 2.4µM. For glycine detection, a good linear relationship was obtained in the concentration range of 5-1000µM with a low LOD down to 0.76µM. Moreover, the practicability of the assay was also demonstrated by measuring glycine content in human serum samples. This work provides an economical, green and fast method for biological analysis of Sn2+ and glycine.

A Simple Methods for Determination of Methylene Blue using Smartphone-Based as Digital Image Colorimetry

The advancement of smartphones that produce high-resolution photos has made their use even more developed. One application that is currently developing is a digital image produced as colorimetry. Digital image-based colorimetry is an easy and simple determination method and can be an alternative for analyzing dyes quantitatively. In this method, the color response of the digital image is analyzed, namely red-green-blue (RGB) with values from 0 to 255. This data is extracted from digital images using ImageJ 1.52a software for quantitative determination. In this study, 2 standard methods were used, namely external standards and standard addition to evaluating the most suitable method to obtain accurate measurements. In addition, 2 methods were used to determine the relationship of response to methylene blue levels. The 1st method used simple linear regression of each color red, green, or blue (univariate). The 2nd method is used in multivariate linear regression of the 3 colors red, green, and blue. The accuracy of the method was validated using UV-Vis spectrophotometry as the reference method. The results showed that the multivariate models produced very good accuracy, namely precision of up to 99 % compared to the simple regression method with the univariate. HIGHLIGHTS Smartphone cameras can be applied easily as a simple, fast, and inexpensive colorimetric method for quantitative analysis of methylene blue content The univariate models for external standard and standard addition methods showed up to 90 % precision for fish drug samples against UV-Vis spectrophotometric methods The multivariate models for the external standard method provide up to 99 % precision results for fish drug samples against UV-Vis spectrophotometric methods· GRAPHICAL ABSTRACT

Rapid determination of lead (Pb) in the soil-plant system by laser-induced breakdown spectroscopy (LIBS): case study of Pb-pollution from perovskite solar cells.

With the widespread usage of lead (Pb)-containing perovskite solar cells (PSCs), it is critical to monitor Pb pollution from PSCs in the environment. Among different analytical techniques, laser-induced breakdown spectroscopy (LIBS) has demonstrated good performance in the fast quantification of many elements in solid samples, without using toxic and expensive chemical reagents. Therefore, LIBS offers significant potential for detecting and quantifying Pb in the environment. In this study, a Pb migration model in the PSCs-soil-Houttuynia plants system was assessed based on the LIBS data. The Pb transfer rates and the Pb uptake coefficients were calculated to evaluate Pb migration from PSCs to plants. The results showed that the R2 of quantitative results were all greater than 0.98, with the root-mean-square error of cross-validation (RMSECV) being less than 1.53 wt.%. Furthermore, above 49% Pb from PSCs was swiftly diffused into soil under watering conditions, while Houttuynia plants absorbed over 10% Pb from polluted soil. This study revealed that Pb leakage from PSCs should not be underestimated and that LIBS is a viable and fast analytical method for monitoring Pb in the environment.

A reduced-plate model transmission method for fast dynamic analysis of vehicle–pavement interaction

A reduced-plate model transmission method is developed in this study to accurately and efficiently analyze the vehicle–pavement interaction dynamics over long travel distances. A wide pavement is reduced to many small areas (referred to as the reduced-plate models) along the vehicle-moving trajectory, and each reduced-plate model accounts for the vibration of the plate structure caused by the moving vehicle in a small period. All reduced-plate models are assumed as stationary in the global coordinate system, and the vehicle moves from one reduced-plate model to another. The dynamic equations of the reduced-plate model are derived using the modal superposition method (MSM) and Lagrange's equations, and the calculation results of one reduced-plate model are considered as the initial conditions of the next reduced-plate model. Because only the reduced-plate model that is located inside the vehicle needs to be calculated in different periods, the number of degrees of freedom of the model is significantly reduced, and its calculation efficiency is increased. In addition, the present model is simple and better reflects the real situation of the vehicle–pavement interaction. After validating the present model, it is used in the vehicle–pavement interaction dynamic analysis during a long-distance travel. The calculation results obtained from the reduced-plate model transmission method are in good agreement with the MSM results for an entire plate, and the calculation time of the reduced-plate model transmission method is only one-third of that using the MSM for the entire plate. The calculation results further show that road roughness has a significant influence on the vehicle–pavement interaction dynamics in different situations. The Young's modulus and thickness of the base course layer also have a leading influence on pavement dynamic responses in the vehicle-pavement interaction system, where the variation of these parameters can lead to excessive responses of the pavement structure.

