Non-destructive Analytical Method Research Articles

Temperature-Dependent Electrochemical Impedance Spectroscopy of Li/Li Symmetric Cells and Li/MnO2 Batteries

Understanding the electrochemical properties of Lithium metal is a crucial step for developing and optimizing rechargeable batteries with lithium metal anodes. During discharge, the lithium metal is oxidized, generating dissolved lithium cations. Transport of the ions to the electrolyte is a significant part of this process. To investigate this process, temperature-dependent EIS experiments are done to independently measure the resistance due to this transport and, eventually, the activation energy.Electrochemical Impedance Spectroscopy (EIS) is a non-destructive analysis method mainly employed for studying the characteristics of electrochemical energy storage systems. EIS can be invaluable in isolating and studying buried interfaces without physically separating individual components. The anodic process, specifically the charge transfer through the solid-electrolyte interface can be studied without any influence of the rest of the processes.In this study, we built and measured the Potentiostatic EIS of both symmetric (Li/Li) and batteries (Li/MnO2). Button size (CR 2032) cells were constructed with different battery-grade electrolytes. A symmetric cell can isolate the processes related to metallic lithium. In contrast, a battery type (with an anode and a cathode) can differentiate the effect of the cathode on the anode. A broad range of temperatures (15ᵒC to 65ᵒC) was achieved with a Peltier-powered, homemade climate chamber. The limiting factors were selected according to the freezing and boiling temperatures of the electrolyte systems. Cycling of symmetric cells and discharging of batteries were performed, and the cells were measured after cycling/discharging.The evaluation of the spectra as a function of temperature and states-of-charge can give a comprehensive understanding of metallic Li and the oxidation process. The resistance modeling the transport as well as the capacitance values were investigated as functions of temperature. The change in resistance values at different temperatures resulted in not only Arrhenius behavior but also regions where Arrhenius behavior is violated.Interpretation of the temperature dependence provides a detailed understanding of the underlying electrochemical phenomena involving the metallic lithium anode. The effect of electrolytes on Li+ conductance can also be interpreted from the achieved results.

Art and technique of Cuojinyin: Two belt hooks from luoyang of eastern zhou dynasty

Cuojinyin is an inlay technique for delicately decorating the surface of bronze and iron wares. It takes advantage of the ductility of gold and silver to perfectly embed them into prepared grooves on the surface of bronze and iron wares without traces of using adhesives. This paper presents preliminary analyses of two belt hooks with cuojinyin decoration excavated from Luoyang, the capital of Eastern Zhou, dated to the Warring States Period. Non-destructive analytical methods including Ultra-Depth Three-Dimensional Microscope and energy dispersive X-ray fluorescence spectrometer (XRF) were conducted to obtain analytical data and reconstruct the manufacturing process. Several interesting technical features were observed such as the secondary casting of tin–lead alloy, the elemental composition of which is totally different from the bronze surface, and the chasing marks inside the casted grooves for gold and silver inlay. The aesthetic orientation and technical details of cuojinyin not only indicate inspirations from the nomadic world and the long distance interaction across the Eurasia, but also reflect the long developed local tradition in ancient China laid in bronze mold casting technology and turquoise inlay.

Assessment of extra virgin olive oil quality by miniaturized near infrared instruments in a rapid and non-destructive procedure

Food fraud in olive oil is a major concern for consumers and authorities due to the health risks and economic impacts. Common frauds include blending with other cheaper non-olive oils, or misleading labelling. The main issue is that legislation and methods presently used in routine laboratories are not always up to date with current fraudulent practices, making detection difficult, so new analytical methods development is required.This study focuses on developing an affordable and non-destructive analysis method based on NIR spectroscopy and chemometrics for EVOO quality assessment, specifically by monitoring 7 parameters of interest in EVOO measured by official methods and used to develop calibrations through NIR data. For this, two NIR low-cost portable instruments were employed, studied in-depth and compared with a NIR benchtop instrument. Calibration results enabled detection of atypical olive oils and excellent accuracy, especially for palmitic and oleic acid predictions, demonstrating the potential of the instruments.

