Raman Imaging Research Articles

Multimodal Microscopy Characterisation of 2D Materials

Spatial characterisation of transition metal dichalcogenides (TMDCs) is crucial for understanding their optoelectronic properties and optimising film growth conditions. In this work, we present a multimodal microscopy platform combining conventional widefield, Raman, photoluminescence and second harmonic generation imaging for the characterisation of 2D materials.The platform was used to characterise CVD grown WSe2 crystals. Raman imaging identified the presence of both monolayer and multilayer WSe2 through a change in the intensity and position of the E12g / A1g phonon modes. Photoluminescence imaging confirmed the presence of monolayer WSe2 with emission at 780 nm and identified two distinct multilayer regions through changes in the photoluminescence wavelength and intensity. Lastly, second harmonic generation imaging under femtosecond laser excitation was used to obtain the relative growth orientation of the three identified domains in the crystal.Figure 1 highlights Raman imaging of WSe2. (a) Intensity of the E12g / A1g (250 cm-1) Raman band, (b) Peak position of the E12g / A1g (250 cm-1) Raman band, (c) least squares spectral matching revealing three distinct Raman spectral areas, (d) Averaged Raman spectra from areas A, B and C. Figure 1

High Temperature Oxidation of Zircaloy-4 Under Conditions Simulating a Loss of Cooling Accident in Spent Fuel Pools Examined with Raman Imaging and 18o Tracer Techniques

Since the Fukushima accident, an increased attention is continuously paid to the vulnerability of the Spent Fuel Pools (SFPs). In SFPs, for different reasons, degradation of the fuel rods induced by oxidation in air-steam mixtures after dewatering is a major safety concern: (i) the cladding material is the only barrier left against fission product dissemination, (ii) the heat released by the oxidation reactions can overcome the residual decay heat of the fuel and may become the main driving force to the accident escalation, (iii) hydrogen production is a strong concern because of the presence of steam, (iv) presence of air in the atmosphere is known to be an aggravating factor because of the “catalytic” role of nitrogen on the oxidation mechanism.In this study, high temperature oxidation tests in oxygen, air and air - steam atmospheres were performed with Zircaloy-4 cladding tubes and plates specimens. Because spent fuels are concerned, the presence of the corrosion oxide scale formed in reactor during in-service operation was also considered. The tests described in this study were restricted to the 700 – 950°C range, and mostly performed at 850°C. Raman imaging was used to examine the specimens after the oxidation tests, either directly at the oxide surface, or on metallographic preparations for cross-section examinations.We first used new opportunities given by Raman imaging to analyze, at least qualitatively, the structural and mechanical phenomena involved in this particular corrosion phenomena. Indeed, this method can provide information on the nature of phases that are present in the scale and on the stress level with sub-micron spatial resolution. The different methods used to extract this information that is present in the Raman data will be described and discussed.Moreover, isotopic substitution was also considered. Actually, Raman spectroscopy has also the ability to quantitatively extract the 18O distribution in a zirconia oxide scale after a two stage experiment with a fair accuracy. Raman imaging gave clear evidence for different characteristic distributions of 18O in the scales. Some of them have been correlated with the development of cracks and porosity in the oxide which allows the corroding medium to penetrate locally in the scales. Nitrogen appears to have no or limited influence on the oxygen diffusion, but is observed to reach the metal/oxide interface much faster than oxygen. Thus, we also emphasize the potential of this method to investigate the 18O distribution in such complex corrosion scales.

Identification and visualization of environmental microplastics by Raman imaging based on hyperspectral unmixing coupled machine learning

Microplastics (MPs) are ubiquitous contaminants that have become an emerging pollutant of concern, potentially threatening human health and ecosystem environments. Although current detection methods can accurately identify various types of MPs, it remains necessary to develop non-destructive and rapid methods to meet growing demands for detection. Herein, we combine a hyperspectral unmixing method and machine learning to analyse Raman imaging data of environmental MPs. Five MPs types including poly(butylene adipate-co-terephthalate) (PBAT), poly(butylene succinate) (PBS), p-polyethylene (PE), polystyrene (PS) and polypropylene (PP) were visualized and identified. Individual or mixed pure or aged MPs along with environmental samples were analysed by Raman imaging. Alternating volume maximization (AVmax) combined with unconstrained least squares (UCLS) method estimated end members and abundance maps of each of the MPs in the samples. Pearson correlation coefficients (r) were used as the evaluation index; the results showed that there is a high similarity between the raw spectra and the average spectra calculated by AVmax. This indicates that Raman imaging based on machine learning and hyperspectral unmixing is a novel imaging analysis method that can directly identify and visualize MPs in the environment.

