Non-destructive Characterization Method Research Articles

Intraoperative characterization of cardiac tissue: the potential of light scattering spectroscopy.

Damage to the cardiac conduction system remains one of the most significant risks associated with surgical interventions to correct congenital heart disease. This work demonstrates how light-scattering spectroscopy (LSS) can be used to non-destructively characterize cardiac tissue regions. To present an approach for associating tissue composition information with location-specific LSS data and further evaluate an LSS and machine learning system as a method for non-destructive tissue characterization. A custom LSS probe was used to gather spectral data from locations across 14 excised human pediatric nodal tissue samples (8 sinus nodes, 6 atrioventricular nodes). The LSS spectra were used to train linear and neural-network-based regressor models to predict tissue composition characteristics derived from the 3D models. Nodal tissue region nuclear densities were reported. A linear model trained to regress nuclear density from spectra achieved a prediction r-squared of 0.64 and a concordance correlation coefficient of 0.78. These methods build on previous studies suggesting that LSS measurements combined with machine learning signal processing can provide clinically relevant cardiac tissue composition.

A nondestructive method using X-ray micro computed tomography for variation analysis of bamboo nodes’ vascular bundle among species

Bamboo node is a specialized structure that has great effect on both bamboo growth, physical-mechanical properties, and its utilization. Compact structure and particularly high hardness make bamboo node very difficult for slice specimens preparation. In this study, in order to explore the differences in anatomical characteristics of bamboo nodes' vascular bundles among species, 3D structures of bamboo nodes in four bamboo species (Bambusa emeiensis, Phyllostachys edulis, Pleioblastus amarus, and Bambusa rigida) were reconstructed by the micro-CT technique. The characteristics of vascular bundles were comparatively analyzed based on the CT images. The results showed that the vascular bundle morphology of Pleioblastus amarus and Bambusa rigida changed significantly from node to internode. The areas of fiber cells in the middle part along the radial direction of bamboo culm wall has the largest area (0.204 mm2), followed by the inner part (0.151 mm2) and the outer part (0.072 mm2). The area of transport tissue in the inner part along the radial direction of bamboo culm wall has the largest area (0.046 mm2), followed by the middle part (0.028 mm2) and the outer part (0.002 mm2). The bending vascular bundles in bamboo node can be divided into thick and thin types. The average thickness of the thin bending vascular bundles among the four bamboo species was 125 μm, with the thinnest value of 109 μm. While, the average thickness of thick bending vascular bundles was 265 μm, with the thickest value of 335 μm. The number proportion of thick vascular bundles was 60.8%, 42.5%, 32.9%, and 10.7% for Bambusa rigida, Bambusa emeiensis, Pleioblastus amarus, and Bambusa rigida. This study set up a nondestructive and efficient method for 3D anatomical structure characterization of bamboo node and the variation analysis in bamboo nodes’ vascular bundles.

Online state of health estimation for lithium-ion batteries based on gene expression programming

Lithium-ion battery is a kind of energy storage devices with complex internal reaction and many factors affecting its performance. Accurate prediction of its SOH (State of Health) is of great significance to prolong its service life and improve safety performance. However, the current prediction for SOH has the difficulties of selecting health factors and using data-driven methods with opaque mechanisms. In this paper, a data-driven model will be established with the capacity change during aging of lithium-ion batteries as a health indicator to realize precise prediction of capacity degradation. Firstly, the feature parameters were extracted and analyzed from the battery cyclic aging test dataset. Two in-situ nondestructive characterization methods, incremental capacity analysis curve and differential thermal voltammetry curve, were utilized to resolve the evolution paths of the feature parameters. After that, a data-driven battery capacity degradation estimation is realized based on the GEP (Gene Expression Programming) algorithm, the performance is compared with the existing vehicle-end and cloud-end models, and a higher-accuracy SOH stacking model is developed with an increase of less than 1 ms in computational time. The results indicated that the GEP model proposed in this paper has obvious advantages in terms of physical explain-ability, computational efficiency and robustness.

