Light-scattering Patterns Research Articles

Detecting water-soaked disorder and reddish-pulp disorder in peach fruit using bio-speckle

This study addresses the challenges in nondestructive identifying diseases, particularly water-soaked and reddish-pulp disorders, in peaches during storage and transport. Existing technologies have struggled to detect these diseases during this period, leading to potential food loss and consumer distrust. Biospeckle has emerged as a promising discriminator of the internal state of the fruit by utilizing laser-induced scattered light patterns. Pigment interference is minimized by employing lasers with wavelengths of 532 and 650 nm. This study focuses on the ability of biospeckle to distinguish between healthy and diseased fruit based on characteristic values, specifically the Fujii index and cumulative amplitude (Cum. amp.) at 2–3, 3–4, 4–5, and 6–7 Hz. The t-test results demonstrated significant differences in these values, particularly for water-soaked and reddish-pulp disorders. Biospeckle outperforms other non-destructive methods by identifying the symptoms pre-storage. These results indicate that Cum. amp. at 3–4, 4–5, and 6–7 Hz may be more useful in identifying water-soaked fruit than the Fujii index and Cum. amp. at 2–3 Hz. Red lasers are more effective in detecting reddish-pulp disorders than green lasers, which are hindered by pigment absorption. This finding highlights the potential of biospeckle in precise symptom identification, which is crucial for ensuring food quality and consumer confidence.

Rheo-optics of giant micelles: SALS patterns of cetyltrimethylammonium tosylate solutions in presence of sodium bromide

In this work, we present a systematic study based on Small-Angle Light Scattering (SALS) patterns of the simple shear flow response of semi-diluted solutions of cetyltrimethylammonium tosylate (CTAT; 5.5 wt.% - 0.12 M) in the presence of sodium bromide (NaBr) at different [NaBr]={0,0.12,0.19,0.25,0.3} M concentrations. We evidence a relationship between rheological and light scattering data that reveals a transition into a fast-breaking regime in the dynamics of wormlike micelles formed by the CTAT/NaBr system (Macías et al., 2011; Fierro et al., 2021). This transition into a micellar fast-breaking regime with salt addition ([NaBr]≥0) appears marked by the following features: (i) a decrease in the relaxation time of the material λ0, accompanied by (ii) a decrease of the viscosity level at low shear rates η0 (Macías et al., 2011; Fierro et al., 2021; Schubert et al., 2003; Alkschbirs et al., 2015; Bandyopadhyay et al., 2003). With these, (iii) the formation of butterfly-like patterns is recorded originating from concentration fluctuations, evolution that is accompanied by: (iv) shear banding in the form of non-monotonic flow curves and (v) slow oscillatory transient responses in start-up flow tests captured theoretically with the Bautista–Manero–Puig (BMP) model. In addition, the Cox–Merz rule is fulfilled at molar salt-to-surfactant ratios of R≥1.5. This results in shorter structure-recovery time-scales than the characteristic-time of the flow (Macías et al., 2011; Fierro et al., 2021; Manero et al., 2002). In the case of the elastic modulus G0, the variation was small, which suggests a transition from an entangled to a multiconnected network, as suggested by Kadoma & van Egmond (1997), Kadoma et al. (1997) and Fierro et al. (2021). From a theoretical perspective, we provide predictions for the shear–stress and the first normal-stress growth coefficients in transient start-up simple shear flow using the BMP model. Here, banding R=1.5 solutions display overshot responses at relatively high shear rates (γ̇=10−30s−1), in-line with experimental findings on the start-up flow of wormlike micellar solutions (Soltero et al., 1999; Lerouge et al., 2004; Hu & Lips 2005; Pipe et al., 2010; Mohammadigoushki et al., 2019). Our results are consistent with those reported in other investigations (Macías et al., 2011; Fierro et al., 2021; Schubert et al., 2003; Alkschbirs et al., 2015; Bandyopadhyay et al., 2003; Kadoma et al., 1997; Soltero et al., 1999; Lerouge et al., 2004; Hu & Lips 2005; López-Barrón et al., 2014) and reveal the influence of the Br−-ion used on the mechanical and optical response of the CTAT-NaBr system.

