Vibrational Imaging Research Articles

Revealing the Molecular Physics of Lattice Self-Assembly by Vibrational Hyperspectral Imaging.

Lattice self-assemblies (LSAs), which mimic protein assemblies, were studied using a new nonlinear vibrational imaging technique called vibrational sum-frequency generation (VSFG) microscopy. This technique successfully mapped out the mesoscopic morphology, microscopic geometry, symmetry, and ultrafast dynamics of an LSA formed by β-cyclodextrin (β-CD) and sodium dodecyl sulfate (SDS). The spatial imaging also revealed correlations between these different physical properties. Such knowledge shed light on the functions and mechanical properties of LSAs. In this Feature Article, we briefly introduce the fundamental principles of the VSFG microscope and then discuss the in-depth molecular physics of the LSAs revealed by this imaging technique. The application of the VSFG microscope to the artificial LSAs also paved the way for an alternative approach to studying the structure-dynamic-function relationships of protein assemblies, which were essential for life and difficult to study because of their various and complicated interactions. We expect that the hyperspectral VSFG microscope could be broadly applied to many noncentrosymmetric soft materials.

Improved tissue preparation for multimodal vibrational imaging of biological tissues

The complementary nature of Infrared (IR) and Raman spectroscopies enables a thorough understanding of biological tissue – so called multimodal vibrational spectroscopic imaging. However, new approaches in terms of sample preparation and data analysis are required to release the full potential of multimodal spectroscopy. Herein, we propose an inexpensive and relatively simple sample preparation technique incorporating mirror-finished stainless-steel slides and polyethylene glycol as an embedding medium that is compatible for both infrared and Raman spectroscopy of tissue sections. K-Means Clustering and Principal Component Analysis (PCA) were used to evaluate the performance of multimodal vibrational spectroscopic imaging compared with IR and Raman spectroscopic imaging individually using a rat kidney as a model. The K-Means cluster maps generated with the multimodal dataset showed the best correlation between different tissue types identified by an adjacent section stained with Masson’s Trichrome compared to either Raman or IR spectroscopy analysed independently. PCA score maps of the multimodal dataset produced a clear separation of individual tissue types along the first three Principal Components. Additionally, PCA permitted the correlation of IR and Raman peaks arising mainly from collagen vibrational modes. Finally, polyethylene glycol embedding is shown as an attractive alternative to paraffin embedding for spectroscopic analyses, due to significantly less fluorescence in Raman measurements and retention of lipids in the tissue, without any retention of the medium within the tissue.

Vision-Based Measurement for Catenary Transverse Vibrations in the Winding Mine Hoist

The characteristics of rope vibration of the winding mine hoist are one of the main evaluation indices for evaluating the characteristics of the engineering mechanics and the safety coefficient of the hoist system. For this purpose, research was conducted on the vision-based measurement method for catenary transverse vibration. Firstly, a high-speed camera was utilized to capture grayscale images of the catenary transverse vibration. Secondly, the region of interest (ROI) was selected, and the ROI was binarized based on the wavelet edge extraction algorithm. Thirdly, in the ROI, the centroid position of the rope was obtained by calculating the matrix, which was described by the binary, and later, the transverse vibration signal of the catenary was obtained. Finally, the empirical mode decomposition (EMD) method was used to remove the periodic excitation caused by the displacement of the catenary winding. The experimental results show that the proposed method quickly and accurately comprehends the noncontact measurement of the catenary transverse vibration, and this method could also provide the technical support necessary for the measurement and evaluation of the safety parameters of the hoisting system.

Label-Free Live-Cell Imaging of Internalized Microplastics and Cytoplasmic Organelles with Multicolor CARS Microscopy.

As the bioaccumulation of microplastics (MPs) is considered as a potential health risk, many efforts have been made to understand the cellular dynamics and cytotoxicity of MPs. Here, we demonstrate that label-free multicolor coherent anti-Stokes Raman scattering (CARS) microscopy enables separate vibrational imaging of internalized MPs and lipid droplets (LDs) with indistinguishable shapes and sizes in live cells. By simultaneously obtaining polystyrene (PS)- and lipid-specific CARS images at two very different frequencies, 1000 and 2850 cm-1, respectively, we successfully identify the local distribution of ingested PS beads and native LDs in Caenorhabditis elegans. We further show that the movements of PS beads and LDs in live cells can be separately tracked in real time, which allows us to characterize their individual intracellular dynamics. We thus anticipate that our multicolor CARS imaging method could be of great use to investigate the cellular transport and cytotoxicity of MPs without additional efforts for pre-labeling to MPs.