Ubiquitous occurrence of organogenic dolomite in a late Ediacaran limestone-dominated succession from the Eastern Yangtze Gorges area of South China

The widespread occurrence of dolostones in Paleozoic and Precambrian sequences stands in stark contrast to the difficulty of precipitating dolomite in experiments at low temperature under abiotic conditions. The discoveries of dolomite within modern microbial mats and the successful synthesis of proto-dolomite under ambient conditions catalyzed by various microorganisms and organic molecules lend support to the development of organogenic dolomite models. Despite this, convincing evidence for ancient primary dolomite precipitation is rare, mainly due to post-depositional modifications of original textures and mineralogy. Here we present petrographic, mineralogical and C-isotopic data of carbonates preserving primary sedimentary textures in the late Ediacaran Shibantan Member of the Dengying Formation from the eastern Yangtze Gorges area, South China. These carbonates are characterized by calcite layers intercalated with millimeter-thick, organic-rich layers that contain up to 50 % dolomite. Petrographic observations and Raman imaging show that the dolomite occurs preferentially in microbial mats and is closely associated with organic matter. Scanning electron microscopy also revealed the presence of Mg-rich nanoscopic carbonate aggregates and ribbon-like microstructures akin to mineralized extracellular polymeric substances (EPS) in mat layers. These observations are inconsistent with replacive origins but instead support an organogenic origin for the Shibantan dolomite. The relatively high TOC (total organic carbon) contents and consistently positive δ13Ccarb values (up to +4.8 ‰) suggest enhanced burial of organic carbon produced in microbial mats during the Shibantan deposition. Subsequent organic degradation by microorganisms in anoxic, early diagenetic environments likely promoted the ubiquitous formation of organogenic dolomite in Shibantan microbial mats. Our study highlights the important roles of organogenic dolomites in interpreting the wide occurrence of Paleozoic and Precambrian dolostones.

Automatic subtyping of Diffuse Large B-cell Lymphomas (DLBCL): Raman-based genetic and metabolic classification

Diffuse large B-cell lymphoma (DLBCL), the most common non-Hodgkin’s lymphoma in adults, is a genetically and metabolically heterogeneous group of aggressive malignancies. The complexity of their molecular composition and the variability in clinical presentation make clinical diagnosis and treatment selection a serious challenge. The challenge is therefore to quickly and correctly classify DLBCL cells. In this work, we show that Raman imaging is a tool with high diagnostic potential, providing unique information about the biochemical components of tumor cells and their metabolism. We present models of classification of lymphoma cells based on their Raman spectra. The models automatically and efficiently identify DLBCL cells and assign them to a given cell-of-origin (COO) subtype (activated B cell-like (ABC) or germinal center B cell-like (GCB)) or, respectively, to a comprehensive cluster classification (CCC) subtype (OxPhos/non-OxPhos). In addition, we describe each lymphoma subtype by its unique spectral profile, linking it to biochemical, genetic, or metabolic features.

Probing Chemical Complexity of Amyloid Plaques in Alzheimer's Disease Mice using Hyperspectral Raman Imaging.

One of the distinctive pathological features of Alzheimer's disease (AD) is the deposition of amyloid plaques within the brain of affected individuals. These plaques have traditionally been investigated using labeling techniques such as immunohistochemical imaging. However, the use of labeling can disrupt the structural integrity of the molecules being analyzed. Hence, it is imperative to employ label-free imaging methods for noninvasive examination of amyloid deposits in their native form, thereby providing more relevant information pertaining to AD. This study presents compelling evidence that label-free and nondestructive confocal Raman imaging is a highly effective approach for the identification and chemical characterization of amyloid plaques within cortical regions of an arcAβ mouse model of AD. Furthermore, this investigation elucidates how the spatial correlation of Raman signals can be exploited to identify robust Raman marker bands and discern proteins and lipids from amyloid plaques. Finally, this study uncovers the existence of distinct types of amyloid plaques in the arcAβ mouse brain, exhibiting significant disparities in terms of not only shape and size but also molecular composition.