A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis.

Biomedical data analysis is essential in current diagnosis, treatment, and patient condition monitoring. The large volumes of data that characterize this area require simple but accurate and fast methods of intellectual analysis to improve the level of medical services. Existing machine learning (ML) methods require many resources (time, memory, energy) when processing large datasets. Or they demonstrate a level of accuracy that is insufficient for solving a specific application task. In this paper, we developed a new ensemble model of increased accuracy for solving approximation problems of large biomedical data sets. The model is based on cascading of the ML methods and response surface linearization principles. In addition, we used Ito decomposition as a means of nonlinearly expanding the inputs at each level of the model. As weak learners, Support Vector Regression (SVR) with linear kernel was used due to many significant advantages demonstrated by this method among the existing ones. The training and application procedures of the developed SVR-based cascade model are described, and a flow chart of its implementation is presented. The modeling was carried out on a real-world tabular set of biomedical data of a large volume. The task of predicting the heart rate of individuals was solved, which provides the possibility of determining the level of human stress, and is an essential indicator in various applied fields. The optimal parameters of the SVR-based cascade model operating were selected experimentally. The authors shown that the developed model provides more than 20 times higher accuracy (according to Mean Squared Error (MSE)), as well as a significant reduction in the duration of the training procedure compared to the existing method, which provided the highest accuracy of work among those considered.

Validation of method for analysis of glucose in food additives

The dangers of consuming various food and drink products containing substances with a potential risk to human health are the subject of increased interest. There is a drive among manufacturers and the scientific community to ensure safety in the use of various foods and beverages with proven and/or suspected links to one or another disease state. Fast and reliable methods of analysis are increasingly sought for the purpose of establishing the type and concentrations of the contained substances and elements, which methods support the implementation of strict control of the quantitatively regulated levels of various indicators, including main components and impurities. The current work presents a validation procedure of an iodometric method for determining the content of glucose as the main component of three types of nutritional supplements. To evaluate the method, results for the analytical characteristics specificity, repeatability, accuracy and linearity are presented and analyzed.

An ACA-BM-SBM for 2D acoustic sensitivity analysis

<abstract> <p>In this paper, we present a novel computational approach (named ACA-BM-SBM) for the calculation of 2D acoustic sensitivity by combining the Burton-Miller-type singular boundary method (BM-SBM) with the adaptive cross-approximation (ACA) algorithm. The BM-SBM circumvents the source singularities of the fundamental solutions by introducing the origin intensity factors, and it eliminates the fictitious frequency problem in external acoustic fields by introducing the Burton-Miller formula. As a semi-analysis meshless method, the BM-SBM can accurately solve the external acoustic problem governed by the Helmholtz equation. Nevertheless, the computational inefficiency introduced by the dense coefficient matrix renders this method suboptimal, particularly for large-scale simulations. As the number of nodes increases, the computation time and store memory increase dramatically. ACA is a purely algebraic method based on hierarchical matrices which can be used to partition the coefficient matrix step by step. By employing ACA, the BM-SBM can be effectively accelerated, and this results in less computation time, as well as fewer memory requirements. Numerical experiments, including Dirichlet and Neumann boundary conditions, illustrate that the proposed approach is an accurate, efficient and fast numerical method for acoustic sensitivity analysis.</p> </abstract>

Construction of Fluorescence Sensing Platform on the Basis of Molybdenum Disulfide Nanosheet for the Detection of AFB<sub>1</sub>

