Non-destructive Analysis Research Articles

Ultra-broadband emission from Mg7Ga2GeO12:Ni2+,Cr3+ phosphor spanning the NIR I–III regions for spectroscopy applications

Ultra-broadband near-infrared (NIR) lighting sources covering NIR Ⅰ to III regions are pivotal components for NIR spectroscopy devices. However, achieving ultra-broadband and efficient emission covering the entire NIR Ⅰ to III regions remains a significant challenge. To address this, we selected Mg7Ga2GeO12 as host material to realize ultra-broadband NIR emission. Density functional theory calculations on formation energies confirmed the feasibility of doping both Ni2+ and Cr3+ within Mg7Ga2GeO12. Band structure calculations reveal a significantly reduced band gap upon the doping of Ni2+. Following that, we synthesized Mg7Ga2GeO12:Ni2+ through conventional solid-state-reaction method. This phosphor exhibits broadband NIR emission with a full-width-at-half-maximum of 377 nm in the range of NIR II ∼ III (1100–2100 nm). To further enhance the emission efficiency, we co-doped Mg7Ga2GeO12:Ni2+ phosphor with Cr3+. This co-doping strategy not only dramatically enhances the Ni2+ emission intensity, but also extends the lower limit of emission band to 600 nm. However, a spectral gap around 1150 nm remains between Cr3+ and Ni2+ emission centers. To bridge this spectral gap, we employed LiScSiO4:Cr3+ phosphor alongside Mg7Ga2GeO12:Ni2+,Cr3+ phosphor to fabricate a phosphor converted LED (pc-LED) with ultra-broadband NIR emission covering entire NIR Ⅰ to III regions. By utilizing this pc-LED as the lighting source for NIR spectral detection, multiple organic functional group signals can be identified simultaneously in NIR II and III bands from 1000 to 2100 nm. The performance of such an NIR pc-LED not only confirms the application potential of Mg7Ga2GeO12:Ni2+,Cr3+ phosphor in non-destructive spectral analysis but also underscores the feasibility of combining two phosphors to achieve ultra-broadband NIR pc-LEDs, rendering them suitable to be lighting sources for portable spectrometers.

Development of a New Non-Destructive Analysis Method in Cultural Heritage with Artificial Intelligence

Cultural assets are all movable and immovable assets that have been the subject of social life in historical periods, have unique scientific and cultural value, and are located above ground, underground or underwater. Today, the fact that most of the analyses conducted to understand the technologies of these assets require sampling and that non-destructive methods that allow analysis without taking samples are costly is a problem for cultural heritage workers. In this study, which was prepared to find solutions to national and international problems, it is aimed to develop a non-destructive, cost-minimizing and easy-to-use analysis method. Since this article aimed to develop methodology, the materials were prepared for preliminary research purposes. Therefore, it was limited to four primary colors. These four primary colors were red and yellow ochre, green earth, Egyptian blue and ultramarine blue. These pigments were used with different binders. The produced paints were photographed in natural and artificial light at different light intensities and brought to a 256 × 256 pixel size, and then trained on support vector machine, convolutional neural network, densely connected convolutional network, residual network 50 and visual geometry group 19 models. It was asked whether the trained VGG19 model could classify the paints used in archaeological and artistic works analyzed with instrumental methods in the literature with their real identities. As a result of the test, the model was able to classify paints in artworks from photographs non-destructively with a 99% success rate, similar to the result of the McNemar test.

Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

Exploring Carolingian and Romanesque cycles: a study of the detached wall paintings from the Church of St. Johann in Müstair

Twenty-six fragment of wall paintings detached from the upper register of the Carolingian and Romanesque cycles of the church of St. John in Müstair (Switzerland) have been part of the Swiss National Museum collection since the beginning of the twentieth century. As some of these objects were in a critical state of conservation, a research project was launched to assess and document their conditions, and to gain knowledge about painting materials and execution techniques, in view of planning a conservation project. In a first phase, non-destructive analyses were carried out, using handheld XRF and portable FTIR spectroscopy; followed by the targeted collection of samples for detailed analysis in the laboratory. In parallel, similar investigations were carried out on the imprints left on the walls of the church’s attic. The results allowed the identification of the pigment palette and the execution technique. Besides earth pigments such as ochres, a lime-based white, probably bianco di San Giovanni, and limited amounts of lapis lazuli were used in both painting cycles. For the Carolingian cycle, Egyptian blue and mixtures of several lead-based pigments were also identified. In several instances, the deterioration of lead-based pigments resulted in shades of grey to black severely modifying the original appearance of the wall paintings. Additionally, both cycles have suffered from pigment alteration due to fire. The obtained results, together with iconographic considerations, allowed for an extended interpretation of possible original colour palette.Graphical

Dose mapping of a 252Cf based non-destructive on-line elemental analysis device using Monte Carlo simulation and verification with experimental results

Environmental dose values are critical for human health in non-destructive analysis systems. The International Commission on Radiological Protection (ICRP) has defined specific limitations for permissible dose values. This study assesses the dose distribution of a prototype analysis device utilizing Californium-252 (252Cf) radioactive material through both Monte Carlo simulations and laboratory experiments. Conducted at the Istanbul Technical University (ITU) Energy Institute, the experimental investigation measured gamma-ray and neutron dose values surrounding the device. The objective was to compare dose maps generated by the Monte Carlo-based GEANT4 code with experimental data. Ten dose maps were created, and fundamental statistical analyses were performed. Results indicate that the dose values on the operator’s working surface of the prototype device are within ICRP limits, not exceeding 1 millisievert (mSv) annually, with an effective dose rate of 0.949±0.052 mSv per year. These findings underscore the device’s potential for safe and effective use in radiation safety and environmental dose assessment.

Burnt Cervidae bones and sedimentary environments at Luotuodun, Jiangsu Province, China: New insights for Neolithic human behavior

The Luotuodun archaeological site is located in Yixing City, Jiangsu Province, in the transition zone from the Yili Mountains to the coastal plain. It is of Neolithic Age and dates from approximately 7000 to 5000 years ago. A large number of animal skeleton fossils have been unearthed at the site, many of which, especially those belonging to species of Cervidae (possibly Sika, Cervis nippon), are characterized by the presence of black residue on the surface. There are three types of residue, one is black film-like coating, which is suggestive of exposure to fire, one is also black, but crystalline in nature, while the other is grey-white, particulate deposit. With the aim of exploring the nature of these residues and their possible origins, we applied a number of analytical techniques, including Raman Spectroscopy (RS), Fourier Transform Infrared Spectroscopy (FTIR), Energy dispersive spectroscopy (EDS) and Electron Microscopy (EM) on six bone fossils belonging to different parts of Cervidae skeleton unearthed from Luotuodun to evaluate the possible influence of fire and/or depositional processes in their formation. The black film-like coating, widespread on the surface of these bones, is highly reflective and appears white under the Raman microscope. The RS peak indicates that the film-like substance is rich in carbonaceous (graphitic carbon) components, suggesting that the bones have been exposed to combustion. FTIR measurements on these bones show that the structure of hydroxyapatite carbonate (Ca10(PO4)6−x(CO3)x(OH)2) has changed, consistent with their exposure to very high temperatures (500–600 °C), and implying that they were burnt, either for cooking or ritual behavior by ancient humans. In the case of the crystallized black particles, these are primarily confined to localities on the surface of bone fractures. EDS results, as confirmed by the RS analysis, show that the black crystals are comprised of an iron-rich, phosphatic mineral, compatible with vivianite (Fe3(PO4)2·(H2O)8). The substantial vivianite content is consistent with the fossil bones being submerged and deposited in waterlogged soils. The results of EDS show that the composition of the grey-white particle is hydroxyapatite carbonate (Ca10(PO4)6−x(CO3)x(OH)2). Non-destructive geochemical analysis of the surface residues of these Luotuodan bones provides valuable evidence for reconstructing the taphonomy of the Cervidae bones, and reveals that they were utilized by ancient humans during the Luotuodun culture period in SE China.