Conquering Metal–Organic Frameworks by Raman Scattering Techniques

AbstractMetal–organic frameworks (MOFs) have emerged as a versatile class of materials. Understanding the structural and chemical properties of MOFs is essential for tailoring their performance to suit specific applications. In recent years, Raman spectroscopy, a non‐destructive vibrational spectroscopic technique, has evolved into a powerful tool for characterizing MOFs. Beyond spontaneous Raman scattering, numerous advanced Raman techniques have been developed to amplify the inherently weak Raman signal. These innovations have significantly enhanced the spatial resolution, frame rate, and sensitivity of Raman imaging, thereby opening up new possibilities for applications, such as label‐free sensing. This tutorial work is designed to demystify the fundamental theory and instrumentation of Raman spectroscopy, elucidating the differences between commonly used Raman techniques. Particular emphasis is placed on their advantages and limitations when applied to MOF materials. Furthermore, this work showcases how Raman techniques are employed in studying MOF systems to address societal needs and explore future directions. Ultimately, this review emphasizes the crucial role of Raman spectroscopy and the development of novel Raman‐based in situ techniques to conquer the field of MOFs.

Application of Raman spectroscopy to the evaluation of F-actin changes in sea urchin eggs at fertilization.

The actin filaments on the surface of echinoderm oocytes and eggs readily undergo massive reorganization during meiotic maturation and fertilization. In sea urchin eggs, the actin cytoskeletal response to the fertilizing sperm is fast enough to accompany Ca2+ signals and to guide sperm's entry into the egg. Although recent work using live cell imaging technology confirmed changes in the actin polymerization status in fertilized eggs, as was previously shown using light and electron microscopy, it failed to provide experimental evidence of F-actin depolymerization a few seconds after insemination, which is concurrent with the sperm-induced Ca2+ release. In the present study, we applied Raman microspectroscopy to tackle this issue by examining the spectral profiles of the egg's subplasmalemmal regions before and after treating the eggs with actin drugs or fertilizing sperm. At both early (15 s) and late (15 min) time points after fertilization, specific peak shifts in the Raman spectra revealed change in the actin structure, and Raman imaging detected the cytoskeletal changes corresponding to the F-actin reorganization visualized with LifeAct-GFP in confocal microscopy. Our observation suggests that the application of Raman spectroscopy, which does not require microinjection of fluorescent probes and exogenous gene expression, may serve as an alternative or even advantageous method in disclosing rapid subtle changes in the subplasmalemmal actin cytoskeleton that are difficult to resolve.

Advancing the understanding of Aβ plaques through a systematic comparison of statistical methods applied to hyperspectral Raman data

AbstractBackgroundWhile fluorescence microscopy is widely applied in Alzheimer’s disease (AD) research, spectroscopic imaging methods like Raman imaging have only emerged recently. As a label‐free, chemically selective method, Raman microscopy can complement established imaging approaches by providing data as hyperspectral images, containing information on the molecular structure, composition, and distribution within a sample. However, brain tissue is a challenging tissue to analyze, since spectral variations in the sample are extremely low. Thus, to analyze pathologic hallmarks such as beta‐amyloid (Aβ) plaques, including the surrounding tissue, a systematic approach to accessing hyperspectral images is needed and herein presented.MethodRegions of interest containing Aβ plaques in brain sections of 10 month old APP/PS1 (Amyloid‐Precursor‐Protein / Presenilin‐1) mice were imaged using a confocal Raman microscope (WITec GmbH, Ulm, Germany). The hyperspectral Raman data sets were processed using Project 4 software (WITec GmbH, Ulm, Germany), in‐house written Matlab scripts (The Mathworks, Inc., Natick, USA) and the Matlab‐integrated RamanLight App.ResultBy comparing univariate peak integration analysis and multivariate methods, such as hierarchical cluster analysis, spectral unmixing, and 2D correlation spectroscopy on the same hyperspectral Raman data set of an Aβ plaque and its surrounding tissue, the approaches could be systematically studied. pathological structures in the tissue can only be successfully differentiated, if multivariate methods (cluster analysis, spectral unmixing) are used. Nonetheless, univariate peak integration can provide basic spatial and compositional information on the tissue and the Aβ plaque. By applying 2D correlation spectroscopy, interfaces between the Aβ plaque and the surrounding tissue can be thoroughly analyzed.ConclusionIn this study, a hyperspectral Raman image of an Aβ plaque and the surrounding tissue has successfully been analyzed with diverse explorative statistical methods to access Therefore, this study paves the way for the application of label‐free techniques such as Raman imaging into AD research by serving as a guide for the systematic analysis of hyperspectral images of pathological structures.

