Estimation Of Refractive Index Research Articles

Refractive Index Resolved Imaging Enabled by Terahertz Time-Domain Spectroscopy Ellipsometry

Material characterization in the terahertz range is an interesting topic of research due to its great applications in material science, health monitoring, and security applications. Advances in terahertz generation, detection, and data acquisition have contributed to improved bandwidth, signal power, and signal-to-noise ratio. This enables advanced material characterization methods such as ellipsometry, which has been little explored in the terahertz frequency range, yet. Here, we introduce a comparison between material characterization with terahertz time-domain spectroscopy in transmission geometry and ellipsometry reflection geometry. Terahertz ellipsometry images were taken, showing spatially resolved refractive index estimation in the far field and higher image quality compared to single-polarization imaging.

Estimation of soot refractive index from its nanostructural parameters with the dispersion model

Particles derived from combustion processes, mainly composed of soot agglomerates, are acknowledged to be among the main contributors to climate change. Their effects depend mostly on their size, shape, and internal structure. Specifically, the latter has a significant effect on their optical properties, mainly through the refractive index. This index has been widely evaluated, but scarcely correlated with the soot internal characteristics. In this work, relationships between the nanostructural parameters (such as the degree of graphitization, among others) obtained with conventional analytical techniques and the input parameters of the dispersion model (a representation of the electromagnetic radiation through the Lorentz-Drude approach) are proposed with the aim to determine the refractive index. From experiments in a chassis dynamometer, it has been observed that as the vehicle speed increases, the soot samples have, in general, higher degree of graphitization, due to increased combustion temperature. The method proposed allows quantifying how both the real and imaginary parts of the complex refractive index increase as the degree of graphitization increases. Much lower dependence on the average crystal length has been observed. Different combinations of techniques can be used to determine the nanostructural parameters, depending on the analytical technique used. As far as the resulting parameters are reliable, the effect of the technique selected is minor, thus providing flexibility to the application of the method.

Carbon‐Based Photonic Microlabels Based on Fluorescent Nanographene‐Polystyrene Composites

AbstractNanographenes (NGs) are attracting significant interest as atomically‐thin carbon‐based optical nanomaterials exhibiting a wide range of optical properties that are fine‐tuned with ultimate atomic precision though chemical design. However, the use of NGs in photonic applications remains poorly explored. Here a straightforward procedure is demonstrated to load NGs onto the surface of polystyrene microbeads in order to synthesize a functional light emitting microcomposite. The resulting all‐carbon‐based microbeads behave as optical microresonators displaying narrowband emission lines spread across the whole visible spectrum. The unique fluorescent pattern of individual microbeads enables applications in metrology and information encoding. This is shown, as a proof‐of‐concept, by absolute diameter and refractive index estimates of individual polystyrene microbeads with nanometric precision, and the design of an unclonable photonic microtag for anti‐counterfeiting applications. These results pave the way to a new family of carbon‐based microdevices with a wide range of applications in photonics.

Machine learning approach for automated data analysis in tilted FBGs

This paper presents the development of an approach for automatic estimation of the regression coefficients as a function of the refractive index (RI) in Tilted Fiber Bragg Gratings (TFBGs) based on their optical spectral characteristics. The methodology is based on machine learning approach, where different samples are analyzed in various test conditions to obtain a dataset for the training of a kNN regression algorithm. In this case, the spectral characteristics related to the wavelength spacing between adjacent cladding mode resonances, number of valleys and amplitude of the normalized spectra are correlated with the linear regression algorithm for RI estimation using the machine learning approach. The analysis are performed as a function of the wavelength shift and optical power, considering two regions on the transmitted spectrum and the envelope of the curve. Furthermore, the number of valleys and the area of the envelope curve are also analyzed as a function of the refractive index, which result in six analyzed features. The Principal Components Analysis (PCA) is used to reduce the number of features to two principal components. The results of the proposed algorithm are obtained with different trained and untrained samples, which indicates a root mean squared error (RMSE) of around 0.003RIU for the trained samples and 0.008 RIU for the untrained ones. Therefore, the results indicate the usefulness of the proposed approach, which can be used to enhance the reproducibility of large set of TFBG sensors in practical applications.