Classification of Ice Particle Shapes Using Machine Learning on Forward Light Scattering Images

Abstract Machine learning (ML) has rapidly transitioned from a niche activity to a mainstream tool for environmental research applications including atmospheric science cloud microphysics studies. Two recently developed cloud particle probes measure the light scattered in the near forward direction and save digital images of the scattering light. Scattering pattern images collected by the Particle Phase Discriminator mark 2, Karlsruhe edition (PPD-2K), and the Small Ice Detector, version 3 (SID-3) provide valuable information for particle shape and size characterization. Since different particle shapes have distinctly different light scattering characteristics, the images are ideally suited for ML. Here, results of a ML project to characterize ice particle shapes sampled by the PPD-2K in ice fog and diamond dust during a 3-yr project in Fairbanks, Alaska. About 2.15 million light-scattering pattern images were collected during 3 years of measurements with the PPD-2K. Visual Geometry Group (VGG) convolutional neural network (CNN) was trained to categorize light-scattering patterns into eight categories. Initial training images (120 each category) were selected by human visual examination of data, and the training dataset was augmented using an automated iterative method for image identification of further images which were all visually inspected by a human. Results were well correlated to similar categories identified from previously developed classification algorithms. ML identifies characteristics not included in automated analysis such as sublimation. Of the 2.15 million images analyzed, 1.3% were categorized as spherical (liquid), 43.5% were categorized as having rough surfaces, 15.3% were pristine, 16.3% were categorized as sublimating, and the remaining 23.6% did not fit into any of those categories (irregular or saturated). Significance Statement The shapes and sizes of cloud particles can be extremely important for understanding the conditions that exist in the cloud. In this study, we show that more information about cloud particle characteristics can be identified by using machine learning than by traditional means. To demonstrate this, data are analyzed from a 3-yr study of ice fog and diamond dust events in Fairbanks, Alaska. The Particle Phase Discriminator instrument collected 2.15 million light-scattering pattern images of cloud particles during ground-based measurements. Neither traditional techniques nor machine learning were able to identify all categories, but a combination of both techniques led to a more complete view of ice particle shapes.

Diffusivities in water or aqueous solutions of sodium chloride with dissolved hydrogen and methane by dynamic light scattering

Knowledge of diffusion in water or aqueous solutions of sodium chloride with dissolved hydrogen and methane is important for geological hydrogen storage. This study examines the accessibility of diffusivities in the corresponding mixtures through dynamic light scattering at macroscopic thermodynamic equilibrium and provides corresponding data for hydrogen-storage-relevant temperatures T up to 393 K and pressures up to 20 MPa. The thermal and mutual diffusivities are simultaneously accessible in mixtures of water and hydrogen. Moreover, for water with dissolved mixtures of hydrogen and methane, the thermal diffusivity and an apparent diffusivity related to the diffusion of the gas mixture can be accessed. In aqueous solutions of sodium chloride at T > 300 K, a strong molecular association leading to a quasi-static light-scattering pattern in the liquid sample was observed, which hindered the determination of the molecular gas diffusivities. However, the salt diffusion coefficient was accessible for all T values.

Synthesis of hot spring origin bacterial cell wall polysaccharide-based copper nanoparticles with antibacterial property