Nondestructive geochemical characterization of fossil hominin taphonomy and burial history

To date, only three Homo habilis specimens have been discovered that have associated craniodental and postcranial elements, providing a limited fossil record of the ontogeny and morphology of early members of the genus Homo. Recently, a nearly complete dentition, likely attributable to H. habilis, was discovered and excavated from early Pleistocene-age fluvial-lacustrine sediments of the upper Burgi Member of the Koobi Fora Formation at site F25787 in Area 13, near Ileret, Kenya. On the surface less than 15 m away, at site F25966, postcranial elements were found, which, if from the same individual as the nearby dentition, would represent the fourth associated craniodental and postcranial assemblage of this species. We developed a geochemical taphonomic history of these ca. 2 Ma hominin fossils using nondestructive X-ray based microanalytical tools (synchrotron and benchtop X-ray fluorescence chemical imaging and micro- and nano-computed tomography volumetric reconstruction), bulk analyses of sediments and paleosols at the excavation sites, and sedimentologic and stratigraphic observations. We integrate the chemical and physical taphonomic histories to test whether teeth (excavated in situ) and postcranial bones (eroded onto the outcrop surface) derive from a single individual. Minor differences in taphonomic history are attributable to the different biomineral properties of the dental and osseous components and to differences in physical damage during early post-mortem scavenging, dispersal, and burial in adjacent depositional settings. Microscale geochemical mapping enabled the temporal ordination of chemical and physical events in the specimens’ chemical taphonomic histories. Specifically, authigenic Fe- and K-bearing clays and Y, U, and Sr uptake occurred in post-burial fractures in bones and were also incorporated pervasively throughout dentin in teeth. Barite mineralization occurred along the latest fractures in both materials, and as a coating on tooth roots. The stratigraphic, taphonomic, and geochemical evidence supports the interpretation that the hominin fossils represent a single individual. Successful application of these nondestructive sample characterization methods demonstrates capabilities for thorough interrogation of the taphonomic histories of other potential hominin fossil associations, enabling more robust and accurate palaeontologic constraints using those relationships.

Nanoscale Infrared Spectroscopic Characterization of Extended Defects in 4H-Silicon Carbide.

Extended defects in wide-bandgap semiconductors have been widely investigated using techniques providing either spectroscopic or microscopic information. Nano-Fourier transform infrared spectroscopy (nano-FTIR) is a nondestructive characterization method combining FTIR with nanoscale spatial resolution (∼20 nm) and topographic information. Here, we demonstrate the capability of nano-FTIR for the characterization of extended defects in semiconductors by investigating an in-grown stacking fault (IGSF) present in a 4H-SiC epitaxial layer. We observe a local spectral shift of the mid-infrared near-field response, consistent with the identification of the defect stacking order as 3C-SiC (cubic) from comparative simulations based on the finite dipole model (FDM). This 3C-SiC IGSF contrasts with the more typical 8H-SiC IGSFs reported previously and is exemplary in showing that nanoscale spectroscopy with nano-FTIR can provide new insights into the properties of extended defects, the understanding of which is crucial for mitigating deleterious effects of such defects in alternative semiconductor materials and devices.

Ultrasonic elastography for the prevention of breast implant rupture: Detection of an increase with stiffness over implantation time

Breast implants are widely used after breast cancer resection and must be changed regularly to avoid a rupture. To date, there are no quantitative criteria to help this decision. The mechanical evolution of the gels and membranes of the implants is still underinvestigated, although it can lead to early rupture. In this study, 35 breast explants having been implanted in patients for up to 17 years were characterized by ex vivo measurements of their mechanical properties. Using Acoustic Radiation Force Impulse (ARFI) ultrasound elastography, an imaging method for non-destructive mechanical characterization, an increase in the stiffness of the explants has been observed. This increase was correlated with the implantation duration, primarily after 8 years of implantation. With an increase of the shear modulus of up to a factor of nearly 3, the loss of flexibility of the implants is likely to lead to a significant increase of their risk of rupture. A complementary analysis of the gel from the explants by mass spectrometry imaging (MSI) and liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) confirms the presence of metabolites of cholesterol originating from the breast tissues, which most likely crossed the membrane of the implants and most likely degrades the gel. By observing the consequences of the physical–chemical mechanisms at work within patients, this study shows that ultrasound elastography could be used in vivoas a quantitative indicator of the risk of breast implant rupture and help diagnose their replacement.

