entire archived database

Version 1.3, updated 11/15/2024.

Added a file with 27 regional dust sample mineral composition information 'NewRegionalSamples.xlsx',

along with the refractive index data.

All refractive index files here have 127 rows (wavelengths) and 27 columns (samples)

'kall27_coarse.dat' is the imaginary part of the coarse mode.

'kall27_fine.dat' is the imaginary part of the fine mode.

'nall27_coarse.dat' is the real part of the coarse mode.

'nall27_fine.dat' is the real part of the fine mode.

Version 1.2, updated 04/23/2024.Major changes: Changed all the data file names to new format: "mix"+{property name}+{number}, rearranged the number of mixing samples

Updated all the bulk optical property data. This version use constant values of standard deviation in the lognormal size distribution settings for the coarse mode and the fine mode respectively.

The phase matrices are separated from the other bulk properties due to their large file sizes. The readme file is updated correspondingly. The information of scattering angles (498 angles in total) is uploaded as "TAMUdust2020_Angle.dat".

Added supplemental file data in 'Supplemental.tar.gz'.

Additional refractive indices are zipped in 'AdditionalRefInd.tar.gz'

Version 1.1, updated 03/14/2024.Major changes: Added mixed bulk properties for "0 (99%coarse+1%fine)" and "11 (2.0 µm coarse+ 0.4 µm fine)";Added "reff.dat" in the 'BulkProperties.tar.gz'. The data include four columns: fine mode fraction, bulk projected area , bulk volume , effective radius r_eff. The information is for mixed sample number 0 to 11, each corresponds to one row.Added refractive indices for chlorite, mica, smectite, pyroxene, vermiculite and pyroxenes. These groups can be applied in some other models.

Version 1.0, uploaded 01/02/2024.

This database include supplemental data and files for the publication of this paper:

Sensitivities of Spectral Optical Properties of Dust Aerosols to their Mineralogical and Microphysical Properties. Yuheng Zhang, M. Saito, P. Yang, G. L. Schuster, and C. R. Trepte, J. Geophys. Res. Atmos. 2024.

The supplemental data include:

1) 'GroupRefInd.tar.gz' Mineral (group) refractive index files.E. g., 1All_Illite.dat contains the complex refractive index files of illite group. Format (from left to right columns): Wavelength (unit: µm), Real part (n), Imaginary part (k), standard deviation of n, standard deviation of k.

The file 'fine_log.dat' includes the mean and standard deviation values of n and k for all the generated fine mode dust samples at 11,044 wavelengths from 0.2 to 50 micron.

The file 'fine_log127.dat' only includes the values at 127 wavelengths from 0.2 to 50 micron (defined in 'swav.txt' and 'lwav.txt'), and is used for the bulk property computations.

The files 'coarse_log.dat' and 'coarse_log127.dat' are for the coarse mode dust samples.

2) 'CompositionFraction.xlsx': Mineral composition data sources/references and composition data (mean and standard deviation values of each group).'Vlog_coarse.dat': Randomly generated VOLUME FRACTION of 9 mineral groups for the coarse mode dust. Left to right: Illite, Kaolinite, Montmorillonite (Other clays), Quartz, Feldspar, Carbonate, Gypsum (Sulphate), Hematite, Goethite.

'Vlog_fine.dat': For the fine mode dust.

3) 'RefSources.xlsx': The data source references of mineral refractive indices. We didn't include the olivine, other silicates, soot and titanium-rich minerals in the paper, but the refractive indices are available for those who are interested. Chlorite, Mica and Vermiculite group are mentioned in some studies, and we included the refractive indices for these minerals as well.

4) 'DustSamples.tar.gz' Dust sample refractive index files.The files are enclosed in four folders: fine_sw/ fine_lw/ coarse_sw/ coarse_lw/.

fine: fine mode. coarse: coarse mode.

'sw' means shortwave (< 4 µm, in total 76 wavelengths defined in 'swav.txt') while 'lw' means longwave (>= 4 µm, in total 51 wavelengths defined in 'lwav.txt').

All files start with 'rdn', which means that they are computed based on randomly generated composition (data given in sheet 2 of 'CompositionFraction.xlsx').

The four digit number after 'rdn' is the index of each dust sample. In total, there are 5,000 samples. The sample composition is the same for the same sample index in the same size mode (fine/coarse). Data file format (from left to right columns): real part, imaginary part.