Single microparticle characterization using multi-wavelength lens-free imaging

Holographic imaging captures an interference pattern, effectively encoding an object‘s properties such as size, shape and refractive index in the hologram. Lens-free holographic imaging offers a scalable solution with large field of view to analyze microparticles or cells in high-throughput biological imaging applications. We studied characterization of single particles based on their holographic fingerprint using multi-wavelength illumination based lens-free holography. Deciphering this information directly in the hologram domain with our multi-wavelength approach allows for reliable estimation of object refractive index along with its size without ambiguity. This work provides a path forward for lens-free imaging-based microparticle characterization that can prove useful in biological studies such as cell analysis and characterization.

LiqDetector

With the advancement of wireless sensing technologies, RF-based contact-less liquid detection attracts more and more attention. Compared with other RF devices, the mmWave radar has the advantages of large bandwidth and low cost. While existing radar-based liquid detection systems demonstrate promising performance, they still have a shortcoming that in the detection result depends on container-related factors (e.g., container placement, container caliber, and container material). In this paper, to enable container-independent liquid detection with a COTS mmWave radar, we propose a dual-reflection model by exploring reflections from different interfaces of the liquid container. Specifically, we design a pair of amplitude ratios based on the signals reflected from different interfaces, and theoretically demonstrate how the refractive index of liquids can be estimated by eliminating the container's impact. To validate the proposed approach, we implement a liquid detection system LiqDetector. Experimental results show that LiqDetector achieves cross-container estimation of the liquid's refractive index with a mean absolute percentage error (MAPE) of about 4.4%. Moreover, the classification accuracies for 6 different liquids and alcohol with different strengths (even a difference of 1%) exceed 96% and 95%, respectively. To the best of our knowledge, this is the first study that achieves container-independent liquid detection based on the COTS mmWave radar by leveraging only one pair of Tx-Rx antennas.

Estimating refractive index of thin-film alloys of Cu, Ag, and Au by extension from those of the main metal

Due to dependence of nanostructural material properties on the deposition conditions, the refractive index (RI) of a thin-metal alloy can vary by over 100×. For a more accurate RI estimation of thin-film alloys under a specific deposition condition, a model of RI for untested alloys is derived by extension from the alloy’s main element properties. One of the key derivations is to simplify RI polynomial equations into monomial expressions through four assumptions. Thus, a ratio method can accurately predict the RI of thin-film alloys from the properties of their main metals under the same deposition condition. The model is valid in the red to infrared spectrum for thin-film alloys MX where M is one of copper, silver, or gold and X is one or more elements with total concentration <10%.

Contribution of physical and system parameters in the determination of optical constants of thin solid phase samples using THz time domain transmission spectroscopy

Accurate estimation of complex refractive index of optically thin sample is an important challenge in terahertz time domain spectroscopy (THz-TDS). While majority of the previous studies on this topic consider Fabry-Perot (FP) effect as the primary cause of erroneous optical parameter extraction, advanced application criteria in industrial domains, such as, quality control of pharmaceutical composites or semiconductor heterostructure characterization etc. demand further assessment beyond FP effect only. Instead of conventional time-of-flight calculation pivoted to the reference data (usually obtained separately without any sample through air), we employed the differential data obtained from primary and secondary THz pulse through thin pellets to obtain accurate thickness mapping of the same. We observed that for pelletized samples with significantly larger porosity (>15%), the competing optical process like multiple internal scattering has greater impact on the accuracy of estimated optical properties. While in thin pelletized samples with smaller porosity (<10 %), the principal contributor of error is the non-uniform distribution of pellet thickness. For extremely thin pellets the optical parameter extraction is highly susceptible to uncertainties due to external factors such as environmental and/or system drift because of significantly small sample-THz interaction volume. Our observations indicate that short THz-sample interaction, porosity and non-uniformity of sample thickness are the principal causes of erroneous estimation of complex refractive index. It must be noted, however, that, this description does not specify an absolute value of material thickness but is highly dependent on the complex refractive index of the material itself. We also conclude that for specific material, there exists an optimized optical thickness beyond which the contribution of physical and system parameters in the determination of optical constants becomes minimal: it was determined to be around 3 mm for HDPE and 3.2 mm for PTFE. For most of the materials (with lower values of real and complex parts of optical constant) suitable for THz-TDS transmission spectroscopy, this critical thickness for accurate determination of the optical constant, therefore, is expected to be substantial, based on this present study.