Purpose: Aflatoxin B1 is the most common mycotoxin in cereal crops; it is of stronger toxicity and has a carcinogenic effect. In recent years, a series of fluorescence sensors constructed on the basis of MoS2NS fluorescence quenching property have become a research hotspot. Therefore, we can construct a fast and simple analysis method with high specificity to detect AFB1 by utilizing MoS2NS, which can be effectively applied to food safety monitoring and clinical diagnosis. Method: In the current research, a fluorescence biosensor is developed on the basis of a new type of two-dimensional nano-material namely MoS2NS applied for the detection of AFB1. The fluorescence of Apt@AFB1 can be quickly quenched by MoS2NS through the fluorescence resonance energy transfer (FRET). When the target molecule AFB1 exists, after the specificity binding between AFB1 and aptamer, the Apt@AFB1 loses its single stranded structure and is away from MoS2NS, and the fluorescence of Apt®AFB1 cannot be quenched effectively. Such sensing signals can be used to achieve the sensitive detection of AFB1. Result: With this new method, under the optimized conditions, the AFB1 is analyzed in the MoS2NS/Apt®AFB1 sensing platform. Within the dynamic range of 0.2 - 25 ng/mL, the sensing platform expresses a good linear response to the level of AFB1 with the R2 = 0.9964 and LOD as 90 pg/mL. This method is applied to detect the actual serum samples and soybean milk with the recovery rate of 93.10% - 107.23% and 95.15% - 102.60% separately, and it can be used in the quantitative detection under the interference of other mycotoxins in a relatively accurate way. Conclusion: It is proved that this new detection method can be used as a potential biosensor platform for the detection of AFB1. This detection method features several advantages such as specificity, rapidness and low costs, which can meet the requirement of trace detection in clinical detection and food safety.

Development of fast analytical method for the detection and quantification of Moroccan picholine extra virgin olive oil adulteration using MIR spectroscopy and chemometrics tools

In this study, the adulteration of Moroccan Picholine extra virgin olive oil with Arbequina virgin olive oil was monitored using the Fourier transform mid-infrared (FT-MIR) spectroscopy technique and chemometrics methodologies. To discriminate between olive oil that has been adulterated and unadulterated, principal component analysis (PCA) was utilized for qualitative analysis. We created the best calibration models for quantitative analysis using principal component regression (PCR) and partial least-squares regression (PLS). The first three principal components account for 95% of the overall variability, according to PCA analysis. PCA allows for the classification of the dataset into two groups: adulterated and unadulterated Moroccan Picholine olive oil. The application of the PLS and PCR calibration models for the quantification of adulteration demonstrates high-performance capabilities, as indicated by high values of correlation coefficients R2 greater than 0.999 and 0.995 and lower values of root mean square error (RMSE) less than 0.767 and 2.16 using PLS and PCR, respectively. According to our results, FT-MIR spectroscopy combined with chemometrics approaches can be used successfully as a simple, quick, and non-destructive method for the quantification and discrimination of adulterated olive oil.

Electromagnetic Analysis of Periodic Iris-Fed Patch Antenna Array Using Fast and Efficient Hybrid Analytical Approach

Planar antenna arrays are renowned for their high directivity and ease of implementation, while offering the possibility of controlling the radiation pattern. However, the study of the electromagnetic aspect taking into account the coupling between the array’s elements requires a memory space and a considerable calculation time. To overcome these drawbacks, a fast and rigorous full-wave numerical method for the electromagnetic analysis of a unidimensional periodic iris-fed patch antenna array has been proposed. The spatial electromagnetic calculation of a periodic structure of finite dimensions is reduced to a spectral calculation carried out on a single unit reference cell by applying periodicity conditions to it. The modeling of a unit cell is based on the MOM-GEC flexible formalism, which reduces the resolution of the electromagnetic problem to a simple manipulation of equivalent circuits. Firstly, a validation of the numerical approach took place through the verification of the boundary conditions applied to the current distribution. Next, the mutual coupling between the elements of the antenna array was calculated. Results were validated with those obtained with HFSS commercial software and they yield accurate and efficient solutions. Furthermore, a significant reduction of both memory storage requirement and computational time was observed.

