Scattering Spectra Research Articles

Path shadowing Monte Carlo

We introduce a Path Shadowing Monte Carlo method, which provides the prediction of future paths, given any generative model. At any given date, it averages future quantities over generated price paths whose past history matches, or ‘shadows’, the actual (observed) history. We test our approach using paths generated from a maximum entropy model of financial prices, based on a recently proposed multi-scale analogue of the standard skewness and kurtosis called ‘Scattering Spectra’. This model promotes the diversity of generated paths while reproducing main statistical properties of financial prices, including stylized facts such as volatility roughness. Our method yields state-of-the-art predictions for future realized volatility and allows one to determine conditional option smiles for the S&P500 that outperform both the most recent low-parametric models and the option market itself. The code is available at https://github.com/RudyMorel/shadowing (This work is supported by the PRAIRIE 3IA Institute of the French ANR-19-P3IA-0001 program and the ENS-CFM models and data science chair.).

Estimation of red blood cell aggregation size using power spectrum considering density of red blood cells in focal region

We have studied a noninvasive and quantitative method for measuring the aggregate size of red blood cells (RBCs). In our previous study, the scattering power spectrum obtained from the vascular lumen was fitted to the reference scattering spectra calculated from the scattering spectrum of a single-sphere scatterer. In this paper, we propose a method for calculating the reference scattering spectra by summing the scattering spectra for numerous scatterers in the ultrasound focal region. By applying this method to the size estimation of microparticles simulating RBC aggregates, the estimated sizes are found to be close to the true values. In in vivo measurements, the estimated sizes at rest are equivalent to the size of a single sphere with the same volume as an RBC, and those during avascularization are larger than those at rest, which is reasonable. The proposed method has the potential to accurately estimate the size of RBC aggregates.

Fabrication of core-shell-like bimetallic SERS substrates with inter-coordination effect for catalysis of p-mercaptoaniline.

The present study discusses the core-shell structures of Au@Ag prepared by a seed-growth method. The morphology and composition of Au@Ag nanoparticles were analyzed, indicating that they were successfully prepared. By studying the surface-enhanced Raman scattering spectra (SERS) of p-mercaptoaniline (PATP) molecules adsorbed on Au@Ag substrates, it was found that PATP molecules could be oxidized to form p-mercaptoazobenzene (DMAB) on Au@Ag substrates, indicating that the gold-silver bimetallic nanomaterials could catalyze the PATP molecules with excellent enhancement effect and stability. In order to further study the enhancement effect of the Au@Ag substrate, the electric field strength of Au nanoparticles and Au@Ag nanoparticles was simulated by using the finite difference time domain (FDTD) method, which showed that the SERS enhancement effect of Au@Ag nanoparticles was more significant as well as consistent with the experimental results. This work provides a reference for further preparation of efficient and stable bimetallic SERS substrates.

On the Measurements of the Surface-Enhanced Raman Scattering Spectrum: Effective Enhancement Factor, Optical Configuration, Spectral Distortion, and Baseline Variation

In this paper, a comprehensive theoretical framework for understanding surface-enhanced Raman scattering (SERS) measurements in both solution and thin-film setups, focusing on electromagnetic enhancement principles, was presented. Two prevalent types of SERS substrates found in the literature were investigated: plasmonic colloidal particles, including spherical and spheroid nanoparticles, nanoparticle diameters, and thin-film-based SERS substrates, like ultra-thin substrates, bundled nanorods, plasmonic thin films, and porous thin films. The investigation explored the impact of analyte adsorption, orientation, and the polarization of the excitation laser on effective SERS enhancement factors. Notably, it considered the impact of analyte size on the SERS spectrum by examining scenarios where the analyte was significantly smaller or larger than the hot spot dimensions. The analysis also incorporated optical attenuations arising from the optical properties of the analyte and the SERS substrates. The findings provide possible explanations for many observations made in SERS measurements, such as variations in relative peak intensities during SERS assessments, reductions in SERS intensity at high analyte concentrations, and the occurrence of significant baseline fluctuations. This study offers valuable guidance for optimizing SERS substrate design, enhancing SERS measurements, and improving the quantification of SERS detection.