Non-Destructive Analysis of Tetracycline Drugs by the Digital Colormetric Method Using a Smartphone and the Photometrix Pro® Software

Non-Destructive Analysis of Tetracycline Drugs by the Digital Colormetric Method Using a Smartphone and the Photometrix Pro® Software

Process analytical technology based quality assurance of API concentration and fiber diameter of electrospun amorphous solid dispersions

In this study, a novel quality assurance system was developed utilizing Process analytical technology (PAT) tools and artificial intelligence (AI). Our goal was to monitor the critical quality attributes (CQAs) like drug concentration, morphology and fiber diameter of electrospun amorphous solid dispersion (ASD) formulations with fast at-line techniques. Doxycycline-hyclate (DOX), a tetracycline-type antibiotic was used as a model drug with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) as the matrix excipient. The water-based formulations were electrospun with high-speed electrospinning (HSES). Raman and NIR sensors and machine vision-based color measurement techniques were employed to accurately determine the drug concentration. Given that morphology can influence the solubility of the drug, a convolutional neural network (CNN)-based AI model was developed to examine this property and detect manufacturing defects. Additionally, the diameter of electrospun fibrous samples was measured using camera images and a trained AI model, enabling rapid analysis of fiber diameter with results similar to that of scanning electron microscopy (SEM). These methods and models demonstrate potential in-line analytical tools, offering rapid, cheap and non-destructive analysis of ASD formulations.

Analytical Techniques for the Authenticity Evaluation of Chokeberry, Blackberry and Raspberry Fruit Wines: Exploring FT-MIR Analysis and Chemometrics

The modern analytical technique of Fourier-transform mid-infrared spectroscopy (FT-MIR) has found its place in routine wine quality control. It allows rapid and nondestructive analysis, with easy sample preparation and without the need for chemical pretreatment or expensive reagents. The objective of this research was to apply these advantages to fruit wines in order to create a tool for the authentication of fruit wines produced from different fruit species (chokeberry, blackberry, and raspberry). The aim of this work was to establish a chemometric model from FT-MIR spectra and to find a “fingerprint” of specific fruit wines, enabling the classification of fruit wines by plant species. Physicochemical analysis of 111 Croatian fruit wine samples (38 liqueur fruit wines and 73 fruit wines) revealed content levels of the following parameters: alcoholic strength (5.0–15.2% vol.), total dry extract (60.4–253.3 g/L), total sugars (1.2–229.9 g/L), pH (3.13–4.98), total acidity (4.2–18.3 g/L) and volatile acidity (0.2–1.5 g/L). For statistical data processing, spectral ranges between 926 and 1450 cm−1 and between 1801 and 2951 cm−1 were used. The first principal component (PC1) explained 70.4% of the observed variation, and the second component (PC2) explained 16.7%, clearly separating chokeberry fruit wines from blackberry and raspberry fruit wines. Soft Independent Modeling Class Analogy (SIMCA) was performed following the development of a PCA model showing that the chokeberry and blackberry wine samples form clearly separated clusters. Key discriminators for classifying chokeberry vs. blackberry wines were identified at 1157, 1304, and 1435 cm−1, demonstrating high discrimination power (DP 26, 17, and 14, respectively). FT-MIR spectroscopy, in combination with chemometric methods, has shown promising potential for the authenticity assessment of fruit wines.