Rapid and nondestructive identification of adulterate capsules by NIR spectroscopy combined with chemometrics

This study aims to develop a rapid and non-destructive method to identify counterfeit and substandard drugs, addressing the critical need for better quality control in drug production. According to the reasons for counterfeit products in actual production, the commonly used solid preparation excipients such as HPMC, MCC, Mg-St and Pregelatinized Starch, as well as three chemical drugs with similar efficacy to Guizhi-Fuling (GZFL) Capsule as adulterants, including Aspirin, Ibuprofen and Sinomenine Hydrochloride were selected and designed as adulteration samples with different levels of adulteration. NIR spectra were collected in a non-invasive mode and analyzed by one-class classification methods. The feasibility of using Near-infrared (NIR) spectroscopy as a detection method to qualitatively identify adulterated samples was explored at three packaging levels of powder, intact capsules and capsules in PVC. The differences between the samples were analyzed by NIR spectra comparison, cluster analysis and principal component analysis. The performance of SVM, OCPLS and DD-SIMCA models in dealing with the authentication of genuine and counterfeit products was established and compared. The results show that the spectra contain sample information and the adulterated samples could be discriminated correctly by established models. Moreover, applying appropriate spectral preprocessing methods can further improve the model's performance. In addition, a PLS regression model was developed to predict the adulteration levels of the three packing level samples, which yielded satisfactory results. This study highlights the potential of NIR spectroscopy combined with Chemometrics as a rapid and non-destructive testing analysis method to accurately identify counterfeit and substandard drugs, thereby ensuring drug quality.

Determination of potential absorbance bands of fumonisin B1 in methanol with near infrared spectroscopy

The contamination of maize, a major staple food in South Africa, with fumonisin B1 (FB1), has become a major food safety concern. The regulation of this mycotoxin is extremely important and requires efficient detection methods. Near infrared (NIR) spectroscopy has gained widespread interest as a rapid and non-destructive mycotoxin analysis method. The purpose of this study was, therefore, to determine the NIR absorbance bands of FB1. The spectra of 30 FB1 solutions, constituted in methanol, as well as 30 methanol-only samples were recorded in the spectral range of 1000–2500 nm (10,000 – 4000 cm−1). The data was pre-processed with multiplicative scatter correction (MSC) and a partial least squares discriminant analysis (PLS-DA) model was computed. The variable importance in projection (VIP) scores and selectivity ratio (SR) values were used for wavelength selection. A new PLS-DA model was computed with 454 chosen wavelengths and the regression vector of this model was investigated to further identify and remove irrelevant wavelengths. The final model was computed with 150 wavelengths and nine latent variables (LVs) and obtained a classification accuracy of 100% for both the calibration and external validation sets. By investigating the regression vector of the final PLS-DA model, potential FB1 absorbance bands were identified at 1446 nm, 1453 nm, 1891 nm, 2036 nm, 2046 nm, 2148 nm, 2224 nm, 2262 nm and 2273 nm. This study was therefore able to identify the previously unknown NIR absorbance bands of FB1 at 100 ppm.

Synthesis of Axinyssa digitata extract directed hybrid nanoflower and investigation of its antimicrobial activity.

First time in this study, the antibacterial effects of Axinyssa digitata sponge extracts and Axinyssa digitata-based cupper hybrid nanoflowers (Cu hNFs) were evaluated. Herein, hybrid nanoflowers (Cu hNFs) were produced by combining Axinyssa digitata sponge extract with Cu2+ ions in Phosphate-buffered saline (PBS) (at pH 7.4) at room temperature for three days using green synthesis method. The shape and size of hNFs were evaluated using scanning electron microscope (SEM) images. Energy dispersive X-ray spectroscopy (EDX) mapping was used to determine the presence of Cu metals and other components. X-ray diffraction (XRD) is a non-destructive analysis method that was used to determine of the crystallographic properties of materials and the phases they contain. Fourier-transform infrared spectroscopy (FT-IR) peaks were used to discuss the presence of functional groups that played a key role in the synthesis. The Cu-hNFs had antimicrobial activity against selected microorganisms. This research is expected to provide knowledge on hNFs synthesis and antimicrobial activity application investigations using Axinyssa digitata rather than biomolecules obtained through costly and time-consuming methods.