5) 'BulkProperties.tar.gz' Bulk property files (excluding phase matrices)'mixqx.dat' files format (from left to right columns): Extinction efficiency (Qext), Scattering efficiency (Qsca), Backscattering efficiency (Qbck), and Asymmetry coefficient (Qasy). To obtain asymmetry factor, use Qasy/Qsca.

'mixbkx.dat' files format (from left to right columns): P11(pi) P12(pi) P22(pi) P33(pi) P34(pi) P44(pi).

'x' refers to the number at the end of the file name. It can be 100 ~ 112, each represents a setting of coarse and fine mode effective radius and volume fraction (see details in "reff.dat")

'reff.dat' contains the effective radius information of the mixture. It has 7 columns: File number "x", Fine mode volume fraction, Fine mode effective radius (µm), Coarse mode effective radius (µm), Bulk projected area (µm^2), Bulk volume (µm^3), Bulk effective radius (µm).

6) 'PhaseMatrices.tar.gz' Phase matrices data'mixphswx.dat' files contain phase matrix results at 532 nm (shortwave). From left to right: P11, P12, P22, P33, P34, P44.

'mixphlwx.dat' files contain phase matrix results at 10.5 µm (longwave).

There are 635,000 rows in each data file. 635,000 rows = 127 wavelengths * 5,000 samples. Row 1~127 is sample 1, row 128~254 is sample 2, etc.. Suggest to use matlab function 'reshape(property, 127, 5000)' for each column when processing the data.

7) 'Supplemental.tar.gz'

We also include data files mentioned in the supplemental file of the paper. The adjusted source data files of the nine mineral groups are included.

The supplemental bulk property files are named based on the figure number.

8) 'AdditionalRefInd.tar.gz'

We also include additional refractive indices for chlorite, smectite, vermiculite, mica, dolomite, titanium-rich minerals, pyroxenes and soot. These data can be useful in other models.

For more detailed information and datasets, please contact: Yuheng Zhang, yuheng98@tamu.edu or yuhengz98@qq.com.

An optical property database of refractive indices and dielectric constants is presented, which comprises a total of 49,076 refractive index and 60,804 dielectric constant data records on 11,054 unique chemicals. The database was auto-generated using the state-of-the-art natural language processing software, ChemDataExtractor, using a corpus of 388,461 scientific papers. The data repository offers a representative overview of the information on linear optical properties that resides in scientific papers from the past 30 years. The database has been prepared in three formats: SQL, JSON and CSV.

Cu(In,Ga)Se2 -based solar cells have reached efficiencies close to 23%. Further knowledge-driven improvements require accurate determination of the material properties. Here, we present refractive indices for all layers in Cu(In,Ga)Se2 solar cells with high efficiency. The optical bandgap of Cu(In,Ga)Se2 does not depend on the Cu content in the explored composition range, while the absorption coefficient value is primarily determined by the Cu content. An expression for the absorption spectrum is proposed, with Ga and Cu compositions as parameters. This set of parameters allows accurate device simulations to understand remaining absorption and carrier collection losses and develop strategies to improve performances.

This is the refractive index data for 150 nm Ag thin film measured using the Sopra GES 5E spectroscopic ellipsometer. The data is extracted using the bulk calculation model of the WinEliII software. The incidence angle was 75 degrees, the analyzer was kept at 45 degrees. The film was deposited using e-beam evaporation technique in Lab 600H. The substrate used was silicon wafer of 525 microns thick. 2 nm of titanium was deposited initially as an adhesion layer and 1 nm of AgOx was deposited as the seed layer. It was followed by deposition of Ag (50 nm each for 3 times). The deposition rate of all the metals was 4 angstorm/sec with a base pressure of 1.5*10^(-6) mbar.

Version 1.2, updated 04/23/2024.
Major changes:
Changed all the data file names to new format: "mix"+{property name}+{number}, rearranged the number of mixing samples

Updated all the bulk optical property data. This version use constant values of standard deviation in the lognormal size distribution settings for the coarse mode and the fine mode respectively.

The phase matrices are separated from the other bulk properties due to their large file sizes. The readme file is updated correspondingly. The information of scattering angles (498 angles in total) is uploaded as "TAMUdust2020_Angle.dat".

Added supplemental file data in 'Supplemental.tar.gz'.