A time-dependent momentum-resolved scattering approach to core-level spectroscopies

While new light sources allow for unprecedented resolution in experiments with X-rays, a theoretical understanding of the scattering cross-section is lacking. In the particular case of strongly correlated electron systems, numerical techniques are quite limited, since conventional approaches rely on calculating a response function (Kramers-Heisenberg formula) that is obtained from a perturbative analysis of scattering processes in the frequency domain. This requires a knowledge of a full set of eigenstates in order to account for all intermediate processes away from equilibrium, limiting the applicability to small tractable systems. In this work, we present an alternative paradigm, recasting the problem in the time domain and explicitly solving the time-dependent Schrödinger equation without the limitations of perturbation theory: a faithful simulation of the scattering processes taking place in actual experiments, including photons and core electrons. We show how this approach can yield the full time and momentum resolved Resonant Inelastic X-Ray Scattering (RIXS) spectrum of strongly interacting many-body systems. We demonstrate the formalism with an application to Mott insulating Hubbard chains using the time-dependent density matrix renormalization group method, which does not require a priory knowledge of the eigenstates and can solve very large systems with dozens of orbitals. This approach can readily be applied to systems out of equilibrium without modification and generalized to other spectroscopies.

Fast Feature Extraction Method for Brillouin Scattering Spectrum of OPGW Optical Cable Based on BOTDR.

Brillouin optical time domain reflectometry (BOTDR) detects fiber temperature and strain data and represents one of the most critical ways of identifying abnormal conditions such as ice coverage and lightning strikes on optical fiber composite overhead ground wire (OPGW) cable. Existing BOTDR extracts brillouin frequency shift (BFS) features with cumulative averaging and curve fitting. BFS feature extraction is slow for long-distance measurements, making realizing real-time measurements on fiber optic cables challenging. We propose a fast feature extraction method for block matching and 3D filtering (BM3D) + Sobel brillouin scattering spectroscopy (BGS). BM3D takes the advantage of non-local means (NLM) and wavelet denoising (WD) and utilizes the spatial-domain non-local principle to enhance the denoising in the transform domain. The global filtering capability of BM3D is utilized to filter out the low cumulative average BGS noise and the BFS feature extraction is completed using Sobel edge detection. Simulation verifies the feasibility of the algorithm, and the proposed method is embedded in BOTDR to measure 30 km of actual OPGW line. The experimental results show that under the same conditions, the processing time of this method is reduced by 37 times compared to that with the 50,000-time cumulative averaging + levenberg marquardt (LM) algorithm without severe distortion of the reference resolution. The method improves the sensor demodulation speed by using image processing technology without changing the existing hardware equipment, which is expected to be widely used in the new generation of BOTDR.

Autonomous screening of complex phase spaces using Bayesian optimization for SAXS measurements

The advent of modern, ultrafast X-ray experiments has enabled scientists to probe physical phenomena at an ever smaller scale. However, this has come at a cost of excessive data generation, to the point where current storage and hardware capabilities are routinely surpassed. As such, handling the data efficiently and selectively storing only the information of most relevance is crucial. In this paper, we propose to use Bayesian optimization as a method to alleviate this problem. We apply the method to locate global features in Small Angle X-ray Scattering spectra obtained from conducting experiments with supercritical CO2. By evaluating the algorithm on more than 250 data points, we show that the implementation is versatile, robust, and computationally efficient, often converging with just a few iterations and with a minimal error penalty. This paves the way for creating fully autonomous experiments, where data science algorithms such as the one presented herein operate hand-in-hand with the expert user to maximize scientific discovery and minimize the associated experimental cost.

Electromagnetic Scattering and Doppler Spectrum Simulation of Land–Sea Junction Composite Rough Surface

In this paper, a weighted arctangent function is used in conjunction with the spectral method to generate a land–sea junction composite rough surface under the spatially homogeneous and time-stationary hypotheses. The exponential correlation function and the Joint North Sea Wave Project (JONSWAP) spectrum, combined with an experiment-verified shoaling coefficient, are applied to model the land surfaces and the time-varying sea surfaces separately. The second-order small slope approximation (SSA-II) with tapered wave incidence is utilized for evaluating the electromagnetic scattering characteristics and Doppler characteristics of the generated composite rough surface. The influence of land–sea interface factors on radar cross-section (RCS) and Doppler shift of radar echoes is investigated in detail by comparing the RCS and Doppler spectra of the land–sea junction composite rough surfaces with those of finite-depth sea surfaces. It can be found that the Doppler spectra of the land–sea junction composite rough surface is narrower than that of the finite-depth sea surface under upwind directions and wider than that of the finite-depth sea surface under crosswind directions.