Investigating the Performances of Wide-Field Raman Microscopy with Stochastic Optical Reconstruction Post-Processing.

Super-resolution fluorescence microscopy based on localization algorithms has tremendously impacted the field of imaging by improving the spatial resolution of optical measurements with specific blinking fluorophores and concomitant reduction of acquisition time. In vibrational spectroscopy and imaging, various methods have been developed to surpass the diffraction limit including near-field scattering methods, such as in tip-enhanced Raman and infrared spectroscopies. Although these scanning-probe techniques can provide exquisite spatial resolution, they often require long acquisition times and tedious fabrication of nano-scale scanning probes. Herein, stochastic optical reconstruction microscopy (STORM) protocol is applied on Raman measurements acquired using a wide-field home-built microscopy setup. We explore how the fluctuations of the Raman signal acquired over a series of time-lapse images at specific spectral ranges can be exploited with STORM processing, possibly revealing details with improved spatial resolution, under lower irradiance and with faster acquisition speed that cannot be achieved in point scanning mode over the same field of view. Samples studied here include patterned silicon, polystyrene microspheres on a silicon wafer, and graphene on a silicon/silicon dioxide substrate. The outcome presents an effective way to collect Raman images at selected spectral ranges with spatial resolutions of ∼200 nm over a large field of view under 532 nm excitation together with an acquisition speed improved by two orders of magnitude and under a significantly reduced irradiance compared to confocal laser scanning acquisition.

Femtometer-amplitude imaging of coherent super high frequency vibrations in micromechanical resonators

Dynamic measurement of femtometer-displacement vibrations in mechanical resonators at microwave frequencies is critical for a number of emerging high-impact technologies including 5G wireless communications and quantum state generation, storage, and transfer. However, the resolution of continuous-wave laser interferometry, the method most commonly used for imaging vibration wavefields, has been limited to vibration amplitudes just below a picometer at several gigahertz. This is insufficient for these technologies since vibration amplitudes precipitously decrease for increasing frequency. Here we present a stroboscopic optical sampling approach for the transduction of coherent super high frequency vibrations. Phase-sensitive absolute displacement detection with a noise floor of 55 fm/√Hz for frequencies up to 12 GHz is demonstrated, achieving higher bandwidth and significantly lower noise floor simultaneously compared to previous work. An acoustic microresonator with resonances above 10 GHz and displacements smaller than 70 fm is measured using the presented method to reveal complex mode superposition, dispersion, and anisotropic propagation.

Insightful vibrational imaging study on the hydration mechanism of carbamazepine.

Anhydrous carbamazepine (CBZ) is an anti-convulsant drug commonly used to treat epilepsy and relieve trigeminal neuralgia. The presence of the dihydrate form in commercial CBZ tablets can change the dissolution rate of the active pharmaceutical ingredient (API), thus decreasing its activity. The hydration transformation can occur during wet granulation or storage, within a few weeks, depending on the ambient conditions. This work aims to investigate the effect of relative humidity (RH) in the transition of pure anhydrous CBZ (CBZ III) into the hydrate form by using confocal Raman microscopy with cluster analysis (CA). Firstly, several tablets of pure CBZ III containing different amounts of CBZ DH (50%, 10%, 1%, 0.5%) were prepared and analyzed by Raman imaging with CA. Our results show that CBZ DH crystals can be detected in the CBZ III tablets, at as low a concentration as 0.5%, giving distinct Raman features for the analysed polymorphs. The stability of pure anhydrous (CBZ III) tablets was then monitored by Raman imaging at room temperature (20-22 °C) and different RH (6%, 60% and 89%). The Raman imaging with CA showed that the anhydrous CBZ tablets start to convert into the hydrate form after 48 h, and it completely changes after 120 hours (5 days) at RH 89%. The tablets exposed to RH 6% and 60% did not demonstrate the presence of CBZ DH after 1 week of exposure. The exposure time was extended for 9 months in the former, and no CBZ DH was observed. A comparative study using IR imaging was also performed, demonstrating the viability of these vibrational imaging techniques as valuable tools to monitor the hydration process of active pharmaceutical ingredients.