Combined Operando Investigations Reveal Correlation between Formation Parameters and Transport Mechanisms in Solid Electrolyte Interphases of Lithium-Ion Battery Anodes

Despite its thickness being only in the nanometer range, the solid electrolyte interphase (SEI) of lithium-ion battery anodes has proven to be a crucial component, contributing critically to the battery’s longevity and long-term rate capability. This is due to the SEI’s passivating properties, protecting the battery electrolyte from further decomposition during operation while maintaining a good conductivity for lithium ions to be intercalated into the active anode material. Notwithstanding prolonged scientific efforts, a reliable SEI characterization is still very challenging, due to its fragile and inherently unstable nature which can easily lead to the introduction of artifacts in its evaluation, when dried, rinsed, or exposed to ambient atmospheric conditions. Thus, the relation between SEI formation parameters and the resulting morphological, compositional, and electrochemical properties is still poorly understood.To overcome the limitations of often-employed ex-situ methods, we developed an interlocked combination of several non-destructive operando characterization methods consisting of atomic force microscopy (AFM), electrochemical quartz-crystal microbalance (EQCM) and electrochemical impedance spectroscopy (EIS) to provide a plethora of information on the SEI’s morphological evolution, mass and thickness development and its viscoelastic and electrochemical properties like charge transfer resistance. All this information is gathered in the same electrochemical cell during the SEI formation on carbon model electrodes in typical LIB electrolytes containing 1 M LiPF6 in various carbonate mixtures.The combination of AFM, EQCM and EIS data gives us the opportunity to correlate the different growth modes during formation and resulting compositions of the SEI with its dominant transport mechanisms. Additional redox probe experiments help to further distinguish the transport parameters for lithium ions, solvent molecules, and redox molecules. A better understanding of these correlations allows battery researchers to find optimized SEI formation procedures and SEI forming additives, which reduce the formation duration during battery production and can thus contribute to lowering costs of lithium-ion batteries.

Extraction of polymeric hemicelluloses from okoumé sapwood by steam explosion: Use of non-destructive characterization methods

The hemicelluloses, which represent an important family of polysaccharides constituting ≈ 20–30% of the plant wall are currently one of the most poorly valorized fractions of the biomass. Extracted in polymeric form, hemicelluloses could be used for innovative material applications, replacing fossil materials. Five cumulative pretreatments involving bleaching and/or steam explosion (SE) and/or alkaline extraction processes have been studied for polymeric hemicelluloses extraction from Okoumé sapwood. Morphological analysis of the cell wall of untreated and steam exploded wood was performed by optical and fluorescence microscopy after staining. Near infrared spectroscopy combined with multivariate calibration modeling were also performed for evaluating hemicellulose extraction. Hemicelluloses extracts were characterized by HPEAC/PAD ion chromatography, size exclusion chromatography (SEC), and high-performance liquid chromatography (HPLC), FTIR and HSQC NMR spectroscopy analyses. Alkali extraction of bleached wood led to high yields (≈ 19%) of high molecular weight hemicelluloses (Mw ≈ 65 kDa) while neutral SE pretreatment allowed the recovery of highly acetylated polymers with a low degree of branching. A bleaching step prior to SE increased accessibility to hemicelluloses extraction but weakened the pulp and led to the removal of micro-particles of wood during the SE.

Nondestructive volumetric optical analysis of corroded copper oxidation using 1700nm swept-source optical coherence microscopy.