Nanoscale Surface-Enhanced Raman Spectroscopy Investigation of a Polyphenol-Based Plasmonic Nanovector.

The demand for next-generation multifunctional nanovectors, combining therapeutic effects with specific cellular targeting, has significantly grown during the last few years, pursuing less invasive therapy strategies. Polyphenol-conjugated silver nanoparticles (AgNPs) appear as potential multifunctional nanovectors, integrating the biorecognition capability and the antioxidant power of polyphenols, the antimicrobial activity of silver, and the drug delivery capability of NPs. We present a spectroscopic and microscopic investigation on polyphenol-synthesized AgNPs, selecting caffeic acid (CA) and catechol (CT) as model polyphenols and using them as reducing agents for the AgNP green synthesis, both in the presence and in the absence of a capping agent. We exploit the plasmonic properties of AgNPs to collect Surface-Enhanced Raman Scattering (SERS) spectra from the nanosized region next to the Ag surface and to characterize the molecular environment in the proximity of the NP, assessing the orientation and tunable deprotonation level of CA, depending on the synthesis conditions. Our results suggest that the SERS investigation of such nanovectors can provide crucial information for their perspective biomedical application.

Investigation on the Radar Scattering and Doppler Spectrum From Trimaran Based on the Motion of Six Degrees of Freedom

Investigation on the Radar Scattering and Doppler Spectrum From Trimaran Based on the Motion of Six Degrees of Freedom

Plasmonic nanorod probes' journey inside plant cells for in vivo SERS sensing and multimodal imaging.

Nanoparticle-based platforms are gaining strong interest in plant biology and bioenergy research to monitor and control biological processes in whole plants. However, in vivo monitoring of biomolecules using nanoparticles inside plant cells remains challenging due to the impenetrability of the plant cell wall to nanoparticles beyond the exclusion limits (5-20 nm). To overcome this physical barrier, we have designed unique bimetallic silver-coated gold nanorods (AuNR@Ag) capable of entering plant cells, while conserving key plasmonic properties in the near-infrared (NIR). To demonstrate cellular internalization and tracking of the nanorods inside plant tissue, we used a comprehensive multimodal imaging approach that included transmission electron microscopy (TEM), confocal fluorescence microscopy, two-photon luminescence (TPL), X-ray fluorescence microscopy (XRF), and photoacoustics imaging (PAI). We successfully acquired SERS signals of nanorods in vivo inside plant cells of tobacco leaves. On the same leaf samples, we applied orthogonal imaging methods, TPL and PAI techniques for in vivo imaging of the nanorods. This study first demonstrates the intracellular internalization of AuNR@Ag inside whole plant systems for in vivo SERS analysis in tobacco cells. This work demonstrates the potential of this nanoplatform as a new nanotool for intracellular in vivo biosensing for plant biology.

Deep Scattering Spectrum Germaneness for Fault Detection and Diagnosis for Component-Level Prognostics and Health Management (PHM)