CHEMICAL COMPOSITION OF RUSSIAN COPPER COINS OF THE IMPERIAL PERIOD

The article is devoted to the study of the chemical composition and assessment of the quality of the metal of Russian copper coins of the 18th-early 20th centuries. Coins of this period are common in museum and private collections. However, the problem of metal purity and features of its composition is practically not covered. These issues are of interest for understanding the development processes and assessing the state of the Russian non-ferrous metallurgy in this period and, in particular, the Ural copper industry, since this industry has been and still remains one of the leading ones in the Ural and a significant part of Russian copper coins were minted in Yekaterinburg. To determine the chemical composition of coin metal, a non-destructive method of metal analysis, X-ray fluorescence analysis, was used in the work. At the same time, the problem of the presence of a corrosive layer on the surface of coins, the removal of which is not always possible for reasons of the preservation of the artifact, is solved. A method for using X-ray fluorescence analysis is proposed taking into account the phase composition of the surface layer. It is shown that the purity of copper remained above 99% in the studied sample of coins, which characterizes the high level of the Russian copper industry in the 18th-19th centuries. The presence of natural micro impurities of various composition and concentration in copper indicates the use of metal from various sources. The paper shows the effectiveness of using X-ray fluorescence analysis to determine the chemical composition of the metal of coins, which contributes to a more complete attribution of artifacts. The approaches developed in this work can be useful in the study of artifacts based on copper alloys.

Interpretation of Hyperspectral Shortwave Infrared Core Scanning Data Using SEM-Based Automated Mineralogy: A Machine Learning Approach

Understanding the mineralogy and geochemistry of the subsurface is key when assessing and exploring for mineral deposits. To achieve this goal, rapid acquisition and accurate interpretation of drill core data are essential. Hyperspectral shortwave infrared imaging is a rapid and non-destructive analytical method widely used in the minerals industry to map minerals with diagnostic features in core samples. In this paper, we present an automated method to interpret hyperspectral shortwave infrared data on drill core to decipher major felsic rock-forming minerals using supervised machine learning techniques for processing, masking, and extracting mineralogical and textural information. This study utilizes a co-registered training dataset that integrates hyperspectral data with quantitative scanning electron microscopy data instead of spectrum matching using a spectral library. Our methodology overcomes previous limitations in hyperspectral data interpretation for the full mineralogy (i.e., quartz and feldspar) caused by the need to identify spectral features of minerals; in particular, it detects the presence of minerals that are considered invisible in traditional shortwave infrared hyperspectral analysis.

Effect of pore structure on compressive strength and permeability of planting recycled concrete

The relationship between strength, permeability, and pore structure of planting recycled concrete (PRC) was investigated using industrial computed tomography non-destructive testing technology and image analysis methods. An increase in the aggregate particle size and porosity increased the pore size and fractal dimension, and the relationship between the compressive strength and porosity conformed to the Ryshkewitch model. The fractal dimension had a weaker influence on compressive strength and permeability than did aperture size and porosity. Although vegetation growth reduced the compressive strength and permeability coefficient of PRC, the rainwater infiltration and vegetation compatibility of PRC was confirmed.

Hidden Macroscopic Degradation Behavior in Rechargeable Lithium–Oxygen Batteries under Lean Electrolyte and High Areal Capacity Conditions

As lithium–oxygen batteries (LOBs) possess extremely high theoretical energy densities that exceed those of lithium-ion batteries, they are regarded as promising candidates for portable energy applications, including as power sources for next-generation electrical devices. However, physical degradation factors affecting the prolonged cycle of the LOBs, such as electrode volume changes and electrolyte displacement, have been rarely explored thus far, owing to the technical difficulty of applying non-destructive analytical methods to LOBs during cycling. In the present study, we employed ex situ X-ray computerized tomography (XCT), in situ internal pressure monitoring, and in situ confocal microscopy to study the physical states of the electrodes and the electrolyte in high energy density LOBs. The analysis results reveal that large irreversible volume changes occurred in both the positive electrode and the negative electrode of the LOB cells. Furthermore, it was also confirmed that the electrolyte was displaced from the pores of the positive electrode associated with the formation of Li2O2 in these pores during the discharge process. As these results present new fundamental insights into the physical behavior of LOBs during cycling conditions, the methodologies demonstrated here are therefore effective means to gain a new understanding of complex physical processes previously unexamined in LOBs, which shed light on the future design strategies of LOBs with high energy density and long cycle life.