This study presents a novel nondestructive analysis method for precise characterization of corroded copper oxidation using optical coherence microscopy (OCM). By exploiting the partial light transmission through metallic oxide layers, we employed a specialized OCM system with a wavelength of 1700nm and enhanced the analysis accuracy compared to conventional optical coherence tomography (OCT). The developed OCM system featured a numerical aperture (NA) of 0.15, providing improved surface profiling and higher lateral resolution than OCT. we developed a peak-finding algorithm to accurately determine the thickness of the copper oxide layer from the acquired interference data with zero padding. Our method was validated by comparing the measured thickness profiles with those obtained from scanning electron microscope (SEM) images of corroded metals. The copper oxidation specimens were prepared after heat treatment for 1, 2, 4, and 8 h in an alumina tube furnace at a temperature of 900 °C to find the correlation between the OCM thickness measurement. Additionally, the acquired enface 3D images enabled the identification of local corrosion distribution within a 4 mm × 4 mm area. The en-face mapping images are utilized to analyze the uniformity of the metal oxidation process across the imaging area of the copper oxidation specimens. With an increase in heat treatment time, the median value of the thickness histogram for the copper oxide within the area consistently remained around 10 µm. However, the thickness variation ranged from -2 µm to 5 µm. This indicates that as the heat treatment time progresses, the thickness of the copper oxide becomes more non-uniform. Our technique holds great potential for nondestructive and noncontact detection of metal corrosion and assessment of corrosion rates in various industrial applications. Future research efforts could focus on expanding the application of OCM to different metals and exploring its commercialization prospects for practical implementation in diverse industries.

Bonding wire characterization using non-destructive X-ray imaging

Counterfeiting in the semiconductor industry poses a significant threat to the economy and can lead to lower performance and reliability of electronic devices. X-ray technology in this respect is commonly used for non-destructive testing to identify physical defects and anomalies in integrated circuits. However, for accurate material identification, destructive methods such as spectroscopic techniques including X-ray diffraction and X-ray fluorescence are typically required. In this work, a fast and non-destructive method is presented for material characterization of bonding wires in ICs and other SMD components by measuring X-ray absorption profiles. The results demonstrated that the X-ray imaging technique can effectively distinguish between bonding wires made of gold, silver, copper, and aluminum. Moreover, the thickness of these materials can also be detected. This technique exhibits high potential for utilization as a simple and quick method for non-destructive material characterization in the industrial environment.

Machine learning based dual flat-spherical indentation approach for rough metallic surfaces

Instrumented indentation test (IIT) is regarded as one of the non-destructive test methods for mechanical characterization and safety assessment of engineering components. However, indentation test is very sensitive to surface topography i.e surface roughness, requiring specimens with ideally flat and smooth surface for producing reliable and reproducible test results. Recently, a dual flat-spherical indentation (DFSI) technique was proposed to extract material properties directly from rough metallic surface via indentation tests. Integration of DFSI technique with machine learning (ML) approach can facilitate reliable and fast characterization of rough metallic surfaces. Here, ML models including artificial neural network (ANN) and physics-informed artificial neural network (PI-ANN) are established, and then trained by using a database of indentation parameters generated via DFSI simulations. Spherical indentation techniques available in the literature are used to calculate the physics-informed loss in the PI-ANN model. The performances of trained ANN and PI-ANN models are compared, and an optimal ML model with hyperparameters is suggested based on validation study; PI-ANN model with sigmoid activation function shows a better performance than the ANN model and baseline model. The model performances are evaluated by performing DFSI experiments with SM45C and SS304. The applicability of ML based-DFSI method is demonstrated for extracting the mechanical properties from the rough surface of general metals.

Evaluation of near-infrared spectroscopy as a contactless method for health monitoring of resin-based coating materials applied to concrete surfaces.

The surfaces of concrete structures are often coated with protective materials to minimize corrosion and weathering-based deterioration. Therefore, it is important to monitor the aging of the coating materials and their overall condition to extend the service lifetime of the structure effectively. Near-infrared spectroscopy (NIRS) is a contactless, nondestructive, rapid, and convenient method for material characterization; therefore, it is useful for onsite inspection of coating materials. Hence, in this study, we attempt to determine whether NIRS can be used for simple inspection for health monitoring of organic resin-based coating materials. In addition to identifying different severities of peeling damage, we characterize the ultraviolet-induced deterioration of coating materials with different thicknesses using diffuse reflection spectra acquired in the near-infrared wavelength region. For independent comparison with the NIR spectra, the state of the coating materials on the mortar specimens was analyzed using a combination of Fourier-transform infrared spectroscopy and scanning electron microscopy, while the state of the underlying mortar specimens was analyzed using permeability and salt-water immersion tests. The results confirm that the NIRS could detect the degradation of coating materials at early stages of deterioration before their permeability had been affected. NIRS offers the possibility of intermittent monitoring of coating deterioration. In addition, because the NIR spectrometer is portable, it can help in inspecting high-rise areas and areas that are difficult to reach. Therefore, we believe that NIRS is a simple, safe, and inexpensive method for inspection of surface coating materials.