Most methodologies for fault detection and diagnosis in prognostics and health management (PHM) systems use machine learning (ML) or deep learning (DL), in which either some features are extracted beforehand (in the case of typical ML approaches) or the filters are used to extract features autonomously (in the case of DL) to perform the critical classification task. In particular, in the fault detection and diagnosis of industrial robots where the primary sources of information are electric current, vibration, or acoustic emissions signals that are rich in information in both the temporal and frequency domains, techniques capable of extracting meaningful information from non-stationary frequency-domain signals with the ability to map the signals into their constituent components with compressed information are required. This has the potential to minimise the complexity and size of traditional ML- and DL-based frameworks. The deep scattering spectrum (DSS) is one of the approaches that use the Wavelet Transform (WT) analogy for separating and extracting information embedded in a signal's various temporal and frequency domains. Therefore, the primary focus of this work is the investigation of the efficacy and applicability of the DSS's feature domain relative to fault detection and diagnosis for the mechanical components of industrial robots. For this, multiple industrial robots with distinct mechanical faults were studied. Data were collected from these robots under different fault conditions and an approach was developed for classifying the faults using DSS's low-variance features extracted from input signals. The presented approach was implemented on the practical test benches and demonstrated satisfactory performance in fault detection and diagnosis for simple and complex classification problems with a classification accuracy of 99.7% and 88.1%, respectively. The results suggest that, similarly to other ML techniques, the DSS offers significant potential in addressing fault classification challenges, especially for cases where the data are in the form of signals.

Development of Data Processing and Analysis Pipeline for the Ricochet Experiment

Achieving a percentage-level precision measurement of the Coherent Elastic Neutrino Nucleus Scattering (CE{\nu}NS) spectrum requires a robust data processing pipeline which can be characterised with great precision. To fulfil this goal we present hereafter a new Python-based data processing pipeline specifically designed for temporal data analysis and pulse amplitude estimation. This pipeline features a data generator allowing to accurately simulate the expected data stream from the RICOCHET experiment at the Institut Laue Langevin (ILL) nuclear reactor, including both background and CE{\nu}NS signals. This data generator is pivotal to fully understand and characterise the data processing overall efficiency, its reconstruction biases, and to properly optimise its configuration parameters. We show that thanks to this optimized data processing pipeline, the CryoCube detector array will be able to achieve a 70 eV energy threshold combined with electronic/nuclear recoil discrimination down to {\sim}100 eV, hence fulfilling the RICOCHET targeted performance.

Evolution During Accelerated Stress Test of Performance and of the Catalyst Layer's Ionomer Physical and Chemical Structures in Proton Exchange Membrane Fuel Cell

Catalyst layer’s ionomer plays a crucial role in the operation of Proton Exchange Membrane Fuel Cell (PEMFC) not simply because it binds the catalyst nanopowder but mostly because it allows protons transport and affects both gas diffusivity and water management [1]. In the catalyst layer, the ionomer is a perfluorosulfonic acid polymer similar to that use in the membrane as electrolyte. It is in the form of a few nanometer thick film dispersed around the carbon support or as aggregates larger than 150 nm within the pores of the carbon network [2,3]. In contrast with bulk ionomer membrane or model thin films that have been extensively studied [ref], little is known on the structural and functional properties of this proton conducting ionomer inside the electrode, and even less on their evolution upon aging. To date, it has proven difficult to selectively probe the fluorinated polymer dispersed at the nanoscale in a few micrometer thick electrode using conventional laboratory characterization techniques (electrochemistry, spectroscopy, and microscopy). Especially, its evolution upon aging is still an open question whereas the degradation of the ionomer due to radical attack was demonstrated and extensively investigated. Only Morawietz and co-workers evidence a thinning of the ionomer after operation [4]In this study, we studied the evolution of performance of a Membrane Electrode Assembly (MEA) after Accelerated Stress Test (AST) known to induce severe membrane degradation due to radicals, as well as the physical and chemical structures of the ionomer in the catalyst layer. Small Angle Neutron Scattering (SANS) spectra have been extensively analysed to characterise its swelling behaviour as a function of relative humidity. Its chemical structure was investigated by elemental analyses and XPS measurements, in addition to measurements of ion exchange capacity and of vapour sorption isotherms. Electrochemical characterisations including polarisation curve, impedance measurements and limiting current analyses in dry and wet conditions were performed to assess limiting phenomena and to try to unravel the contribution of the ionomer properties in the loss of performance. The SANS measurements clearly evidence an evolution of the structure and swelling behaviour of the ionomer after AST but, despite multiple characterisations, they can be hardly related to the evolution of electrochemical characteristics. Jinnouchi, R. et al. The role of oxygen-permeable ionomer for polymer electrolyte fuel cells. Commun. 12, (2021).Ueda, S., Koizumi, S., Ohira, A., Kuroda, S. & Frielinghaus, H. Grazing-incident neutron scattering to access catalyst for polymer electrolyte fuel cell. Phys B Condens Matter 551, 309–314 (2018).Morawietz, T., Handl, M., Oldani, C., Friedrich, K. A. & Hiesgen, R. Influence of Water and Temperature on Ionomer in Catalytic Layers and Membranes of Fuel Cells and Electrolyzers Evaluated by AFM. Fuel Cells 18, 239–250 (2018).Morawietz, T. et al. High-resolution analysis of ionomer loss in catalytic layers after operation. J Electrochem Soc 165, F3139–F3147 (2018). Figure 1