The Application of Handheld Near-Infrared Spectroscopy and Raman Spectroscopic Imaging for the Identification and Quality Control of Food Products.

The following investigations describe the potential of handheld NIR spectroscopy and Raman imaging measurements for the identification and authentication of food products. On the one hand, during the last decade, handheld NIR spectroscopy has made the greatest progress among vibrational spectroscopic methods in terms of miniaturization and price/performance ratio, and on the other hand, the Raman spectroscopic imaging method can achieve the best lateral resolution when examining the heterogeneous composition of samples. The utilization of both methods is further enhanced via the combination with chemometric evaluation methods with respect to the detection, identification, and discrimination of illegal counterfeiting of food products. To demonstrate the solution to practical problems with these two spectroscopic techniques, the results of our recent investigations obtained for various industrial processes and customer-relevant product examples have been discussed in this article. Specifically, the monitoring of food extraction processes (e.g., ethanol extraction of clove and water extraction of wolfberry) and the identification of food quality (e.g., differentiation of cocoa nibs and cocoa beans) via handheld NIR spectroscopy, and the detection and quantification of adulterations in powdered dairy products via Raman imaging were outlined in some detail. Although the present work only demonstrates exemplary product and process examples, the applications provide a balanced overview of materials with different physical properties and manufacturing processes in order to be able to derive modified applications for other products or production processes.

Label‐free Raman spectroscopy for the assessment of liver fibrosis

AbstractThe enormous incidence and dire consequences of liver fibrosis highlight the need for biologically plausible markers as readouts of pathology and drug efficacy. The classic reference standard for diagnosis of liver fibrosis is histopathological examinations. However, it is burdened by time‐consuming steps, subjectivity, and the static nature of information. Raman spectroscopy is a rapid, objective, and label‐free analytical tool that provides numerous molecular information. Thus, we utilized Raman spectroscopy to characterize the pathological features of liver fibrosis and to evaluate the efficacy of drugs (obeticholic acid [OCA] and gigantol [YS30]). Principal component analysis with subsequent linear discriminant analysis could basically discriminate between fibrotic mice and healthy controls and showed that animals with OCA or YS30 treatment resembled those with vehicle treatment. Moreover, we identified several potential biomarkers, including cytochrome C, for liver fibrosis development based on their Raman “fingerprints.” Raman imaging provided quantitative and spatial distribution of collagen and cytochrome C in situ. In addition, we demonstrated the role of OCA or YS30 in revitalizing mitochondrial function to alleviate liver fibrosis using Raman spectroscopy. Collectively, Raman spectroscopy succeeded in tracking liver fibrosis progression, assessing drug efficacy, and yielding significant molecular information useful for quantitatively linking pathological assessment and mechanism of drug action.

From Vibrations to Visions: Raman Spectroscopy’s Impact on Skin Cancer Diagnostics

Raman spectroscopy, a non-invasive diagnostic technique capturing molecular vibrations, offers significant advancements in skin cancer diagnostics. This review delineates the ascent of Raman spectroscopy from classical methodologies to the forefront of modern technology, emphasizing its precision in differentiating between malignant and benign skin tissues. Our study offers a detailed examination of distinct Raman spectroscopic signatures found in skin cancer, concentrating specifically on squamous cell carcinoma, basal cell carcinoma, and melanoma, across both in vitro and in vivo research. The discussion extends to future possibilities, spotlighting enhancements in portable Raman instruments, the adoption of machine learning for spectral data refinement, and the merging of Raman imaging with other diagnostic techniques. The review culminates by contemplating the broader implications of these advancements, suggesting a trajectory that may significantly optimize the accuracy and efficiency of skin cancer diagnostics.