Design of experiment and machine learning inform on the 3D printing of hydrogels for biomedical applications

In the biomedical field, 3D printing has the potential to deliver on some of the promises of personalized therapy, notably by enabling point-of-care fabrication of medical devices, dosage forms and bioimplants. To achieve this full potential, a better understanding of the 3D printing processes is necessary, and non-destructive characterization methods must be developed. This study proposes methodologies to optimize the 3D printing parameters for soft material extrusion. We hypothesize that combining image processing with design of experiment (DoE) analyses and machine learning could help obtaining useful information from a quality-by-design perspective. Herein, we investigated the impact of three critical process parameters (printing speed, printing pressure and infill percentage) on three critical quality attributes (gel weight, total surface area and heterogeneity) monitored with a non-destructive methodology. DoE and machine learning were combined to obtain information on the process. This work paves the way for a rational approach to optimize 3D printing parameters in the biomedical field.

Fast Twist Angle Mapping of Bilayer Graphene Using Spectroscopic Ellipsometric Contrast Microscopy.

Twisted bilayer graphene provides an ideal solid-state model to explore correlated material properties and opportunities for a variety of optoelectronic applications, but reliable, fast characterization of the twist angle remains a challenge. Here we introduce spectroscopic ellipsometric contrast microscopy (SECM) as a tool for mapping twist angle disorder in optically resonant twisted bilayer graphene. We optimize the ellipsometric angles to enhance the image contrast based on measured and calculated reflection coefficients of incident light. The optical resonances associated with van Hove singularities correlate well to Raman and angle-resolved photoelectron emission spectroscopy, confirming the accuracy of SECM. The results highlight the advantages of SECM, which proves to be a fast, nondestructive method for characterization of twisted bilayer graphene over large areas, unlocking process, material, and device screening and cross-correlative measurement potential for bilayer and multilayer materials.

Dynamically Rotating Magnetic Levitation to Characterize the Spatial Density Heterogeneity of Materials.

Magnetic levitation (MagLev) is a promising technology for density-based analysis and manipulation of nonmagnetic materials. One major limitation is that extant MagLev methods are based on the static balance of gravitational-magnetic forces, thereby leading to an inability to resolve interior differences in density. Here a new strategy called "dynamically rotating MagLev" is proposed, which combines centrifugal force and nonlinear magnetic force to amplify the interior differences in density. The design of the nonlinear magnetic force in tandem with centrifugal force supports the regulation of stable equilibriums, enabling different homogeneous objects to reach distinguishable equilibrium orientations. Without reducing the magnetic susceptibility, the dynamically rotating MagLev system can lead to a relatively large change in orientation angle (∆ψ>50°) for the heterogeneous parts with small inclusions (volume fraction VF = 2.08%). The rich equilibrium states of levitating objects invoke the concept of levitation stability, which is employed, for the first time, to characterize the spatial density heterogeneity of objects. Exploiting the tunable nonlinear levitation behaviors of objects provides a new paradigm for developing operationally simple, nondestructive density heterogeneity characterization methods. Such methods have tremendous potential in applications related to sorting, orienting, and assembling objects in three dimensions.

Developing Quantitative Nondestructive Characterization of Nanomaterials: A Case Study on Sequential Infiltration Synthesis of Block Copolymers.

The sequential infiltration synthesis (SIS) of inorganic materials in nanostructured block copolymer templates has rapidly progressed in the last few years to develop functional nanomaterials with controllable properties. To assist this rapid evolution, expanding the capabilities of nondestructive methods for quantitative characterization of the materials properties is required. In this paper, we characterize the SIS process on three model polymers with different infiltration profiles through ex situ quantification by reference-free grazing incidence X-ray fluorescence. More qualitative depth distribution results were validated by means of X-ray photoelectron spectroscopy and scanning transmission electron microscopy combined with energy-dispersive X-ray spectroscopy.