Cascades of Fano resonances in light scattering by dielectric particles

Fano lineshapes are fundamental spectroscopic signatures that quantitatively characterize the structural and dynamic properties of physical objects, from nuclei to three-dimensional solids and liquids. The study of cascades of Fano resonances is a fresh approach to the classical problem that greatly expands our knowledge and the scope of practical applications. Here it is demonstrated that in solid state physics cascades of Fano resonance can be considered as a general property of light scattering by dielectric particles with the Mie and Fabry-Perot manifold of narrow photonic eigenmodes. A general picture of Fano resonance cascades in the spectra of all basic elementary scatterers (spheres, cylinders, rings, split rings, rectangular cuboids) is presented. For rings, split rings, and cuboids, the scattering spectrum is divided into separate spectral regions, which begin with a broad transverse resonance of the Lorentz-type or Fano-type lineshape, genetically related to the modes of the disk that generates the ring, and continue with a gallery of longitudinal modes with exponentially increasing Q factors. The alternation of a cascade of transverse modes in a strict sequence of Lorentz-Fano-Lorentz-Fano-… is shown theoretically, as well as experimentally in the case of a ring. This picture opens the door to new fundamental phenomena and extended functionality of dielectric elementary scatterers due to the highly asymmetric controllable shape of the Fano line, periodically repeating in the scattering spectrum.

Mode Hybridization in Silicon Core-Gold Shell Nanosphere.

A dielectric core-metal shell nanosphere has attracted scientific and technological interests due to the unique optical resonances arising from the hybridization of surface plasmon modes and cavity modes. The previous studies focus on a low-index dielectric core without its own optical resonances. Here, optical resonances of a core-shell nanosphere with a high refractive index (n≈ 4) core with the lowest order Mie resonances in the visible range are investigated theoretically and experimentally. Scattering and absorption spectra of a core-shell nanosphere for different values of the core refractive index are first analyzed, and there is a transition of the hybridization scheme around n≈ 2. Above the value, a characteristic hybridized mode with strong absorption and weak scattering emerges in the near-infrared range. A core-shell nanosphere composed of a silicon core and a gold shell is prepared, and the resonance modes are studied by single particle scattering spectroscopy and electron energy loss spectroscopy (EELS) in a transmission electron microscope. The core-shell nanospheres exhibit the hybridized modes depending on the core diameter. The hybridized mode as well as the higher order one that is not observable in the scattering spectroscopy is observed in the EELS.

Research of the Optical Fibers Structure Influence on the Acousto-Optic Interaction Characteristics and the Brillouin Scattering Spectrum Profile

The research results of the structure influence of single-mode optical fibers of various types on the interaction characteristics of the optic and acoustical modes of lower orders are presented in this work. A method for obtaining of optic and acoustical modes distribution in single-mode optic fibers is considered. The structures of the radial distribution of refractive index profiles for some varieties of single-mode optical fibers with dispersion shift (ITU–T recommendation G.653 and G.653) are presented. The obtained graphs for varieties of optical fibers are presented.