Characterization of Equilibrium Catalysts from the Fluid Catalytic Cracking Process of Atmospheric Residue

In the FCC conversion of heavy petroleum fractions as atmospheric residues, the main challenge for refiners to achieve the quantity and quality of various commercial products depends essentially on the catalyst used in the process. A deep characterization of the catalyst at different steps of the process (fresh, regenerated, and spent catalyst) was investigated to study the catalyst’s behavior including the physicochemical evolution, the deactivation factor, and kinetic–thermodynamic parameters. All samples were characterized using various spectroscopy methods such as N2 adsorption–desorption, UV-visible spectroscopy, Raman spectroscopy, LECO carbon analysis, scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray fluorescence (XRF), nuclear magnetic resonance spectroscopy (NMR13C) analysis, and thermogravimetric analysis. The results of the N2 adsorption–desorption, UV-vis, Raman, LECO carbon, and SEM imaging showed that the main causes of catalyst deactivation and coking were the deposition of carbon species that covered the active sites and clogged the pores, and the attrition factor due to thermal conditions and poisonous metals. The XRD and XRF results showed the catalyst’s physicochemical evolution during the process and the different interlinks between catalyst and feedstock (Nickel, Vanadium, Sulfur, and Iron) elements which should be responsible for the coking and catalyst attrition factor. It has been found that, in addition to the temperature, the residence time of the catalyst in the process also influences catalyst structure transformation. NMR13C analysis revealed that polyaromatic hydrocarbon is the main component in the deposited coke of the spent catalyst. The pyridine-FTIR indicates that the catalyst thermal treatment has an influence on its Brønsted and Lewis acid sites and the distribution of the products. Thermogravimetric analysis showed that the order of catalyst mass loss was fresh > regenerated > spent catalyst due to the progressive losses of the hydroxyl bonds (OH) and the structure change along the catalyst thermal treatment. Moreover, the kinetic and thermodynamic parameters showed that all zones are non-spontaneous endothermic reactions.

Label-Free Identification of Biochemical Footprints of Key Mutations in Acute Myeloid Leukemia Using Raman Spectroscopy

Introduction: With increasing understanding of the complexity and heterogeneity of acute myeloid leukemia (AML), the diagnostic process becomes more complicated and time-consuming. Therefore, there is a need to supplement AML diagnostics with a quick method to identify key, druggable genetic lesions that serve as a target for small molecule drugs. To address this need, we used Raman spectroscopy (RS)-based imaging methods. RS uses non-elastic scattering of laser light on cell biomolecules. When coupled to computational image segmentation and analysis, composition of the analyzed fragment of the cytoplasm can be characterized. In this way, by mapping the focal surface of the cell, we obtain a multidimensional image containing information about the distribution of its biochemical components. In our study, we utilized RS microscopy to investigate how single mutations affect the Raman spectral images of leukemic cells. The primary objective was to determine whether this technique could be utilized to identify mutations in key genes related to leukemia development. Methods: Genetic models of AML were generated by introducing wild-type and mutated forms of three frequently mutated and/or druggable genes (FLT3, IDH1/2, and MLL) into myeloid cell lines (THP1, HEL, HL60). The presence of the desired phenotype was confirmed using flow cytometry, immunoblotting, high-throughput kinase activity assay (Pamgene), and confocal microscopy. For RS experiments, the cells were fixed, and subjected to Raman imaging measurements using the WITec Alpha 300 microscope. Reference or control samples consisted of cells with wild-type genes (WT). The recorded hyperspectral data was subjected to multidimensional chemometric analysis, including k-means cluster analysis (KMCA), principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). To validate the findings from the cell models, primary AML cells from 30 patients diagnosed with FLT-3 ITD, MLL or IDH1/2 mutations were examined with RS. Peripheral blood mononuclear cells (PBMC) isolated from healthy donors blood served as reference. Results: The spectral differences between cells with wild-type FLT3 and FLT3/ITD were subtle, yet significant. The results revealed distinct variances in lipid (1440, 1240, 1660, and 2850 cm -1) and hemoprotein content (740-760 cm -1). Additionally, we successfully distinguished cells harboring IDH1/2 and MLL mutations from those with the wild-type genes. In these cases Raman bands corresponding to protein and nucleic acids composition were changed in comparison to control cells. Raman features in PCA loadings were also related to proteins and nucleic acids. Using the PLS-DA method and simple AI model, we built successful algorithm for distinguishing transgenic models carrying mutation from wild-type cells. PCA analysis also enabled us to distinguish AML primary cells from PBMC. Analysis weather primary cells carrying the selected mutations can be distinguished from non-mutated AML cells is concordant with our findings on models, however, it requires further analysis in larger group of patients to increase statistical power of these observations. Conclusions: Collectively, these findings suggest that Raman spectroscopy coupled with appropriate chemometric analysis may serve as rapid, label-free tool for identification of key druggable mutations in AML. Acknowledgements: The studies were performed as a part of the “ Label-free and rapid optical imaging, detection and sorting of leukemia cells” project carried out within the Team-Net programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.