Three-dimensional refractive index estimation based on deep-inverse non-interferometric optical diffraction tomography (ODT-Deep).

Optical diffraction tomography (ODT) solves an inverse scattering problem to obtain label-free, 3D refractive index (RI) estimation of biological specimens. This work demonstrates 3D RI retrieval methods suitable for partially-coherent ODT systems supported by intensity-only measurements consisting of axial and angular illumination scanning. This framework allows for access to 3D quantitative RI contrast using a simplified non-interferometric technique. We consider a traditional iterative tomographic solver based on a multiple in-plane representation of the optical scattering process and gradient descent optimization adapted for focus-scanning systems, as well as an approach that relies solely on 3D convolutional neural networks (CNNs) to invert the scattering process. The approaches are validated using simulations of the 3D scattering potential for weak phase 3D biological samples.

Heat of Vaporization and Refractive Index Estimation for Hydrocarbons and Petroleum Fractions based on Simple Models

The present research reports that simple two parameter and three parameter models integrating normal boiling point, specific gravity and molecular weight of hydrocarbons can effectively be used for estimation of different important properties namely heat of vaporization and refractive index. Multivariate regression analysis was employed for model development based on experimental data of pure hydrocarbons reported in literature. Subsequently, it has been demonstrated that this model can be utilized further for prediction of those properties for different petroleum fractions as well. The developed simple generalized two parameter regression-based models can predict heat of vaporization of pure hydrocarbons (C3 – C30) and petroleum fractions with wide boiling point range from 355.5 to 646.8 K with good accuracy (percentage error less than 10% for pure hydrocarbons and lower than 13% for petroleum fractions) in addition, refractive index of petroleum fractions are estimated with percentage error of less than 4.01%. Moreover, comparison results demonstrated that developed models are more accurate and simpler for practical applications in petroleum industry as compared to earlier published correlations for both pure hydrocarbons and petroleum fractions.

Refractive index estimation in biological tissues by quantitative phase imaging

The refractive index (RI) of biological tissues represents a relevant optical property that correlates with intrinsic factors such as dry mass or water content, providing valuable information about tissue composition. First, we review available methods to measure the RI in biological tissues, making special emphasis on quantitative phase imaging (QPI) and its ability to obtain RI maps as well as tri-dimensional projections of transparent samples at diffraction-limited resolution. Second, we propose a simple, general QPI method to estimate the RI of complex materials without prior knowledge of sample thickness. Here we have tested several samples in various immersion media with different refractive indices (RIs) to use the lineal relation between the optical path difference (OPD) and the RI of the medium. Least squares regression operates to simultaneously determine the RI and the thickness with diffraction-limited resolution and nanometric transversal sensitivity. Our findings categorize a set of immersion media covering a good RI range, identify possible error sources and uncover the applicability of the QPI method, paving the way to its use on heterogeneous biological tissues.

Predicting Real Refractive Index of Organic Aerosols From Elemental Composition

AbstractAccurate estimates of aerosol refractive index (RI) are critical for modeling aerosol‐radiation interaction, yet this information is limited for ambient organic aerosols, leading to large uncertainties in estimating aerosol radiative effects. We present a semi‐empirical model that predicts the real RI n of organic aerosol material from its widely measured oxygen‐to‐carbon (O:C) and hydrogen‐to‐carbon (H:C) elemental ratios. The model was based on the theoretical framework of Lorenz‐Lorentz equation and trained with n‐values at 589 nm () of 160 pure compounds. The predictions can be expanded to predict n‐values in a wide spectrum between 300 and 1,200 nm. The model was validated with newly measured and literature datasets of n‐values for laboratory secondary organic aerosol (SOA) materials. Uncertainties of predictions for all SOA samples are within 5%. The model suggests that ‐values of organic aerosols may vary within a relatively small range for typical O:C and H:C values observed in the atmosphere.