Progressive degradation behavior and mechanism of lithium-ion batteries subjected to minor deformation damage

Minor deformation damage poses a concealed threat to battery performance and safety. This study delves into the progressive degradation behavior and mechanisms of lithium-ion batteries under minor deformation damage induced by out-of-plane compression. The effects of varying initial states of charge and loading speed on battery degradation are also analyzed. It has been observed that a deformation damage degree as low as 3.1 % can cause a significant transient capacity reduction up to 6.3 %. More transient capacity reduction can occur at higher initial state of charges and loading speed. Additionally, the investigation reveals that the capacity decay rate between the normal cell and cells experiencing deformation damage degree of <4.7 % is almost similar, with a maximum difference of merely 1.887 mAh/cycle and a minimum difference of 0.001 mAh/cycle, both observed over 3000 cycles. Non-destructive and destructive analysis methods are used to investigate degradation behaviors of the cells experiencing minor deformation dagame. It is found that such progressive degradation is primarily driven by the loss of active lithium ions, followed by the loss of cathode and anode active materials. It is anticipated that the findings will offer theoretical underpinnings for advancements in battery damage assessment, early failure prediction, and facilitation of loss adjustment and compensation by insurance agencies.

Validation of non-destructive morphology-based selection of cerebral cortical organoids by paired morphological and single-cell RNA-seq analyses.

Organoids, self-organized cell aggregates, contribute significantly to developing disease models and cell-based therapies. Organoid-to-organoid variations, however, are inevitable despite the use of the latest differentiation protocols. Here, we focused on the morphology of organoids formed in a cerebral organoid differentiation culture and assessed their cellular compositions by single-cell RNA sequencing analysis. The data revealed that organoids primarily composed of non-neuronal cells, such as those from the neural crest and choroid plexus, showed unique morphological features. Moreover, we demonstrate that non-destructive morphological analysis can accurately distinguish organoids composed of cerebral cortical tissues from other cerebral tissues, thus enhancing experimental accuracy and reliability to ensure the safety of cell-based therapies.

Comparative Analysis of XGB, CNN, and ResNet Models for Predicting Moisture Content in Porphyra yezoensis Using Near-Infrared Spectroscopy.

This study explored the performance and reliability of three predictive models-extreme gradient boosting (XGB), convolutional neural network (CNN), and residual neural network (ResNet)-for determining the moisture content in Porphyra yezoensis using near-infrared (NIR) spectroscopy. We meticulously selected 380 samples from various sources to ensure a comprehensive dataset, which was then divided into training (300 samples) and test sets (80 samples). The models were evaluated based on prediction accuracy and stability, employing genetic algorithms (GA) and partial least squares (PLS) for wavelength selection to enhance the interpretability of feature extraction outcomes. The results demonstrated that the XGB model excelled with a determination coefficient (R2) of 0.979, a root mean square error of prediction (RMSEP) of 0.004, and a high ratio of performance to deviation (RPD) of 4.849, outperforming both CNN and ResNet models. A Gaussian process regression (GPR) was employed for uncertainty assessment, reinforcing the reliability of our models. Considering the XGB model's high accuracy and stability, its implementation in industrial settings for quality assurance is recommended, particularly in the food industry where rapid and non-destructive moisture content analysis is essential. This approach facilitates a more efficient process for determining moisture content, thereby enhancing product quality and safety.

Analysis of buried ink elements in library artefacts using pulsed thermography

Pulsed thermography is a well-established method for the non-destructive analysis of cultural heritage items. This technique has been recently applied by the authors of this paper for reading written scraps, used for the making of bookbinding of ancient manuscripts, located between the end papers and the covers. The readability of the hidden text depends on several geometrical, optical and thermal parameters characterizing the typology of paper and the ink employed. To this end, a comprehensive mathematical model was developed by the authors for analyzing the influence of the various involved parameters. In particular, two indices were introduced, namely the signal contrast and the distortion index, used to quantitatively characterize the hidden text readability. Several numerical simulations are reported for assessing the dependence of the contrast and distortion from various parameters appearing in the model. Finally, a preliminary application to the analysis of original books is also presented.

Simultaneous Determination of Ethanol and Methanol in Wines Using FTIR and PLS Regression.