Tip-induced bond weakening, tilting, and hopping of a single CO molecule on Cu(100)

The interaction between a probing tip and an adsorbed molecule can significantly impact the molecular chemical structure and even induce its motion on the surface. In this study, the tip-induced bond weakening, tilting, and hopping processes of a single molecule were investigated by sub-nanometre resolved tip-enhanced Raman spectroscopy (TERS). We used single carbon monoxide (CO) molecules adsorbed on the Cu (100) surface as a model system for the investigation. The vibrational frequency of the C−O stretching mode is always redshifted as the tip approaches, revealing the weakening of the C−O bond owing to tip−molecule interactions. Further analyses of both the vibrational Stark effect and TERS imaging patterns suggest a delicate tilting phenomenon of the adsorbed CO molecule on Cu(100), which eventually leads to lateral hopping of the molecule. While a tilting orientation is found toward the hollow site along the [110] direction of the Cu(100) surface, the hopping event is more likely to proceed via the bridge site to the nearest Cu neighbour along the [100] or [010] direction. Our results provide deep insights into the microscopic mechanisms of tip−molecule interactions and tip-induced molecular motions on surfaces at the single-bond level.

Wide-field broadband CARS microscopy

Coherent anti-Stokes Raman scattering is an extremely powerful non-linear optical (NLO) microscopy technique for label-free vibrational imaging allowing for a detailed study of biological samples in their native state. To overcome the long acquisition times associated with raster sample scanning required in NLO microscopy, which impair real-time investigation of fast biological dynamics, we employ here wide-field signal generation over a large field of view, covering tens of micrometers. To this aim, we exploit an innovative approach based on the use of an amplified femtosecond ytterbium laser source delivering high energy (≈μJ) pulses in the near infrared. This enables the generation of stable broadband Stokes pulses to measure the entire fingerprint region of the molecular vibrational spectrum, the richest in chemical information. Our results pave the way for future translational applications and clinical diagnostics with video-rate imaging capabilities. ©2022 The Authors

Multi-modal vibrational analysis of blend polymers using mid-infrared photothermal and Raman microscopies

Chemical characterization of polymeric samples, such as blend polymers is a fundamental analytical approach for advancement of polymer science and polymer technologies. Vibrational imaging is a promising technique to analyze chemical compositions of materials in a wide range of fields. Raman scattering and infrared (IR) absorption reflect complementary molecular vibrations; therefore, Raman and IR imaging provide rich information of the spatial distributions of chemical compositions of samples, revealing their complicated chemical structures. However, the discrepancy of spatial resolution in Raman and conventional IR imaging makes it difficult to merge these techniques. In the present study, we performed chemical characterization of polymeric samples using Raman and IR imaging enabled by mid-infrared photothermal (MIP) microscopy. The chemical compositions of blend polymer films were visualized based on both Raman scattering and IR absorption with the spatial resolution of the sub-micrometer scale. We found that the varying contrasts in Raman and MIP imaging enabled the investigation of averaged and surface-sensitive compositions of polymer blends. Raman and MIP imaging holds a promise of unique applications in a wide range of current research fields, such as polymers, two-dimensional materials, and biology.

Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning.

Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-domain, frequency-domain, or time-frequency domain statistical parameters. Vibration signals of bearing fault are highly non-linear and non-stationary making it cumbersome to extract relevant information for existing methodologies. This process even became more complicated when the bearing operates at variable speeds and load conditions. To address these challenges, this study develops an autonomous diagnostic system that combines signal-to-image transformation techniques for multi-domain information with convolutional neural network (CNN)-aided multitask learning (MTL). To address variable operating conditions, a composite color image is created by fusing information from multi-domains, such as the raw time-domain signal, the spectrum of the time-domain signal, and the envelope spectrum of the time-frequency analysis. This 2-D composite image, named multi-domain fusion-based vibration imaging (MDFVI), is highly effective in generating a unique pattern even with variable speeds and loads. Following that, these MDFVI images are fed to the proposed MTL-based CNN architecture to identify faults in variable speed and health conditions concurrently. The proposed method is tested on two benchmark datasets from the bearing experiment. The experimental results suggested that the proposed method outperformed state-of-the-arts in both datasets.

Vibrational Imaging Techniques for the Characterization of Hard Dental Tissues: From Bench-Top to Chair-Side

Currently, various analytical techniques, including scanning electron microscopy, X-Ray diffraction, microcomputed tomography, and energy dispersive X-ray spectroscopy, are available to study the structural or elemental features of hard dental tissues. In contrast to these approaches, Raman Microspectroscopy (RMS) has the great advantage of simultaneously providing, at the same time and on the same sample, a morpho-chemical correlation between the microscopic information from the visual analysis of the sample and its chemical and macromolecular composition. Hence, RMS represents an innovative and non-invasive technique to study both inorganic and organic teeth components in vitro. The aim of this narrative review is to shed new light on the applicative potential of Raman Microspectroscopy in the dental field. Specific Raman markers representative of sound and pathological hard dental tissues will be discussed, and the future diagnostic application of this technique will be outlined. The objective and detailed information provided by this technique in terms of the structure and chemical/macromolecular components of sound and pathological hard dental tissues could be useful for improving knowledge of several dental pathologies. Scientific articles regarding RMS studies of human hard dental tissues were retrieved from the principal databases by following specific inclusion and exclusion criteria.