A Theoretical Estimation of Optical, Vibrational and Structural Properties of II–VI Quaternary Alloy Zn0.5Cd0.5SySe1–y

We present a theoretical estimation of optical, vibrational, and structural properties of II–VI semiconducting quaternary alloy Zn0.5Cd0.5SySe1−y for 0 &lt; y &lt; 1 giving total 10 compositions. The estimation of refractive index, elastic constants, bulk modulus, and vibrational frequencies are performed using the important input parameters provided by the empirical pseudopotential method. In this method, the bandgaps are computed, and the alloying effects are modeled through the modified virtual crystal approximation. We have computed the static refractive index, static and high-frequency dielectric constants, longitudinal and transverse optical phonon frequencies, elastic constants, bulk modulus, and cohesive energy for 10 compositions of the alloy. The results are compared to other experimental and theoretical values wherever available.

On Nanobubble Dynamics under an Oscillating Pressure Field during Salting-out Effects and Its DLVO Potential

We have investigated the origin, stability, and nanobubble dynamics under an oscillating pressure field followed by the salting-out effects. The higher solubility ratio (salting-out parameter) of the dissolved gases and pure solvent nucleates nanobubbles during the salting-out effect, and the oscillating pressure field enhances the nanobubble density further as solubility varies linearly with gas pressure by Henry's law. A novel method for refractive index estimation is developed to differentiate nanobubbles and nanoparticles based on the scattering intensity of light. The electromagnetic wave equations have been numerically solved and compared with the Mie scattering theory. The scattering cross-section of the nanobubbles was estimated to be smaller than the nanoparticles. The DLVO potentials of the nanobubbles predict the stable colloidal system. The zeta potential of nanobubbles varied by generating nanobubbles in different salt solutions, and it is characterized by particle tracking, dynamic light scattering, and cryo-TEM. The size of nanobubbles in salt solutions was reported to be higher than that in pure water. The novel mechanical stability model is proposed by considering both ionic cloud and electrostatic pressure at the charged interface. The ionic cloud pressure is derived by electric flux balance, and it is found to be twice the electrostatic pressure. The mechanical stability model for a single nanobubble predicts the existence of stable nanobubbles in the stability map.

Application of a Multiple Regression Model for the Simultaneous Measurement of Refractive Index and Temperature Based on an Interferometric Optical System

Interferometric optical systems have been proposed for implementing dual‐parameter optical sensors. For this type of sensors, the sensitivity matrix equation is generally used to determine the parameters to be measured based on the sensitivity of each parameter to one particular feature of the output reflective spectrum of the interferometric system. One of the disadvantages of this method is that the measurement ranges will be very short if the sensitivities are not linear or if these present cross‐sensitivity. In this work, a multiple regression model for the simultaneous detection of refractive index and temperature based on an interferometric optical sensor is proposed. Here, the mathematical model is a weighted sum of features used to estimate the values of two response variables. These features are functions of an initial set of 27 explanatory variables whose values were extracted of the output reflective spectrum of the interferometric system. Besides, in order to sustenance the model application, the sensor was modeled and experimentally carried out. Three cases were studied: the estimation of temperature at different refractive indices, the estimation of temperature when refractive index is equal to one, and the estimation of refractive index at different temperatures. For each one of these cases, an optimal basis of functions was founded with the algorithm proposed and used to estimate the values of the response variables. Besides, a technique to reduce the initial set of variables was implemented. Finally, for the experimental data, For each one of these cases, an optimal basis of functions was founded with the algorithm 1 proposed and used to estimate the values of the response variables.

Analysis on the Influence of Incident Light Angle on the Spatial Aberrations of Acousto-Optical Tunable Filter Imaging.

Acousto-optical tunable filter (AOTF) does not conform to the pinhole model due to the acousto-optic interaction. A calculation method of AOTF aberrations under the condition of incident light with a large arbitrary angle is proposed to solve the problem of coordinate mapping between object space and image space of the AOTF system without refractive index approximation. This approach can provide accurate pointing information of the interested targets for the tracking and searching system based on AOTF. In addition, the effect of cut angle values of the paratellurite crystal on aberrations was analyzed to optimize the design of AOTF cutting according to different application requirements. Finally, distribution characteristics and quantitative calculation results of AOTF aberrations were verified by experiments with different targets, respectively. The experimental results are in good agreement with the simulations.