BackgroundAt present, research on facile, green synthesis of nanoparticles has significantly increased because of its fast, one-step, cost-effective, time-efficient, and non-toxic nature. In this study, we have reported a single-step green synthesis of copper nanoparticles using cell wall polysaccharides of a hot spring origin, thermotolerant Bacillus species. ResultCopper nanoparticles were characterized using UV-visible spectrophotometry, fluorescence and Fourier transform infrared spectroscopy, scanning electron microscopy with energy dispersive spectroscopy, particle size, and zeta potential analyses. UV-visible spectra of synthesized copper nanoparticles exhibited a band cantered between 220–235 nm, characteristic spectra of copper oxide nanoparticles. Infrared spectra showed the band at 490-530 cm−1 corresponding to metal-oxygen or copper nanoparticle vibration, supporting the presence of copper oxide nanoparticles in the monoclinic phase. The energy dispersive spectra of copper nanoparticles exhibited a strong signal from elemental copper. The dynamic Light Scattering pattern confirmed the nanoparticle nature of the studied sample. These nanoparticles showed preferential activity against gram-negative pathogens, Salmonella typhi and Escherichia coli. The thermodynamic nature of the nanoparticles is also established for its antibacterial actions. ConclusionsThe antibacterial action and its thermodynamics reinforce the possible use of copper nanoparticles as an alternative to commercially available antimicrobials. This study may open a new path for future studies to treat harmful microorganisms resistant to traditional antibiotics in a greener way.How to cite: Banerjee A, Roy RK, Sarkar S, et al. Synthesis of hot spring origin bacterial cell wall polysaccharide-based copper nanoparticles with antibacterial property. Electron J Biotechnol 2024;68. https://doi.org/10.1016/j.ejbt.2023.11.005.

Nonclassical Crystallization Causes Dendritic and Band-Like Microscale Patterns in Inorganic Precipitates.

The self-organization of complex solids can create patterns extending hierarchically from the atomic to the macroscopic scale. A frequently studied model is the chemical garden system which consists of life-like precipitate shapes. In this study, we examine the thin walls of chemical gardens using microfluidic devices that yield linear Ni(OH)2 precipitate membranes. We observe distinct light-scattering patterns within the compositionally pure membranes, including disorganized spots, dendrites, and parallel bands. The bands are tilted with respect to the membrane axis and their spacing (20-100 μm) increases with increasing flow rates. Scanning electron microscopy reveals that the bands consist of submicron particles embedded in a denser material and these particles are also found in the reactant stream. We propose that dendrites and bands arise from the attachment of solution-borne nanoparticles. The bands are generated by particle-aggregation zones moving upstream along the slowly advancing membrane surface. The speed of the aggregation zones is proportional to the band distance and defines the system's dispersion relation. This speed-wavelength dependence and the flow-opposing motion of the aggregation zones are likely caused by low particle concentrations in the wake of the zones that only slowly recover due to Brownian motion and particle nucleation.

Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns.

The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473-709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%.

Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy

Pollen monitoring have become data-intensive in recent years as real-time detectors are deployed to classify airborne pollen grains. Machine learning models with a focus on deep learning, have an essential role in the pollen classification task. Within this study we developed an explainable framework to unveil a deep learning model for pollen classification. Model works on data coming from single particle detector (Rapid-E) that records for each particle optical fingerprint with scattered light and laser induced fluorescence. Morphological properties of a particle are sensed with the light scattering process, while chemical properties are encoded with fluorescence spectrum and fluorescence lifetime induced by high-resolution laser. By utilizing these three data modalities, scattering, spectrum, and lifetime, deep learning-based models with millions of parameters are learned to distinguish different pollen classes, but a proper understanding of such a black-box model decisions demands additional methods to employ. Our study provides the first results of applied explainable artificial intelligence (xAI) methodology on the pollen classification model. Extracted knowledge on the important features that attribute to the predicting particular pollen classes is further examined from the perspective of domain knowledge and compared to available reference data on pollen sizes, shape, and laboratory spectrofluorometer measurements.