Unraveling Molecular Composition in Biological Samples-Systematic Evaluation of Statistical Methods for the Analysis of Hyperspectral Raman Data.

Recently, confocal Raman microscopy has gained popularity in biomedical research for studying tissues in healthy and diseased state due to its ability to acquire chemically selective data in a noninvasive approach. However, biological samples, such as brain tissue, are inherently difficult to analyze due to the superposition of molecules in the Raman spectra and low variation of spectral features within the sample. The analysis is further impeded by pathological hallmarks, for example beta-amyloid (Aβ) plaques in Alzheimer's disease, which are often solely characterized by subtle shifts in the respective Raman peaks. To unravel the underlying molecular information, convoluted statistical procedures are inevitable. Unfortunately, such statistical methods are often inadequately described, and most natural scientists lack knowledge of their appropriate use, causing unreproducible results and stagnation in the application of hyperspectral Raman imaging. Therefore, we have set out to provide a comprehensive guide to address these challenges with the example of a complex hyperspectral data set of brain tissue samples with Aβ plaques. Our study encompasses established as well as novel statistical methods, including univariate analysis, principal component analysis, cluster analysis, spectral unmixing, and 2D correlation spectroscopy, and critically compares the outcomes of each analysis. Moreover, we transparently demonstrate the effect of preprocessing decisions like denoising and scaling techniques, providing valuable insights into implications of spectral quality for data evaluation. Thereby, this study provides a comprehensive evaluation of analysis approaches for complex hyperspectral Raman data, laying out a blueprint for elucidating meaningful information from biological samples in chemical imaging.

From celebration to contamination: Analysing microplastics released by burst balloons

Air balloons are a ubiquitous presence in our daily lives, and their rupture may release a substantial quantity of debris, as investigated herein. We employ Raman imaging to capture the fragments resulting from balloon explosions, enabling the identification and direct visualisation of minute microplastic particles / fragments with an improved signal-to-noise ratio for precise quantification. To circumvent the generation of misleading confocal Raman images, we recommend employing terrain mapping to scan the three-dimensional surface of the sample. It is important to acknowledge that the analysis of microplastics at the micro-scale inherently poses limitations in terms of throughput, as it necessitates a trade-off between low and high magnifications. We conduct explosive experiments on ten-to-hundred balloons, collecting debris from various angles and positions. Our investigation involves the random testing of multiple samples / sample positions at the micro-scale, with subsequent extrapolation to estimate the total amount of microplastics. The amalgamation of these results through statistical analysis indicates that each balloon explosion can potentially release tens-to-thousands of microplastics, highlighting a concern that has hitherto received insufficient attention. The characterisation approach, particularly the random Raman scanning method in combination with SEM and the statistical analysis on accumulated samples employed in this report, has the potential to serve as a useful tool in future research on microplastics and even nanoplastics.

Dual-purpose nanoparticle assembly for identifying preferential intracellular targets of oxindolimine copper(II) complexes through SERS imaging

In this study, the main goal was to identify the intracellular targets of oxindolimine copper(II) complexes by SERS and Raman imaging of living HeLa cells, whose action as a cancer inhibitor has been previously determined. The used SERS probe was constituted of spherical gold nanoparticles together with the Raman reporter 5,5′-dithiobis-2-nitrobenzoic acid (DTNB) and oxindolimine copper(II) complexes, [Cu(isatp)Cl] and [Cu(nisatp)H2O]. Two excitation radiations were used to acquire living single HeLa cells images: 532 nm to acquire the conventional Raman information of the cells and 785 nm to acquire the localization of the modified SERS probe inside the cells. The Raman images of the main intracellular components were obtained by multivariate analysis, and the SERS images were obtained by univariate analysis using a Raman reporter. The SERS and Raman images showed that the complexes could enter the cells together with the gold nanoparticles, causing main damage to the nuclei and lipids-rich region. In addition, there was no evidence of water entry into the cell.