Deciphering the Color Origin of Pink Conch Pearl Using Nondestructive Spectroscopies and DFT Calculations

Conch pearl is a calcareous concretion produced by the Queen conch mollusk and is of extremely high economic value. Its attractive pink color has remained a mystery for hundreds of years. This difficulty limits the development of cultivation and preservation technology of conch pearl. Due to its super-high value, it is generally impossible to use destructive chemical analysis methods. Here, various nondestructive spectroscopic analysis methods and theoretical calculations were used to reveal the color origin of pink conch pearl. The experimental Raman and UV-Vis spectroscopies were used to characterize the color and pigments. We further discuss the pigments in conch pearl by comparing the Raman spectral characteristics and pigments of the red coral. Based on the characteristics of the experimental spectra, we speculate that the carotenoids color the conch pearl. The theoretical Raman and UV-Vis spectra of all-trans polyene and carotenoids were simulated using DFT calculations. According to the results, we proposed that the pigments in pink conch pearl are polyenes containing 11 shortened C–C bonds with multiplicities > 1.5, which may be demethylated carotenoids.

Analysis of rain-deposited dust on polysulfone membranes using proton-induced X-ray emission spectroscopy

Proton-induced X-ray emission spectroscopy (PIXE) is a long-established method of nondestructive elemental analysis for environmental samples. While a standard method of recovery of solids from rainwater involves filtration onto polysulfone media, PIXE analysis has not typically been used on such substrates. We describe methodologies of the University of North Texas Ion Beam Laboratory for PIXE analysis of dust in Texas rainwater on polysulfone membranes from the National Atmospheric Deposition Program (NADP). PIXE analyses utilized a 2 MeV proton beam of 1 × 1 mm2 spot size with beam currents of 1–5nA. A 100 µm Al absorber attenuated X-rays produced by S native to the polysulfone and minimized pileup, allowing use of higher beam currents at the expense of detecting elements of Z < 22. Uniformity of dust deposition on the membranes was evaluated by utilizing Fe concentration as a dust tracer. Areas of uniform particulate deposition with comparable results to averages across the entire membrane were located, allowing reduced acquisition time. GeoPIXE software was used for quantitative analysis, and 18 elements (Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, As, Se, Br, Rb, Sr, Y, Zr, Hg and Pb) were investigated for a collaborative project. Average minimum detection limits (MDL) were ≤ 10 ng/cm2 for 21 > Z greater than 36 and 10–30 ng/cm2 for 36 < Z < 83. Polysulfone is shown to be an appropriate substrate for PIXE analysis of environmental samples, especially when quantification of low-Z elements is not a priority.

The synergic approach between machine learning, chemometrics, and NIR hyperspectral imagery for a real-time, reliable, and accurate prediction of mass loss in cement samples

Alternative and non-destructive analytical methods that predict analyte concentration accurately and immediately in a specific matrix are becoming vital in the analytical chemistry domain. Here, a new innovative and rapid method of predicting mass loss of cement samples based on a combination of Machine Learning (ML) and the emerging technique called Hyperspectral Imaging (HSI) is presented. The method has proved its reliability and accuracy by providing a predictive ML model, with satisfactory best validation scores recorded using partial least squared regression, with a reported ratio of performance to inter-quartile distance and root mean squared error of 12,89 and 0.337, respectively. Moreover, the possibility of optimizing and boosting the performance of the method by optimizing the predictive model performance has been suggested. Therefore, a features selection approach was conducted to disqualify non-relevant wavelengths and stress only relevant ones in order to make them the only contributors to a final optimized model. The best selected features subset was composed of 28 wavelengths out of 121, found by applying genetic algorithm combined to partial least squares regression as a feature selection method, on spectra preprocessed consecutively by the first-order savitzky-golay derivative calculated with 7-point quadratic SG filter, and multiplicative scatter correction method. The overall results show the possibility of combining HSI and ML for fast monitoring of water content in cement samples.