Retrieving refractive index of single spheres using the phase spectrum of light-scattering pattern

We analyzed the behavior of the complex Fourier spectrum of the angle-resolved light scattering pattern (LSP) of a sphere in the framework of the Wentzel–Kramers–Brillouin (WKB) approximation. Specifically, we showed that the phase value at the main peak of the amplitude spectrum almost quadratically depends on the particle refractive index, which was confirmed by numerical simulations using both the WKB approximation and the rigorous Lorenz–Mie theory. Based on these results, we constructed a method for characterizing polystyrene beads using the main peak position and the phase value at this point. We tested the method both on noisy synthetic LSPs and on the real data measured with the scanning flow cytometer. In both cases, the spectral method was consistent with the reference non-linear regression one. The former method leads to comparable errors in retrieved particle characteristics but is 300 times faster than the latter one. The only drawback of the spectral method is a limited operational range of particle characteristics that need to be set a priori due to phase wrapping. Thus, its main application niche is fast and precise characterization of spheres with small variation range of characteristics.

Rheological studies of molecular effect and processing conditions on blown film property of polyethylenes

Statistically designed blown film experiments were conducted to investigate the effects of molecular weight distribution and processing conditions on film properties of three linear low-density polyethylenes in term of their rheological characteristics. The complex interactions between molecular and processing parameters to film properties can be understood rheologically by relating the ratio of polymer largest molecular relaxation time, τmax, to a process time, tp. When represented in term of the rescaled processing time, τmax/tp, namely as Deborah Number (De), a master curve is obtained for the physical properties of these films blown under similar processing condition, while the variations of processing conditions on film properties can be characterised by families of De master-curves. The influence of complex thermo-mechanical deformation history on crystallisation during blown film processing can be highlighted with complementary pre-shear flow stress relaxation and flow-induced crystallisation experiments by Small Angle Light Scattering patterns.

In situ optical sensor for aerosol ovality and size

Sensors for in situ detection of aerosols using conventional optical scattering methods generally fail to identify the types and sources of particles, and they even have a large discrepancy in particle size measurements because the particles are overly simplistically assumed to be spherical. To address this issue, we investigate the effects of particle shape on light-scattering patterns and develop a portable sensor that can simultaneously determine the particle shape and size. To describe all particles, particularly irregular particles with a generalization parameter, we define ovality to represent the properties of particle shape, which is a distinctive feature related to particle type and source. Furthermore, we analyze the multi-angle light-scattering patterns that vary for particles of different shapes and sizes. On this basis, a sensing method is proposed to simultaneously determine the ovality and size of particles by jointly assessing the light-scattering pattern features. Finally, an 8-channel sensor is developed and tested with di-ethyl-hexyl-sebacate (DEHS) aerosol, Duke particles, and N-Heptane combustion smoke. The DEHS aerosols are used for the calibration of our sensor. The average error of the shape and size of Duke particles determined by the experimental measurements of our sensor is 4.01 %. The measurement error of N-Heptane compared to an ellipsoid with an ovality of 15:1 is 5.84 %. The experimental results show that our sensor has the potential to measure the properties of particle shape to identify the types and formation mechanisms of aerosols. • The effects of particle shape on light-scattering patterns are investigated. • The conventional calculation algorithm is improved according to the geometric properties of the shape determined by our sensing method, for combustion smoke and other examples that deviate greatly from the spherical shape. • A portable sensor that can simultaneously determine the particle properties and size is developed. • Extensive experiments are repeated for proving that the developed sensor is highly portable and suitable for aerosol measurement.

Differentiating single cervical cells by mitochondrial fluorescence imaging and deep learning-based label-free light scattering with multi-modal static cytometry.

Cervical cancer is a high-risk disease that threatens women's health globally. In this study, we developed the multi-modal static cytometry that adopted different features to classify the typical human cervical epithelial cells (H8) and cervical cancer cells (HeLa). With the light-sheet static cytometry, we obtain brightfield (BF) images, fluorescence (FL) images and two-dimensional (2D) light scattering (LS) patterns of single cervical cells. Three feature extraction methods are used to extract multi-modal features based on different data characteristics. Analysis and classification of morphological and textural features demonstrate the potential of intracellular mitochondria in cervical cancer cell classification. The deep learning method is used to automatically extract deep features of label-free LS patterns, and an accuracy of 76.16% for the classification of the above two kinds of cervical cells is obtained, which is higher than the other two single modes (BF and FL). Our multi-modal static cytometry uses a variety of feature extraction and analysis methods to provide the mitochondria as promising internal biomarkers for cervical cancer diagnosis, and to show the promise of label-free, automatic classification of early cervical cancer with deep learning-based 2D light scattering.