Additional refractive indices are zipped in 'AdditionalRefInd.tar.gz'

Version 1.1, updated 03/14/2024.
Major changes:
Added mixed bulk properties for "0 (99%coarse+1%fine)" and "11 (2.0 µm coarse+ 0.4 µm fine)";
Added "reff.dat" in the 'BulkProperties.tar.gz'. The data include four columns: fine mode fraction, bulk projected area , bulk volume

Refractive index measurements were performed on the transparent RTV2-silicone DOWSIL EI-1184 using a prism spectrometer. The experimental setup and its according variables are provided in 'experimental_setup_prism_spectrometer.png'. Three mixing ratios of the silicone components A and B were investigated. The provided data is named accordingly:

• Mixing ratio A:B = 1:1 (file 'Dispersion_EI_1184_A1_B1.csv')
• Mixing ratio A:B = 1:0.6 (file 'Dispersion_EI_1184_A1_B06.csv')
• Mixing ratio A:B = 0.6:1 (file 'Dispersion_EI_1184_A06_B1.csv')

All prism-shaped samples were prepared, stored and tested at room temperature.

Four measurements were performed for each mixing ratio. The .csv-files are structured in a way that the first measurement corresponds to rows 3-5, the second to rows 6-8, the third to rows 9-11 and the fourth to rows 12-14. In columns A-C, the results of the prism angle gamma (see figure 1) can be found. Column D is empty. Column E labels the measurements of the angles delta (see figure 2), which proceed in columns F-M. In row 2, these columns contain the respective wavelengths of the light source at which the angle measurements are taken. Column N is empty again, and the columns O-V contain the calculated refractive indexes at the respective wavelengths.

The refractice index n is calculated from the measurement data as follows: n = sin((gamma + delta)/2) / sin(gamma/2)

This study is a part of the research project eMC-Hammer. It describes an iterative approach to determine quasi-optical properties of standard 3D printer filament material to, in an inexpensive and fast way, construct focusing lenses for millimetre wave systems. Results from three lenses with different focal lengths are shown and discussed. The real part of the permittivity at 60GHz for polylactic acid (PLA) is in this paper determined to be er=2.74.

Purpose:

The purpose with the study is to validate an iterative, low cost, method of determining the refractive index of 3D printed lenses, where otherwise expensive equipment would be needed, such as S-parameter measurements using a vector network analyzer.

The dataset contains measurements, simulation results and matlab code used for the conference article "An iterative approach to determine the refractive index of 3D printed 60GHz PLA lenses" (doi:10.1049/cp.2018.1480) See the conference article (methods) and lapc2018mainfigure.m (data description - meta data) for details.

The refractive index of a y-cut SiO2 crystal surface is reconstructed from polarization dependent soft X-ray reflectometry measurements in the energy range from 45 eV to 620 eV. In the datasets the 1-delta and beta values for the (100) and (001) direction of the SiO2 crystal for the different energies are provided.

Cavity ring-down spectroscopy (CRDS) of single, optically manipulated aerosol particles affords quantitative retrieval of refractive indices for particles of fixed or evolving composition with high precision. We quantify the accuracy with which refractive index determinations can be made by CRDS for single particles confined within the core of a Bessel laser beam and how that accuracy is degraded as the particle size is progressively reduced from the coarse mode (> 1 micrometre radius) to the accumulation mode (< 500 nm radius) regime. We apply generalised Lorenz-Mie theory to the intra-cavity standing wave to explore the effect of particle absorption on the distribution of extinction cross section determinations resulting from stochastic particle motion in the Bessel beam trap. The analysis provides an assessment of the accuracy with which the real, n, and imaginary, κ, components of the refractive index can be determined for a single aerosol particle.

These data are published in M. I. Cotterell et al., Aerosol Science and Technology (2016)

Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction that has been shown to perform especially well for small materials datasets, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.