Characterization of D-amino acids in colostral, transitional, and mature preterm human milk.

D-Amino acids are regulatory molecules that affect biological processes. Therefore, being able to accurately detect and quantify these compounds is important for understanding their impact on nutrition and health. There is a paucity of information regarding D-amino acids in human milk. We developed a fast method for simultaneous analysis of amino acid enantiomers in human milk using liquid chromatography with tandem mass spectrometry. The method enables the separation of 41 amino acids without chemical derivatization. Our results revealed that human milk from mothers of preterm infants contains concentrations of D-amino acids that range from 0.5 to 45% that of their L-counterparts and that levels of most D-amino acids decrease as the milk production matures. Moreover, we found that Holder pasteurization of milk does not cause racemization of L-amino acids. To our knowledge, this is the first study to describe percentages of D-amino acid levels in human milk; changes in D-amino acid concentration as the milk matures; and the effect of Holder pasteurization on D- and L-amino acid concentrations in human milk.

Application of Microwave Transmission Sensors for Water Cut Metering under Varying Salinity Conditions: Device, Algorithm and Uncertainty Analysis.

The measurement of water cut in crude oil is an essential procedure in petroleum production and it is desirable to obtain these data through an automatic and real-time method. Microwave sensors can be used for the task, and they are safe, robust and can cover the whole water cut range. However, they are relatively susceptible to the water conductivity and temperature, and the algorithms for addressing these problems are still rare in the literature. In this paper, a microwave transmission sensor that can measure the water cut under varying salinity conditions is proposed, and the algorithm for solving the water cut and salinity simultaneously with the measured amplitude and phase is described in detail. Experiments under different water cut and salinity conditions are conducted, and the results are used to verify the model and algorithm. Finally, a simplified and fast method for uncertainty analysis is proposed and applied to the iteration algorithm under test conditions. It can be concluded that accuracy higher than 95% in the water cut measurements can be expected under the 0~100% water cut range, and an error of about 10% in the water conductivity is achievable under water-continuous flow conditions. The uncertainty analysis shows that the calculated water cut and salinity results are negatively correlated, and the water salinity uncertainty tends to be larger than the water cut uncertainty. When the water salinity is high, the water cut uncertainty tends to be high whereas the water salinity uncertainty tends to be low.

Detection of the Adulteration of Motor Oil by Laser Induced Fluorescence Spectroscopy and Chemometric Techniques.

Petroleum products are the target of fraudulent practices due to their high commercial value. The aim of this study is to provide a new analysis system to assess motor oil adulteration. For this purpose, Laser Induced Fluorescence (LIF) spectroscopy was exploited coupled with chemometric tools to detectmotor oil adulteration by three types of cheap motor oils. Principal Component Analysis (PCA) was able to distinguish samples in three groups according to the type of adulterant. Besides, Partial Least Squares Regression (PLSR) was exploited to determine the percentage of adulteration. The best model was obtained with a regression coefficient of 0.96, Root Mean Square Error ofPrediction (RMSEP) of 2.83, Standard Error of Prediction (SEP) of 2.83 and Bias of 0.40. The main results of this work provide new analysis system using the combination of LIF spectroscopy combined to PCA and PLS as an efficient and fast method for motor oil analysis.

Rapid detection of milk adulteration using Raman spectroscopy and statistical modelling

Food adulteration has become a concern for consumers and food safety authorities. Milk is a commune adulterated food product, like melamine adulteration, which resulted in devastating effects, especially on young children. Because of the current fast paste economy, it is essential to develop equally fast analysis methods to ensure reliable and sensitive results quickly with little to no sample preparation. For that purpose, a Raman method was developed and Partial least squares regression (PLS) was applied in order to develop a model for adulterated goat milk detection. Minitab 17 software was used for the statistical modeling of data. Validation matrices were constructed using unadulterated goat milk and goat milk adulterated with cow milk in different proportions (0-50%). The prediction model had a correlation coefficient of 99.8 %.