Accurate quantification of ethanol and methanol is essential for regulatory compliance and product quality assurance. Fourier Transform Infrared Spectroscopy (FTIR) offers rapid, non-destructive analysis with minimal sample preparation, making it a promising tool for wine analysis. In this exploratory study, the use of FTIR and PLS regression for the simultaneous quantification of ethanol and methanol in wine samples of 11 different Portuguese mono-varietal wines and different vintages deriving from the same winery in Lisbon was investigated. A model was developed, demonstrating the feasibility of FTIR and PLS regression for the simultaneous quantification of ethanol and methanol in wine samples through dedicated models; it showed good prediction capacity for ethanol determination but poorer performance for methanol quantification. The model could be reliable enough for quality control in wine production, but to improve its performance should be enhanced in the future with more samples from different origins, wine types, and a wider concentration range in the case of methanol.

A novel low-quality Raman hyperspectral image reconstruction method for corn component mapping

Raman imaging used to obtain spatial distribution information of chemical components on the sample surface has been widely applied in the nutritional assessment and quality control of cereal grains. However, obtaining high-quality Raman hyperspectral images of cereal grain based on point scanning is time-consuming due to the limitations of the instrumental imaging principle and Raman hyperspectral images acquired in a short time are low-quality with noise interference, limiting its practical application in the visualization of cereal grain composition. To rapidly and effectively achieve high-quality component distribution mappings of cereal grains based on Raman hyperspectral images, we propose a novel Swin Transformer-based method combining spatial-spectral denoising and image reconstruction (SwinSSID-IR) for low-quality Raman hyperspectral image reconstruction. The novel denoising network named SwinSSID innovatively introduces Swin Transformer blocks (STB) to effectively eliminate noise from raw low-quality Raman hyperspectral images of corn kernels. Next, the super-resolution network named SwinIR rapidly generates high-quality component distribution mappings of corn with exceptional clarity and detail from the denoised low-quality Raman hyperspectral image at characteristic peak. Experimental results show the competitiveness of the proposed method compared with other similar advanced methods and its effective application in non-destructive visual analysis of corn components.

Preface to the Special Issue “Non-destructive or On-site Analysis for Materials related to Steel Industry”

Preface to the Special Issue “Non-destructive or On-site Analysis for Materials related to Steel Industry”

High repetition-rate 0.5 Hz broadband neutron source driven by the Advanced Laser Light Source

Neutron beams are an essential tool to investigate material structure and perform nondestructive analysis, as they give unique access to element composition, thus ideally complementing density analysis allowed by standard x-rays investigation. Laser-driven neutron sources, though compact and cost-effective, currently have lower average flux than conventional neutron sources, due to the limited repetition rate of the lasers used so far. However, advancements in laser technology allow nowadays to address this challenge. Here, we report results obtained at the Advanced Laser Light Source characterizing stable production of broadband (0.1–2 MeV) neutrons produced at a high repetition rate (0.5 Hz). The interaction of laser pulses of 22 fs duration and 3.2 J on-target energy with 2-μm-thick tantalum targets produced protons in the Target Normal Sheath Acceleration (TNSA) regime up to 7.3 MeV. These protons were subsequently converted into neutrons by (p,n) reactions in lithium fluoride (LiF). Activation measurements and bubble detectors were used to characterize neutron emissions, with a neutron fluence of up to ∼1.4×105 neutrons/shot/sr and energies mainly between a few hundred of kilo-electron volt and 2 MeV. The total neutron yield was ∼5×105 neutrons/shot. This paves the way for numerous applications, e.g., in homeland security, materials science, or cultural heritage.