Characterising microplastics in indoor air: Insights from Raman imaging analysis of air filter samples

We are directly exposed to microplastic contamination via indoor air that we breathe daily, for which the characterisation of microplastics is still a challenge. Herein, two typical air filter samples were collected, one from an air-conditioner and another from a personal computer, both of which have been working for around half a year to collect and accumulate microplastics in the indoor air, like microplastic banks. After the sample preparation to remove the mineral dusts, Raman imaging was employed to directly and simultaneously identify and visualise microplastics of polyethylene terephthalate (PET) fibres, distinguish them from other fibres such as cellulose and cross-check them with a scanning electron microscope (SEM). To count the microplastics and to avoid the quantification bias, several areas were randomly scanned and imaged to statistically estimate the percentage of microplastic fibres in the analysed samples. The microplastics amount, which has been estimated at 73–88,000 fibers per filter per half a year, varies and depends on the indoor environment so that the air filter can work as a good indicator to monitor the quality of the indoor air from the microplastic perspective. Overall, human are directly exposed to this emerging contamination every day, raising environmental concerns. Raman imaging characterisation and its corresponding statistical information can help pursue further research on microplastics.

Analysis of Infrared and Raman Imaging Data Using Alternating and Modified Alternating Least Squares

Modified alternating least squares (MALS) outperforms alternating least squares (ALS) in the analysis of infrared and Raman image spectral data. MALS offers superior stability thanks to ridge regression and a substantial speed advantage due to the kernel nature of the algorithm, reducing computational overhead. MALS excels in resolving basis vectors even in low signal-to-noise, nearly collinear data, whereas ALS often falls short. For spectroscopic imaging, both MALS and other ALS methods rely on spatial resolution between sample components, as low spatial resolution leads to increased mixing of components. Spectroscopic imaging combines spectroscopy and digital imaging to extract chemical composition. Multivariate curve resolution (MCR)’s foundation in ALS regression makes it a vital tool for this analysis, enabling a comprehensive examination of complex spectroscopic images. This tutorial delves into the mathematical techniques necessary for extracting chemical insights from infrared and Raman spectroscopic images. While this discussion focuses on two-dimensional spatial data, the methodology can be extended to three-dimensional data.

Silicon-Based Nanoelectronic Semiconductor Material and Its Surface-Enhanced Raman Image Signals

One-dimensional Zinc Oxide (ZnO) nanomaterials have important applications in devices, such as solar energy, sensors, and piezoelectric materials due to surface effects and quantum size effects. As a semiconductor material, Si has good thermal conductivity and low resistivity. A layer of n-type ZnO nanowires is grown on P-type monocrystalline silicon by transferring the Anodic Aluminum Oxide (AAO) template. The enhanced Raman effect on the surface is attempted by preparing n-type ZnO-P-type Si heterojunction nanostructures. This paper uses Si-based AAO as a template and uses electrochemical deposition method to prepare silicon-based nano-electronic semiconductor material ZnO/AAO/Si. According to SEM analysis, there are uniform pores on the surface of the material, and the cross-section shows that ZnO nanowires have been assembled into the AAO/Si structure. XRD analysis shows that ZnOn nanowires have a hexagonal wurtzite structure. In optoelectronic testing, through PL spectral analysis, the semiconductor material ZnO/AAO/Si has a strong yellow green emission peak near 574 nm. Field emission analysis shows that the β value of the ZnO field enhancement factor in the ZnO/AAO/Si structure is relatively high. The Surface-Enhanced Raman Spectroscopy (SERS) image signal experiment shows that the silicon-based nano-electronic semiconductor material prepared by electrochemical deposition method has good SERS characteristics. The SERS spectral intensity increases with the increase of the concentration of the detected substance and can be used as a SERS substrate to achieve Raman spectral image signal detection of chemical substances with low concentration.