Oscillation characteristics and trajectory stability region analysis method of hierarchical control microgrids

To address the broadband oscillation problem caused by isomerized power electronic devices in microgrids, an oscillation characteristic and stability region analysis method was proposed for hierarchical control structure microgrids based on the dynamic time-varying fast and slow scale analysis method. From the perspective of control, the controller was combined with the main circuit, and a nonlinear dynamic model of the microgrid hierarchical control grid-connected inverter was established. Then, the dynamic time-varying fast and slow scale analysis method was used to obtain the Hopf bifurcation oscillation trajectory. Furthermore, and combining with three-dimensional and Hopf bifurcation, considering the correlation between the main circuit parameters and controller parameters as the starting point, the trajectory instability region analysis method is proposed. Finally, the instability mechanism of the hierarchical control structure microgrid is revealed using the above methods. These methods provide a theoretical basis for the correct selection of the main circuit structure parameters and controller parameters of the microgrid.

Development of a Transient Synchronization Analysis Tool for Line-Start PM Motors

With more stringent IEC energy efficiency standards, electrical machine industry increasingly focuses on new motor technologies. Amongst others, the line-start permanent magnet synchronous machine (LSPMSM) is considered as an attractive alternative to induction machine, especially for low power and fixed-speed applications. However, the design of LSPMSMs is rather complex as both steady-state and transient synchronization performances need to be considered. The synchronization capability determination of a LSPMSM design usually relies on time-consuming transient finite-element simulations, which is impractical for use in an iterative design optimization process. This paper compares and evaluates various existing analytical synchronization analysis methods in an attempt to identify most suitable equations and methods for fast synchronization analysis. Using the selected methods, a software tool is developed that can seamlessly work with ANSYS Electronics Desktop to perform rapid transient synchronization analysis. Given its ability to quickly determine the critical inertia factor of a LSPMSM design, the software tool is further adapted for use in a highly iterative, multi-objective design optimization procedure. It shows that the developed software tool can be successfully used in the design of LSPMSMs.

Shock Train Leading Edge Detection Method under the Condition of Steady Back Pressure and Fluctuating Back Pressure

In this paper, the shock train in the isolator is taken as the research object, and the method of detecting the leading edge position of the shock train during self-excited oscillation and forced oscillation is studied by conducting the wind tunnel test. A steady-state throttling system and a fluctuating throttling system are designed to apply the required steady-state and fluctuating back pressure downstream of the isolator. Then, the four methods of pressure ratio method, pressure difference method, standard deviation method, and Fast Fourier Transform (FFT) analysis method are used to detect and study the position of the shock train leading edge under steady and fluctuating back pressure to verify their applicability. The results show that under the condition of steady-state back pressure, the four methods can get effective results, and the results are consistent. Under the condition of fluctuating back pressure, the pressure difference method could not give effective results, but the other three methods could give effective and consistent results. And the results of the standard deviation method and the Fast Fourier Transform analysis method are similar in image and principle.

Machine Learning‐Based Solution for Thermomechanical Analysis of MMIC Packaging

AbstractThermomechanical analysis of monolithic microwave integrated circuit (MMIC) packaging is essential to guarantee the reliability of radio frequency/microwave applications. However, a method for fast and accurate analysis of MMIC packaging structures has not been developed. Here, a machine learning (ML)‐based solution for thermomechanical analysis of MMIC packaging is demonstrated. This ML‐based solution analyzes temperature and thermal stresses considering key design parameters, including material properties, geometric characteristics, and thermal boundary conditions. Finite element simulation with the Monte Carlo method is utilized to prepare a large dataset for supervised learning and validation of the ML solution, and a laser‐assisted thermal experiment is conducted to verify the accuracy of the simulation. After data preparation, regression tree ensemble and artificial neural network (ANN) learning models are investigated. The results show that the ANN model accurately predicts the outcomes with extremely low computing time by analyzing the high‐dimensional dataset. Finally, the developed ML solution is deployed as a web application format for facile approaches. It is believed that this study will provide a guideline for developing ML‐based solutions in chip packaging design technology.