Non-destructive evaluation of coatings using terahertz reflection spectroscopy

Terahertz time-domain spectroscopy (THz-TDS) provides a non-contact, non-destructive method for evaluating different materials and their properties. This short review discusses the commonly used numerical models for the non-destructive estimation of thickness, refractive index, surface and interface roughness of paints, thermal barrier coatings, and polymer coatings using THz-TDS in the reflection geometry. To demonstrate the applicability of these models, we used paint layers on metallic substrates and extracted different paraments by fitting the experimental THz-TDS data. We conclude by discussing further steps to improve the efficiency of the fitting procedure used to extract the layer parameters.

Consideration on the Optical Model in Measuring Complex Refractive Index for the Purpose of Judging Real Contact

In the real contact measurement by ellipsometry, there is a problem that the measured complex refractive index changes gently at the contact/non-contact boundaries. In this report, the multiple reflection in the gap between contact surfaces was considered in the optical model which was used to estimate complex refractive index. The sample measured result for a glass plane pressed by a glass lens in the air was successfully accounted for by the estimation by the optical model which considered the gap of layered structure. Thereby, it could be possible to improve the accuracy of real contact judgement by excluding the influence of multiple reflection in the gap on the estimation of complex refractive index.

Does salting-out effect nucleate nanobubbles in water: Spontaneous nucleation?

The solubility of gases in aqueous salt solution decreases with the salt concentration, often termed the “salting-out effect.” The dissolution of salt in water is followed by dissociation of salt and further solvation of ions with water molecules. The solvation weakens the affinity of gaseous molecules, and thus it releases the excess dissolved gas. Now it is interesting to know that what happens to the excess gas released during salting-out? Since it is imperative to note that the transfer of the dissolved gas in the bulk liquid may often occur in the form of nanobubbles. In this work, we have answered this question by investigating the nano-entities nucleation during the salting-out effect. The solubility of gases in aqueous salt solution decreases with the salt concentration, and it is often termed as the “salting-out effects.” The dissolution of salt in water undergoes dissociation of salt and further solvation of ions with water molecules. The solvation weakens the affinity of gaseous molecules, and thus it releases the excess dissolved gas. Now it is interesting to know that what happens to the excess gas released during salting-out? While it is also imperative to note that the gas transfer in the bulk liquid often occurs in the form of bubbles. With this hypothesis, we have experimentally investigated that whether the salting-out effect nucleates nanobubble or not. What is the strong scientific evidence to prove that they are nanobubbles? Does the salting-out parameter affect the number density? The answers to such questions are essential for the fundamental understanding of the origin and driving force for nanobubble generation. We have provided three distinct proofs for the nano-entities to be the nanobubbles, namely, (1) by freezing and thawing experiments, (2) by destroying the nanobubbles under ultrasound field, and (3) we also proposed a novel method for refractive index estimation of nanobubbles to differentiate them from nano drops and nanoparticles. The refractive index (RI) of nanobubbles was estimated to be 1.012 for mono- and di-valent salts and 1.305 for trivalent salt. The value of RI closer to 1 provides strong evidence of gas-filled nanobubbles. Both positive and negative charged nanobubbles nucleate during the salting-out effect depending upon the valency of salt. The nanobubbles during the salting-out effect are stable only for up to three days. This shorter stability could plausibly be due to reduced colloidal stability at a low surface charge.

Computational multi-wavelength phase synthesis using convolutional neural networks [Invited

Multi-wavelength digital holographic microscopy (MWDHM) provides indirect measurements of the refractive index for non-dispersive samples. Successive-shot MWDHM is not appropriate for dynamic samples and single-shot MWDHM significantly increases the complexity of the optical setup due to the need for multiple lasers or a wavelength tunable source. Here we consider deep learning convolutional neural networks for computational phase synthesis to obtain high-speed simultaneous phase estimates on different wavelengths and thus single-shot estimates of the integral refractive index without increased experimental complexity. This novel, to the best of our knowledge, computational concept is validated using cell phantoms consisting of internal refractive index variations representing cytoplasm and membrane-bound organelles, respectively, and a simulation of a realistic holographic recording process. Specifically, in this work we employed data-driven computational techniques to perform accurate dual-wavelength hologram synthesis (hologram-to-hologram prediction), dual-wavelength phase synthesis (unwrapped phase-to-phase prediction), direct phase-to-index prediction using a single wavelength, hologram-to-phase prediction, and 2D phase unwrapping with sharp discontinuities (wrapped-to-unwrapped phase prediction).