Going Inside a Historical Brazilian Diamond from the Spada Collection (19th Century)

The characterization of objects of historical and cultural interest represents a crucial topic, specifically when it regards gemstones. Actually, the advanced investigation of precious minerals of gemological interest requires exclusively non-destructive analyses which are also suitable for determining their provenance when it is unknown. In this study, a non-destructive analytical protocol, previously tested on diamonds for petrogenetic studies, has been applied to a natural diamond of very high historical and gemological value, donated in 1852 by Monsignor Lavinio de ‘Medici Spada to the Museum of Earth Sciences of Sapienza University (Rome). The analytical protocol used includes X-ray diffraction topography, micro-computed X-ray tomography, single-crystal micro-X-ray diffraction and Fourier-transform infrared spectroscopy. The results show the presence of dislocations originating from inclusions and a very low degree of plastic deformation. The aggregation states of its N impurities show that this diamond is type IaAB, while the inclusions consist of olivine (Fo92-93), suggesting a lithospheric origin. The historical references found in the catalogs of the Museum indicate only a Brazilian origin, without any reference to the mining district. The information acquired in this study, enhanced by document research on mining in Brazil since 1700, suggests that the diamond likely comes from the district of Diamantina, Mina Gerais, Brazil.

The Selection of Lettuce Seedlings for Transplanting in a Plant Factory by a Non-Destructive Estimation of Leaf Area and Fresh Weight

Selecting uniform and healthy seedlings is important to ensure that a certain level of production can be reliably achieved in a plant factory. The objectives of this study were to investigate the potential of non-destructive image analysis for predicting the leaf area and shoot fresh weight of lettuce and to determine the feasibility of using a simple image analysis to select robust seedlings that can produce a uniform and dependable yield of lettuce in a plant factory. To vary the range of the leaf area and shoot fresh weight of lettuce seedlings, we applied two- and three-day irrigation intervals during the period of seedling production and calculated the projected canopy size (PCS) from the top-view images of the lettuce seedlings, although there were no significant growth differences between the irrigation regimes. A high correlation was identified between the PCS and shoot fresh weight for the lettuce seedlings during the period of seedling production, with a coefficient of determination exceeding 0.8. Therefore, the lettuce seedlings were classified into four grades (A–D) based on their PCS values calculated at transplanting. In the early stages of cultivation after transplanting, there were differences in the lettuce growth among the four grades; however, at the harvest (28 days after transplanting), there was no significant difference in the lettuce yield between grades A–C, with the exception of grade D. The lettuce seedlings in grades A–C exhibited the anticipated yield (150 g/plant) at the harvest time. In the correlation between the PCS and leaf area or the shoot fresh weight of lettuce during the cultivation period after transplanting and the entire cultivation period, the R2 values were higher than 0.9, confirming that PCS can be used to predict lettuce growth with greater accuracy. In conclusion, we demonstrated that the PCS calculation from the top-view images, a straightforward image analysis technique, can be employed to non-destructively and accurately predict lettuce leaf area and shoot fresh weight, and the seedlings with the potential to yield above a certain level after transplanting can be objectively and accurately selected based on PCS.

Texture measurements of archaeological objects at STRESS-SPEC neutron diffractometer

The main advantage of neutron diffraction over X-ray diffraction, arises from the fact that the interaction of neutrons with material is relatively weak and not related to the number of electrons, and consequently the penetration depth of neutrons is about 102-103 larger than that of laboratory X-ray diffraction. This is particular essential for the non-destructive texture analysis of archaeological objects as no additional surface treatments of the samples (e.g. polishing) are necessary. STRESS-SPEC at MLZ is designed as a state of the art multi-purpose diffractometer for strain and texture analysis. Besides the optimized high neutron flux the available large variability in gauge volume definition systems together with the robotic sample handling option offer high flexibility for bulk or gradient texture measurements. Since 2014, local and bulk textures of iron and gold artefacts collected by Bavarian State Archaeological Collection (Munich, Germany) have been thoroughly investigated at STRESS-SPEC. Results showed that heat treatment of iron artefacts at high temperatures can re-orientate the inner crystallites. In the gold foil artefacts, the texture represented by the measured pole figures shows a high symmetry – the so-called Cube component, which is commonly found in annealed fcc materials. For comparison, laboratory samples were produced by rolling, flat hammering, and pin / round hammering and also measured in order to elucidate possible manufacturing and processing routes. In turned out that both rolling and pin / round hammering followed by a high temperature annealing can produce similar pole figures to those of the gold artefacts foils.