An experimental study on emission of a diesel engine fuelled with SME (safflower methyl ester) and diesel fuel

In this study, safflower methyl ester (SME), which is an alternative fuel, was obtained from safflower seed oil (SO) for a CI engine by using transesterification method. The use of the produced fuel in an air-cooled single-cylinder diesel engine was investigated. The obtained SME was blended with diesel fuel at the rate of 20% and 50% (vol.) and also 100% SME was used. BSFC, NOX, CO, smoke density, HC, EGT, BTE, vibration and thermal image analyses were made in the test engines. In the first stage, diesel and SME fuel mixtures were used in the test engine volumetrically. In the second stage, the experiments were repeated by coating the combustion chamber elements of the same engine with chromium oxide by plasma spray method. It was determined that BSFC, CO, smoke and HC decreased but NOX, BTE, EGT and vibration increased in all test fuels in the coated engine compared to the standard engine. CO, HC, and smoke emission values of SME and its mixtures decreased except for BTE, BSFC, and NOX compared to diesel fuel. Safflower seeds can be grown easily in many parts of the world and can be considered as an alternative fuel for diesel engines.

Classification of Chaotic Squeak and Rattle Vibrations by CNN Using Recurrence Pattern.

The chaotic squeak and rattle (S&R) vibrations in mechanical systems were classified by deep learning. The rattle, single-mode, and multi-mode squeak models were constructed to generate chaotic S&R signals. The repetition of nonlinear signals generated by them was visualized using an unthresholded recurrence plot and learned using a convolutional neural network (CNN). The results showed that even if the signal of the S&R model is chaos, it could be classified. The accuracy of the classification was verified by calculating the Lyapunov exponent of the vibration signal. The numerical experiment confirmed that the CNN classification using nonlinear vibration images as the proposed procedure has more than 90% accuracy. The chaotic status and each model can be classified into six classes.

Post-Processing Deflectometry Grid Images Using Particle Image Velocimetry Analysis

AbstractDeflectometry is a full-field optical technique for surface slope measurement based on recording the deformation of a grid image. A hybrid method is explored in which the grid images from a deflectometry measurement are processed using a particle image velocimetry analysis tool. The hybrid approach is compared to a common phase shifting algorithm for grid images based on a windowed discrete Fourier transform. The resulting slope maps compare well with those identified using the spatial phase shifting procedure. While the traditional phase shifting method has a tuning requirement that limits the optical setup to configurations that produce an integer number of pixels per grid period in the image, the use of particle image velocimetry analysis omits this calibration step. The applicability of an existing turnkey tool to perform full-field vibration imaging using deflectometry can benefit to research concerning mechanical vibration and related experimental methods.

Vibrational imaging for label-free cancer diagnosis and classification

The first key step in the detection and classification of most cancers is the microscopic assessment of thin tissue slices, the so-called “histopathology”. This procedure is still nowadays, similarly to 150 years ago, performed by staining the tissue with two or more dyes able to bind to specific biological structures, followed by visual inspection by the histopathologist under the bright-field optical microscope. This approach involves long manual procedures which can be accompanied by human errors, subjectivity, and lack of reproducibility. Vibrational microscopies are capable of directly providing chemical and biomolecular information on tissues, identifying them through their fingerprint vibrational spectra without the need of staining and, thus, constitute powerful tools for label-free and objective tumour identification. The two most established techniques, spontaneous Raman microscopy and infrared absorption microscopy, suffer, respectively, from long acquisition times and low spatial resolution. These limitations can be overcome by novel and more technically demanding approaches such coherent Raman scattering and photothermal infrared microscopy. Here we present an extended overview of the major advances in the field of vibrational imaging for cancer diagnosis. We start from a detailed description of the different technologies and then present examples of their applications to tissue imaging for cancer assessment. We critically compare the presented approaches, discussing the steps required to bring these powerful technologies from bench to bedside.