Terahertz (THz) spectroscopy is a powerful tool for unambiguously extracting complex-valued material properties (e.g., refractive index, conductivity, etc.) from a wide range of samples, with applications ranging from materials science to biology. However, extracting complex refractive indices from THz time-domain spectroscopy data can prove challenging, especially for multilayer samples. These challenges arise from the large number of transmission-reflection paths the THz pulse can take through the sample layers, leading to unwieldy strings of Fresnel coefficients. This issue has often been addressed using various approximations. However, these approximations are only applicable to specific classes of samples and can give erroneous results when misapplied. An alternative to this approach is to programmatically model all possible paths through the sample. The many paths through the sample layers can be modeled as a tree that branches at every point where the paths diverge, i.e., whenever the pulse can either be transmitted or reflected. This tree can then be used to generate expressions relating the unknown refractive index to the observed time domain data. Here, we provide a freely available open-source package implementing this method as both a MATLAB library and a corresponding graphical user interface, which can also be run without a MATLAB license (https://github.com/YaleTHz/nelly). We have tested this method for a range of samples and compared the results to commonly used approximations to demonstrate its accuracy and wide applicability. Our method consistently gives better agreement than common approximations.

This dataset contains optimised refractive index profile designs for multimode optical fibres with over 1000 linearly polarised modes. The base profile is composed of a graded-index core and a cladding trench. All parameters were optimised following the procedure described in the manuscript "Maximizing the Capacity of Graded-Index Multimode Fibers in the Linear Regime", DOI: 10.1109/JLT.2023.3324611. The optimisation was carried out on Matlab using a vector finite difference mode solver following the method in DOI: 10.1109/JLT.2008.923643.

This repository contains the data from [1].

This repository is structured as follows: The data itself is stored in the data folder. The raw measurements, as measured with the spectrometer is contained in the HDF5 files. Each HDF5 file represents a single measurement. The file names are given by the volume fraction of particles before the salt dilution. A HDF5 file contains the raw transmission spectra for the reference arm, sample arm and when both are open and both interfere. Therefore, a single raw measurement consists of three spectra. In addition to the spectra, some metadata is stored in the file as well.

The HDF5 files with 'Water' in the file name are water calibration measurements. Each sample measurement has an associated calibration measurement. How this is organized is found in the "settings" folder. Here, the settings for the analysis per measurement is stored in YAML files.

The data for the uncertainty estimate is found in the "IntralipidVarianceMeasurement" folder. The data is structured in a similar manner, except that the HDF5 files are split into three folders based on the day of the measurement.

Each processed measurement is stored in the folder "measurements". In this folder, the processed data for each measurement is stored. These contain the attenuation, group index and GVD. It is the data in these files that contain the experimental results presented in the paper. These results can be viewed with the two Python scripts in this folder.

The analysis scripts are stored in the "analysis" folder. We used Python 3.8 to run these scripts. The purpose of the files is as follows:

calculateConfidenceInterval.py: This scrips calculates the response in the obtained particle diameter and polydispersity to the standard deviation.

calculateVariance.py: This script is for calculating the variance from the Intralipid measurements.

dispersionFormulas.py: This file contains the polynomial phase index dispersion formula's for bulk silica and water, and the refractive index as obtained for the various models with forward fitting.

filters.py: This file contains some Fourier filtering functions.

fitModel.py: This file forward calculates the attenuation and group index to fit the particle size distribution and the refractive index.

measurement.py: This script analyses the raw spectral data to obtain the group index, GVD and attenuation.

mie.py: This file contains the Mie scattering functions and dependent scattering.

refractiveIndexFunctions.py: Convenience function to calculate the group index and GVD from the phase index.

seriesAcceleration.py: Contains the Richardson extrapolation. This might be used to reduce negative effects from truncating integrals.

structure.py: This file contains code to calculate the radial distribution function and the structure factor.

In the folder "models", which is located in the "analysis" folder, the scripts for the forward models are stored.

[1] P.N.A. Speets and J. Kalkman, "Experiment and theory of the complex refractive index of dense colloidal media", J. Opt. Soc. Am. A, 41.2 : 214-228 (2024), https://doi.org/10.1364/JOSAA.510603.

The refractive indices n of pure formamide, 1-butanol, 2-butanol, 1,3-butanediol, 1,4-butanediol, and those of their binary mixtures, with formamide as the common component, covering the whole composition range, have been measured at temperatures (293.15, 298.15, 303.15, 308.15, 313.15, and 318.15) K. From the experimental data, the deviations in refractive index (?n) have been calculated. The variation of ?n with composition and temperature has been discussed. The extent of deviations in refractive indices for these mixtures follows the sequence 1-butanol greater than 2-butanol greater than 1,3-butanediol greater than 1,4-butanediol, and ?n decreases with an increase in temperature.