Measurement of laser powder bed fusion surfaces with light scattering and unsupervised machine learning

Quality monitoring for laser powder bed fusion (L-PBF), particularly in-process and real-time monitoring, is of importance for part quality assurance and manufacturing cost reduction. Measurement of layer surface topography is critical for quality monitoring, as any anomaly on layer surfaces can result in defects in the final part. In this paper, we propose a surface measurement method, based on the use of scattered light patterns and a convolutional autoencoder-based unsupervised machine learning method, designed and trained using a large set of scattering patterns simulated from reference surfaces using a scattering model. The advantage of using an autoencoder is that the monitoring model can be trained using solely data from acceptable surfaces, without the need to ensure the presence of representative observations for all the types of possible surface defects. The advantage of using simulated data for training is that we can obtain an effective monitoring solution without the need for a large collection of experimental observations. Here we report the results of a preliminary investigation on the performance of the proposed solution, where the trained autoencoder is tested on experimental data obtained off-process, using a dedicated experimental apparatus for generating and collecting light scattering patterns from manufactured L-PBF surfaces. Our results indicate that the proposed monitoring solution is capable of detecting both acceptable and anomalous surfaces. Although further validation is required to fully assess performance within an on-machine and in-process setup, our preliminary results are encouraging and provide a glimpse of the potential benefits of using our surface measurement solution for L-PBF in-process monitoring.

Measurements of elastic light-scattering patterns and images of single, oriented, optically trapped particles

We demonstrate a method for recording 2D forward-scattering patterns from optically trapped single airborne particles at multiple angular orientations. We collected images of trapped particles simultaneously with their scattering patterns to observe changes in the patterns with orientation. We present measurements of particles with wide-ranging morphologies from nearly spherical to highly irregular and with various compositions. The highly irregularly shaped particles have scattering patterns with spots of high intensity whose locations change with particle orientation. Additionally, measurements of a manufactured polystyrene spheroid of a known size are compared with calculated diffraction patterns. Such controlled-orientation measurements have the potential to help develop algorithms to characterize airborne aerosol particles using morphological information inferred from remote-sensing or other elastic light scattering measurements.

Rigorous analysis of the spectral sizing of single particles based on light scattering patterns

The sizing of a single particle based on the Fourier spectrum of its angle-resolved light-scattering pattern (LSP) is widely used in practice but has been lacking rigorous theoretical justification. In this paper we fill this gap, starting with systematic analysis based on the Rayleigh-Gans-Debye (RGD) approximation. We related the LSP spectrum with an integrated autocorrelation function (IAF), determined solely by the particle geometry. If the exact forward scattering is not included in the LSP angular range and the Hann window is used for the Fourier transform, the LSP spectrum vanishes everywhere except near the discontinuities of the IAF derivatives. Thus, the main spectral peak corresponds to the particle diameter, i.e. largest distance between two interior points. Next, we showed that the same qualitative conclusions hold in the Wentzel-Kramers-Brillouin (WKB) approximation and in the general case, when no approximations are employed. In the case of the WKB, we proposed a change of the LSP argument to simplify the description of the resulting peak.