Vibrational spectroscopy analysis of silica and silicate glass networks

AbstractVibrational spectroscopy has been widely used to investigate various structural aspects of the glass network, and there are a plethora of papers reporting subtle but consistent changes in infrared and Raman spectral features of glass upon alterations of glass compositions, thermal histories, mechanical stresses, or surface treatments. However, interpretations of such spectral features are still obscured due to the lack of well‐established physical principles accurately describing vibrational modes of the non‐crystalline glass network. Due to the non‐equilibrium nature of the glass network, three‐dimensionally connected without any long‐range orders, vibrational spectral features of glass cannot be interpreted using the analogy with those of isolated molecular moieties or crystalline counterparts. This feature article explains why such comparisons are outdated and describes the recent advances made from theoretical calculations of vibrational spectral features of amorphous networks or comparisons of computational results with experimental data. For the interpretation of vibrational spectral features of silica and silicate glasses, the following empirical relationships are suggested: (i) the intensity‐weighted peak position of the Si‐O‐Si stretch mode negatively correlates with the weighted average of the Si‐O bond length distribution, and (ii) the broad band of the Si‐O‐Si bending mode negatively correlates with the Si‐O‐Si bond angle distribution. Selected examples of vibrational spectroscopic imaging of surface defects are discussed to deliberate the implication of these findings in the structure‐property relationship of silica and silicate glass materials. Unanswered questions and continuing research challenges are identified.

Wide-Field Mid-Infrared Hyperspectral Imaging by Snapshot Phase Contrast Measurement of Optothermal Excitation.

Vibrational microscopy methods based on Raman scattering or infrared absorption provide a label-free approach for chemical-contrast imaging, but employ point-by-point scanning and impose a compromise between the imaging speed and field-of-view (FOV). Optothermal microscopy has been proposed as a promising imaging modality to avoid this compromise, although at restrictively small FOVs capable of imaging only few cells. Here, we present wide-field optothermal mid-infrared microscopy (WOMiM) for wide-field chemical-contrast imaging based on snapshot pump–probe detection of optothermal signal, using a custom-made condenser-free phase contrast microscopy to capture the phase change of samples after mid-infrared irradiation. We achieved chemical contrast for FOVs up to 180 μm in diameter, yielding 10-fold larger imaging areas than the state-of-the-art, at imaging speeds of 1 ms/frame. The maximum possible imaging speed of WOMiM was determined by the relaxation time of optothermal heat, measured to be 32.8 μs in water, corresponding to a frame rate of ∼30 kHz. This proof-of-concept demonstrates that vibrational imaging can be achieved at an unprecedented imaging speed and large FOV with the potential to significantly facilitate label-free imaging of cellular dynamics.

Image of the distribution profile of targets in skin by Raman spectroscopy-based multivariate analysis.

Raman spectroscopic imaging is a label-free spectral technology to investigate the distribution of transdermal targets in skin. However, it is difficult to analyze low content of analytes in skin by direct imaging analysis. Combining Raman mapping technology with multiple linear regression algorithms, concentration contribution factor of targets in ex vivo human skin tissue at every point has been calculated. The distribution profiles are visualized as heat maps demonstrating the targets levels in different skin layers. This method has been successfully employed to investigate the vibrational imaging of distribution of hyaluronic acid and lidocaine in skin. Moreover, three dimensional (3D) images of the penetration profiles of hyaluronic acid with different molecular weight have been obtained. The results from 3D images were in good agreement with these from two-dimensional images, indicating that this method was a reliable way for monitoring the distribution of targets in skin.

Intelligent worm gearbox fault diagnosis under various working conditions using vibration, sound and thermal features

Worm gearboxes (WG) are frequently used in many areas of the industry. WG is different from other gearbox types and due to their working principles they are under high risk of wear and fault. Therefore, detection of faults that may occur in WG and taking measures accordingly are especially important for systems and facilities that require uninterrupted operation. This paper introduces an intelligent feature selection and classification method for fault diagnosis of WGs under different working conditions. The novelty of the study lies in the selection of feature sources and different loading and speed conditions for condition monitoring studies of WGs experimentally. Fault detection and classification were performed based on vibration, sound and thermal images data which were acquired and processed from the healthy and the faulty WG. Apart from classical studies, time and frequency domain features and thermal images features were extracted and evaluated singularly, dual or triple forms with ANN (Artificial Neural Network) and SVM (Support Vector Machines) classifiers. Reasonable classification performances for fault detection were observed when the features of all three sources used (99.2% with ANN and 98.7% with SVM). ANN and SVM classification performances are almost equal for fault classification (98.9% with SVM and 98.6% with ANN). The findings of the study would be a possible means for online condition monitoring of industrial plants, conveyors, elevators, etc.