A non-destructive X-ray fluorescence method of analysis of formalin fixed-paraffin embedded biopsied samples for biomarkers for breast and colon cancer

In this work we present a methodology for the non-destructive elemental determination of formalin-fixed paraffin-embedded (FFPE) human tissue samples based on the Fundamental Parameters method for the quantification of micro Energy Dispersive X Ray Fluorescence (micro-EDXRF) area scans.This methodology intended to overcome two major constraints in the analysis of paraffin embedded tissue samples – retrieval of optimal region of analysis of the tissue within the paraffin block and the determination of the dark matrix composition of the biopsied sample. This way, an image treatment algorithm, based on R® tool to select the regions of the micro-EDXRF area scans was developed. Also, different dark matrix compositions were evaluated using varying combinations of H, C, N and O until the most accurate matrix was found: 8% H, 15% C, 1% N and 60% O for breast FFPE samples and 8% H, 23% C, 2% N and 55% O for colon. The developed methodology was applied to paired normal-tumour samples of breast and colon biopsied tissues in order to gauge potential elemental biomarkers for carcinogenesis in these tissues. The obtained results showed distinctive biomarkers for breast and for colon: there was a significant increase of P, S, K and Fe in both tissues, while a significant increase of Ca an Zn concentrations was also determined for breast tumour samples.

Stone materials of the late twelfth-century incrustation works of Esztergom

The paper deals with the material analysis of four emblematic stone fragments from Saint Adalbert Cathedral and the Royal Palace of Esztergom from King Béla III’s era. All of the four examined objects (two fragments from the Porta speciosa and two throne arm-rests) have incrustations with red limestone basement and other colourful stone pieces. As red limestone is a well-known material in Hungarian art history with a rich historiography, the paper focuses on the findings of the analyses of other stone materials of the incrustations. The research contains several non-destructive analytical methods, such as relative humidity measurement, macroscopic and microscopic photography and X-ray fluorescence with lithologic description. Besides the comparative analysis of the stone materials, archive documents, the current state and the impacts of subsequent restorations of the four stone artefacts were also studied.

Influence of lithium plating on lithium-ion battery aging at high temperature

Lithium plating is an important issue for lithium-ion battery safety and cycle life that can be caused by cycle at low temperature. In this study, we investigated battery aging over an extended low-temperature cycle and at high temperature after the low-temperature cycle. Nondestructive and destructive analysis methods were used to study battery electrochemical dynamics and changes to the materials, and the results indicated that battery aging over the extended low-temperature cycle could be divided into two stages. The mechanism of the first stage is that of “dead lithium,” which causes significant capacity fading, whereas that of the second stage is solid electrolyte interphase (SEI) growth. It was also found that plated lithium accelerates battery aging at high temperature; part of it is intercalated into anode, another part of it reacts with the electrolyte, thus consuming the plated lithium. Thus, electrolyte decomposition and SEI growth are the primary aging mechanisms at high temperature after low-temperature cycling. The plated lithium content and charge/discharge rate affected plated lithium consumption during the high-temperature cycle. Plated lithium was not depleted for the battery with significant amounts of plated lithium or upon charging with high current and discharging with both high and low current when the state of health (SOH) decreased to 70%. The factors affecting battery aging ranked in order of importance are charge current > plated lithium content > high temperature > discharge current.

Synthesized Evaluation of Reinforced Concrete Bridge Defects, Their Non-Destructive Inspection and Analysis Methods: A Systematic Review and Bibliometric Analysis of the Past Three Decades

Defects are essential indicators to gauge the structural integrity and safety of reinforced concrete bridges. Non-destructive inspection has been pervasively explored over the last three decades to localize and characterize surface and subsurface anomalies in reinforced concrete bridges. In addition, different fuzzy set theory-based, computer vision and artificial intelligence algorithms were leveraged to analyze the data garnered from non-destructive evaluation techniques. In light of the foregoing, this research paper presents a mixed review method that encompasses both bibliometric and systematic analyses of the state-of-the-art work pertinent to the assessment of reinforced concrete bridge defects using non-destructive techniques (CBD_NDT). In this context, this study reviews the literature of journal articles and book chapters indexed in Scopus and Web of Science databases from 1991 to the end of September 2022. To this end, 505 core peer-reviewed journal articles and book chapters are compiled for evaluation after conducting forward and backward snowballing alongside removing irrelevant papers. This research study then exploits both VOSVIEWER and Bibiometrix R Package for the purpose of network visualization and scientometric mapping of the appended research studies. Thereafter, this paper carries out a multifaceted systematic review analysis of the identified literature covering tackled bridge defects, used non-destructive techniques, data processing methods, public datasets, key findings and future research directions. The present study is expected to assist practitioners and policymakers to conceive and synthesize existing research and development bodies, and future trends in the domain of the assessment of bridge defects using non-destructive techniques. It can also aid in raising awareness of the importance of defect management in bridge maintenance systems.