Trapping light in a Floquet topological photonic insulator by Floquet defect mode resonance

Floquet topological photonic insulators characterized by periodically varying Hamiltonians are known to exhibit much richer topological behaviors than static systems. In a Floquet insulator, the phase evolution of the Floquet–Bloch modes plays a crucial role in determining its topological behaviors. Here, we show that by perturbing the driving sequence, it is possible to manipulate the cyclic phase change in the system over each evolution period to induce self-interference of a bulk mode, leading to a resonance effect, which can be regarded as a Floquet counterpart of defect-mode resonance in static lattices. This Floquet Defect Mode Resonance (FDMR) is cavity-less since it does not require physical boundaries; its spatial localization pattern is, instead, determined by the driving sequence and is found to be different in topologically trivial and nontrivial lattices. We demonstrated excitation of FDMRs by edge modes in a Floquet octagon lattice on silicon-on-insulator, achieving extrinsic quality factors greater than 104. Imaging of the scattered light pattern directly revealed the hopping sequence of the Floquet system and confirmed the spatial localization of FDMR in a bulk-mode loop. The new Floquet topological resonator could find various applications in lasers, optical filters and switches, nonlinear cavity optics, and quantum optics.

Automatic particle detectors lead to a new generation in plant diversity investigation

Technological progress in modern scientific development generates opportunities that create new ways to learn more about objects and systems of nature. An important indicator in choosing research methods is not only accuracy but also the time and human resources required to achieve results. This research demonstrates the possibilities of using an automatic particle detector that works based on scattered light pattern and laser-induced fluorescence for plant biodiversity investigation. Airborne pollen data were collected by two different devices, and results were analysed in light of the application for plant biodiversity observation. This paper explained the possibility to gain knowledge with a new type of method that would enable biodiversity monitoring programs to be extended to include information on the diversity of airborne particles of biological origin. It was revealed that plant conservation could be complemented by new tools to test the effectiveness of management plans and optimise mitigation measures to reduce impacts on biodiversity.

Measuring Droplets Expelled During Endoscopy to Investigate COVID-19 Transmission Risk

Measuring Droplets Expelled During Endoscopy to Investigate COVID-19 Transmission Risk

Multispectral small-angle light scattering from particles.

Using a supercontinuum laser, reflective optics, and a spatial filter, we measure two-dimensional small-angle light-scattering patterns for a variety of microparticles including spheres, salt, sand, and volcanic dust. The measurements are done at 13 wavelengths from 450-850 nm, where the absence of refractive optical elements minimizes the effects of chromatic aberration. Qualitative particle-material sensitivity is demonstrated by layering differently colored patterns. Last, the multispectral capability of our device demonstrates a new possibility to probe different q-space regimes for a given particle in a single measurement.

An integrated electro-optical biosensor system for rapid, low-cost detection of bacteria

In medical treatment, the detection of pathogens at an early stage of diseases is a key step to set up an appropriate diagnosis. To reach this goal, several techniques have been elaborated for point-of-care diagnostic applications. One of the state-of-the art methods is the application of biosensor devices. Label-free versions of them ensure an appropriate detection of pathogens from fluid samples by their relative sensitivity, rapidity and portability, thus offering a feasible and affordable alternative to the traditional diagnostic techniques. The aim of the present study is to fulfill these requirements with a cheap construction of an electro-optical biosensor, for application as a rapid test in clinical diagnostics. Hence, an integrated microsystem consisting of dielectrophoretic surface-electrodes, a rib waveguide and a microfluidic channel was created for label-free optical detection of bacteria from fluid samples. To model the efficiency of the sensor, we carried out quantitative measurements by observing the light scattered by living Escherichia coli cells located in the vicinity of the waveguide. A significant change in the scattered light pattern was observed even when objectives of moderate magnification (x10, x4.7) were used, implying that such type of sensing of the cells can be achieved by low-cost cameras, as well. The optimal frequency utilized in the process of dielectrophoretic cell-collecting was also established. With this novel system, a detection limit of ca. 102CFU × mL−1 was achieved, which is relevant to characteristic pathogen concentrations in body fluids, e.g., urine. Our further plan is to utilize this cell-gathering method in other, highly sensitive integrated optical sensor constructions, as well. The working principle of this dielectrophoretically enhanced detection of Escherichia coli cells from their suspensions gives us a low-cost and rapid-sensing alternative to routinely used, but time- and money-consuming other methods. Hence, we expect it to be readily applicable in point-of-care diagnostics as a basis of rapid tests to identify general pathogens from various body fluids.