Electronic excitations of α−Fe2O3 heteroepitaxial films measured by resonant inelastic x-ray scattering at the Fe L edge

Resonant Inelastic X-Ray Scattering (RIXS) spectra of hematite were measured at the Fe L3-edge for heteroepitaxial thin films which were undoped and doped with 1% Ti, Sn or Zn, in the energy loss range in excess of 1 eV to study electronic transitions. The spectra were measured for several momentum transfers (q), conducted at both low temperature (T=14K) and room temperature. While we can not rule out dispersive features possibly owing to propagating excitations, the coarse envelopes of the general spectra did not appreciably change shape with q, implying that the bulk of the observed L-edge RIXS intensity originates from (mostly) non-dispersive ligand field (LF) excitations. Summing the RIXS spectra over q and comparing the results at T=14 K to those at T=300 K, revealed pronounced temperature effects, including an intensity change and energy shift of the 1.4 eV peak, a broadband intensity increase of the 3-4 eV range, and higher energy features. The q-summed spectra and their temperature dependences are virtually identical for nearly all of the samples with different dopants, save for the temperature dependence of the Ti-doped sample's spectrum, which we attribute to being affected by a large number of free charge carriers. Comparing with magnetization measurements for different temperatures and dopings likewise did not show a clear correlation between the RIXS spectra and the magnetic ordering states. To clarify the excited states, we performed spin multiplet calculations which were in excellent agreement with the RIXS spectra over a wide energy range and provide detailed electronic descriptions of the excited states. The implications of these findings to the photoconversion efficiency of hematite photoanodes is discussed.

Revealing hidden molecular nanostructure details in the pellet formulation of ibuprofen by combining Synchrotron and laboratory sources

Previous studies could report about different behavior of dried pellets at the 50°C due to the lack of constant rate period of drying. Now, one step further, the synchrotron (i.e. FT-IR, SWAXS) supported with laboratory sources (SWAXS, NMR) allowed deeper analytics reporting hidden partly molecular degradation invents from the frequently used drug ibuprofen in 4-isobutylacetophenone, and that molecule is no longer a drug but a toxic one. In the medication matrix CaSt was no degradation detected but the structural undulation seen with SAXS was also confirmed by FT-IR. However, we also show that a medical molecular degradation in their solid formulation could be detected and predicted not only by chemical analytics but also correlated and identified by a physical method based on X-Ray inner surface calculation. Such molecular degradation from active pharmaceutical ingredients (API) that occurs in the process of drug preparation in solid formulation can automatically be correlated with drug-dissolution. Thus, the Nano-analytical parameters based on the Small Angle X-Ray scattering (SAXS) signals of different electron densities in the material, identify the specific surface area, which could be seen even in closed pores and can be correlated with drug molecular degradation. As an example, is taken a very well-defined pellet solid ibuprofen calcium-stearate formulation. Using a synchrotron and a laboratory multi-technical package, we could reveal hidden nanostructures and molecular information, which have not been detected in previous studies. The Small and Wide X-Ray Scattering (SWAXS) spectra of pellets show that process variables do not affect the crystalline state of components but slight changes in the crystal lattice undulations (i.e., SAXS). Additionally, using the high-resolution NMR, Raman and Synchrotron-beam line FT-IR method, we can identify the molecular partial degradation of the Active Pharmaceutics Ingredient (API) in 4-isobutylacetophenone only at 50 °C in terms of the drying and preparation-condition. Such molecular degradation could not be detected in previous study due to the lack of high-resolution analytical laboratories and synchrotron tools that we were able to apply in this paper. For this sample also SAXS specific surface area display higher values and can be correlated as an indication and prediction for the molecular degradation but also dissolution due to increase of electron density contrasts.In general, such analyzes are helpful for the detection in time before the product comes on the market. Thus, we propose the idea of additional high-resolution multi-techniques i.e. Synchrotrons and new methods to become more common for revealing hidden medical molecular degradations, which can induce non-therapeutically but side effects.

Polarimetric Scattering Spectrum Analysis for Target Characterization

This study proposes the spectrum analysis of the scattering-type parameter, θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FP</sub> , to characterize different land cover targets. Many orthonormal projections of the scattering information onto distinct polarization bases have been proposed in the literature. However, these conventional orthonormal basis projection techniques often provide various target characterization ambiguities and classification. In this work, we propose projecting the target coherency matrix onto several random realizations of the normalized scattering configuration without restriction to the orthogonality constraint. This unique approach helps enhanced target characterization from polarimetric SAR data. We show the efficacy of our proposed technique over different land cover types using the C-band RADARSAT-2 data over SF and the L-band ALOS-2 data over Mumbai.