Au nanocakes as a SERS sensor for on site and ultrafast detection of dioxins

Dioxin is a highly toxic and carcinogenic pollutant created during industrial production and waste incineration. Pollutant monitoring has made extensive use of surface-enhanced Raman spectroscopy (SERS) as a quick, non-destructive, sensitive, and affordable analytical method. However, the fabrication of a SERS substrate with ultrahigh sensitivity is a challenging task, the detection cost is expensive. Currently, these are the challenges faced by SERS technique for the detection of dioxin pollutants. In this paper, we present Au nanocakes (Au NCs) as an easy-to-build SERS substrate for sensitive SERS detection of dioxins in real samples. Based on three-dimensional finite-difference time-domain (3D-FDTD) calculations, the enhancement factor of the substrate following aggregation is approximately 1010. Using a portable Raman spectrometer, the limit of detection for dioxins such as 1-chloro-dibenzo-p-dioxin (1-CDD), 2,8-dichloro-dibenzo-p-dioxin (2,8-DCDD), and 2,3,7-trichloro-dibenzo-p-dioxin (2,3,7-TrCDD) in clean water reached as low as 5, 5, and 10 ng/mL, respectively. As a real-world application, the same toxic pollutants were detected in sewage water samples. Our findings could lead to the development of novel SERS-based sensors for the rapid detection of dioxins in real-world scenarios. Moreover, a portable Raman spectrometer is used to detect the pollutants, which is easy to use and inexpensive.

A rapid and nondestructive approach for forensic identification of novel psychoactive substances using shifted‐excitation Raman difference spectroscopy and machine learning

Rapid drug detection is vital for combating drug abuse and inhibiting transmission, and the emergence of various novel psychoactive substances has placed higher demands on field testing. To establish a rapid, non-destructive and accurate analytical and classification method for testing novel psychoactive substances, SERDS was used to detect 37 NPS including fentanyl, amphetamine and synthetic cannabinoid under the experimental conditions of laser light source (785&785.5) nm. SERDS was also used in conjunction with machine learning to find the best classification prediction model. In this study,the characteristic peaks of 37 NPS analogues were extracted, and the characteristic peaks were attributed to the structures of the substances. We compared the classification effects of SVM,KNN,Ensemble classifier, Neural Network,Tree,Naive Bayes,Discriminant Analysis, and provided three Hyperparametric optimization methods. Finally, the cross validation accuracy of SVM under BOA optimization is 97.3%, which can well distinguish three families of samples. The ability of SERDS to resist strong fluorescence interference makes it a powerful tool for on-site analysis of NPS. In this study, we have given 37 unreported standard Raman peaks of NPS and 3 seized samples, which can be used as a reference. Shift-Excitation Differential Raman spectroscopy combined with machine learning can effectively provide solutions for customs, health care, on-site police, Large event security, and trace evidence inspection.

Emerging nondestructive techniques to quantify the textural properties of food: A state-of-art review.

Texture is an important sensory attribute that drives consumer acceptance of any food material. In recent times consumers' demand for high-quality food urges food industries to provide food with consistent textural properties. However, texture measurement not just requires a trained sensory panel but also a considerable amount of time and effort. On the flip side, human observation could be subjective hence repeatability of the result may not be ensured and/or relied on. Contrary to that, objective methods for texture measurement are reliable and consistent, but are not suitable for in-line application and also destructive in nature. The mentioned crisis has made industries opt for nondestructive texture analysis techniques. In the past decade, considerable research has been carried out on nondestructive texture analysis methods such as micro-deformation, and acoustic and optical techniques, showing feasibility for in-line applications. The current review focuses on the working principles and most recent applications of nondestructive techniques for texture analysis of food products. Moreover, a detailed review of contact and noncontact-type texture measurement has been presented in this article. The literature survey is concluded with future research aspects and challenges involved in the commercialization of the nondestructive texture analysis techniques.