Refractive indices, extinction coefficients and fitting parameters for ellipsometry results of van der Waals materials

Porcine skin data sets of "Temperature dependence of the dielectric function of dehydrated biological samples in the THz band"

The data is stored in a way that the columns represent the following information:

Column | information 1 | Frequency [THz] 2 - 6 | real part refractive index 7 - 11 | error real part refractive index 12 - 16 | imaginary part refractive index 17 - 21 | error imaginary part refractive index 22 - 26 | absorption coefficient [1/cm] 27 - 31 | error absorption coefficient [1/cm] 32 - 36 | real part dielectric function 37 - 41 | error real part dielectric function 42 - 46| imaginary part dielectric function 47 - 51| error imaginary part dielectric function

Subsequent columns show the information for different temperatures @ 20, 22.5, 27.5, 32.5, 36.5 °C.

So that for example:

n @ 20.0°C | n @ 22.5°C | n @ 27.5°C | n @ 32.5°C | n @ 36.5°C

The data sets represent processed data.

Porous media are ubiquitous, a key component of the water cycle and locus of many biogeochemical transformations. Mapping media architecture and interstitial flows have been challenging because of the inherent difficulty of seeing through solids. Previous works used particle image velocimetry (PIV) coupled with refractive index-matching (RIM) to quantify interstitial flows, but they were limited to specialized and often toxic fluids that precluded investigating biological processes. To address this limitation, we present a low-cost and scalable method based on RIM coupled PIV (RIM-PIV) and planar laser induced fluorescence (RIM-PLIF) to simultaneously map both media architecture and interstitial velocities. Here, we store and report the data used in "A biologically friendly, low-cost and scalable method to map permeable media architecture and interstitial flow" by Hilliard et al., 2020, in Geophysical Review Letters, DOI: 10.1029/2020GL090462

This dataset supports the publication: E. Mavrona, et al (2018). Intrinsic and photo-induced properties of high refractive index azobenzene based thin films [Invited]. Optical Materials Express 8, 420-430

Abstract: Double emulsions show great potential for encapsulating active substances and protecting them against external influences. However, due to their complex structure, double emulsions tend to become unstable during storage. Research on double emulsions therefore focuses on maintaining their microstructure during their shelf life. Optical measurement methods such as Raman spectroscopy have hardly been used to date to analyze the microstructure of double emulsions mainly due to multiple scattering effects. This study concentrates on reducing scattering effects by matching the refractive indices of the individual emulsion phases. Double emulsions with adapted refractive indices are investigated using Raman spectroscopy. The refractive indices of the inner and outer water phases are varied, while the refractive index of the oil phase is kept constant. In order to evaluate the signal of the inner water phase the same amount of tracer is present in all inner phases. For individual phase boundaries of single droplets, the refractive index matching plays a minor role. However, if there are many droplets with correspondingly numerous phase boundaries, which leads to multiple scattering during the measurement, the matching has a significant influence on the signal strength of the inner phase. When measuring double emulsions, the phases should always be matched if possible, as this results in higher signals. This in turn improves the sensitivity of the measurement. TechnicalRemarks: There are seven different files: 1-5: "Auswertung_16%AN" ... "Auswertung_61%AN" Those files contain all spectroscopic raw data from the experiments and the baseline correction for the ammonium nitrate peak for each measurement Tab "Rohdatenpython": A phython programm imports the spectroscopic data from txt.files to excel (simple copy&paste) Tab "Rohdaten": Data are copied from Rohdatenphyton to this tab. Negative wavenumbers (-88 till -1) are deleted Tab "Auswertung 1.2": Integral of each ammonium nitrate peak is caluclated Tab "Auswertung 2": Summary of all peaks including x-y-diagramm, which shows the linearity between the measurements 6: "Gesamtauswertung": Tab "Diagramm_W1": Diagramm of W1-Matching Tab "Diagramm_W2": Diagramm of W2-Matching Tab "Gesamt": Summary of the measured data (Tab 16%AN ... 61%AN), refractive indizes, linearity of glycerol. It is mentioned, which data is used for which figure/table Tab "Diagramm_Residuen_W2": Residuen as function of W2-Matching Tab "Diagramm_Residuen_W1": Residuen as function of W1-Matching Tab "Multiple lineare Regression": Calculation of the mlr and residues Tab "16%AN" ... "61%AN": Copy of the tabs "Auswertung2" 7. "Spectra_Fig1": All spectroscopic raw data and diagrams regarding the substance system