High-throughput computation of Raman spectra from first principles

Raman spectroscopy is a widely-used non-destructive material characterization method, which provides information about the vibrational modes of the material and therefore of its atomic structure and chemical composition. Interpretation of the spectra requires comparison to known references and to this end, experimental databases of spectra have been collected. Reference Raman spectra could also be simulated using atomistic first-principles methods but these are computationally demanding and thus the existing databases of computational Raman spectra are fairly small. In this work, we developed an optimized workflow to calculate the Raman spectra efficiently and taking full advantage of the phonon properties found in existing material databases. The workflow was benchmarked and validated by comparison to experiments and previous computational methods for select technologically relevant material systems. Using the workflow, we performed high-throughput calculations for a large set of materials (5099) belonging to many different material classes, and collected the results to a database. Finally, the contents of database are analyzed and the calculated spectra are shown to agree well with the experimental ones.

Visualizing changes of metabolites during iron deficiency chlorosis in field-grown pear leaves using micro-Raman spectral imaging.

Owing to iron chlorosis, pear trees are some of the most severely impacted by iron deficiency, and they suffer significant losses every year. While it is possible to determine the iron content of leaves using laboratory-standard analytical techniques, the sampling and analysis process is time-consuming and labor-intensive, and it does not quickly and accurately identify the physiological state of iron-deficient leaves. Therefore, it is crucial to find a precise and quick visualization approach for metabolites linked to leaf iron to comprehend the mechanism of iron deficiency and create management strategies for pear-tree planting. In this paper, we propose a micro-Raman spectral imaging method for non-destructive, rapid, and precise visual characterization of iron-deficiency-related metabolites in pear leaves. According to our findings, iron deficiency significantly decreased the Raman peak intensities of chlorophylls and lipids in leaves. The spatial distributions of chlorophylls and lipids in the leaves changed significantly as the symptoms of iron insufficiency worsened. The technique offers a new, prospective tool for rapid recognition of iron deficiency in pear trees because it is capable of visual detection of plant physiological metabolites induced by iron deficiency.

Assessment of 3D micro-computed tomography for PV interconnection technology development

X-ray (micro-)computed tomography is a non-destructive characterization method using photons to obtain multiple radiographic projections, which generates a 3D volume rendering of a scanned object after reconstruction. It is recently used in the research and development of photovoltaic modules to investigate smaller volumes in a PV module, for example, detection of cavities in solder joints and visualization of module volumes up to connection ribbon sizes. This work shows that current advanced devices are able to scan larger volumes, using a single-cell 20 × 20 × 0.5 cm 3 module with back-contact multi-ribbon interconnection technology as an example. The generated 3D volume renderings show the interconnection structures and give information on ribbon-metallization interconnections. Next to this, also the solder reflow volume is visible, indicating that the technology has great potential to be used in further interconnection technology development. Finally, a destructive cross section analysis using scanning electron microscopy is performed; it has a higher resolution and can differentiate elements with similar photon absorption coefficients, thus enabling microstructure and intermetallic compound analysis. The findings show the potential for micro-computed tomography as a non-destructive and relatively fast visualization method and how it can give complementary information to other characterization techniques for photovoltaic interconnection research. • X-ray computed tomography is a non-destructive visual inspection technique. • Scanning electron microscopy and x-ray tomography are complementary. • X-ray tomography can aid PV interconnection technology developments.

Preparation and refractive index characterization of chalcogenide glasses with gradient refractive index

AbstractInfrared gradient refractive index (GRIN) lenses have great application value and potential in multispectral imaging systems. This study reports various chalcogenide axial GRIN glasses prepared using the hot‐pressing diffusion method. It is worth noting that the S4–S60 GRIN sample has a difference in refractive index (RI) Δn of greater than 0.3 and a diffusion depth of about 5 mm, which is the deepest diffusion depth reported in chalcogenide glass to date. In addition, the linear portion in the profile of the GRIN sample has a RI difference of 0.15 and a thickness of 1.2 mm. The effects of the temperature, concentration difference, and diffusion time on the sample diffusion process are discussed. The dispersion properties of the GRIN samples were further calculated, providing a new option for correcting chromatic aberrations in optical systems. In addition, a method for the indirect nondestructive characterization of sample RI using the Raman intensity ratio is proposed, and the reliability of the method is verified by practical experiments, which is convenient for the subsequent measurement of the GRIN profile.