entire archived database

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.

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.

Comprehensive data table of refractive index values for mineral identification in optical mineralogy studies

Repository storing the pre-computed aerosol databases for POSEIDON (https://github.com/MartianColonist/POSEIDON)

Includes:

1. Required Database: 'aerosol_database.hdf5'

- includes base aerosol collection introduced in Mullens 2024

- updated as of v1.3.1

- See the readme.txt for more details on refractive indices used to generated database.

2. Optional database: 'aerosol_directional_database.hdf5'

- included as of v1.3.1

- includes directional and temperature dependent aerosol collection introduced in Mullens & Lewis 2025

- Folder includes pdf with relevant refractive index citations

3. Optional database: 'aerosol_diamonds_database.hdf5'

- included as of v1.3.1

- includes diamond aerosols, with relevant refractive index citations.

4. Optional datbase: 'SiO2_free_logwidth_database.hdf5'

- included as of v1.3.1

- includes SiO2 precomputed properties with different lognormal log widths, with relevant refractive index citations.

5. All the refractive index txt files used to generate the aerosol databases.

- includes all the code to used to generate the grids as well. If you source any refractive indices from this zenodo, please ensure you cite where they came from.

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.

294 Global exporters importers export import shipment records of Refractive index detector with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Replication Data for: Confined transverse-electric graphene plasmons in negative refractive-index systems

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.

The data presented in this dataset is published in: Gutiérrez, Y., Dicorato, S., Ovvyan, A. P., Brückerhoff-Plückelmann, F., Resl, J., Giangregorio, M. M., Hingerl, K., Cobet, C., Schiek, M., Duwe, M., Thiesen, P. H., Pernice, W. H. P., Losurdo, M., Layered Gallium Monosulfide as Phase-Change Material for Reconfigurable Nanophotonic Components On-Chip. Adv. Optical Mater. 2023, 2301564. https://doi.org/10.1002/adom.202301564 Experimental details on the structural and optical properties available in this respository are described in the Methods section. The files have the following structure: Refractive index files - files are structured in columns as: eV nm n k Raman files - files are structured in columns as: cm-1 counts Descriptions in the files: refractive_index_amorph_GaS.txt - Refractive index (n,k) of as deposited amorphous GaS. Data shown in Figure 1b of the manuscript. refractive_index_thermally_crystallized_GaS.txt- Refractive index (n,k) of thermallycrystallized GaS. Data shown in Figure 3c,d of the manuscript. refractive_index_laser_crystallized_GaS.txt - Refractive index (n,k) of laser crystallized GaS. Data shown in Figure 3i of the manuscript. Raman_amorph_GaS - Raman spectrum of amorphous GaS. Background substracted. Data shown in Figure 1a. Raman_cryst_GaS - Raman spectrum of crystalline GaS. Data shown in Figure 1a.

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.

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.

The main context of this research is the fluid mechanical analysis of stirred chunky fruit preparations, which are typically highly loaded suspensions (ca. 50%w/w) with particles susceptible to mechanical damage. Knowledge about the transport of such particles in fluid matrices is important in natural and technical processes and can be obtained using optical measurement techniques, e.g. Particle Image Velocimetry. Matching the refractive indices of the relevant material components, a way to ensure signal reliability, is difficult for highly concentrated dispersed systems. Material properties such as plasticity and elasticity of the solid phase and the rheological behaviour of the fluid must be met simultaneously. Fluid motion across the full range of the stirred volume and the immediate surroundings of the stirrer could not be observed without successful refractive index matching of acrylic glass, stirred liquid, and suspended particles. Using the presented materials, the mechanical firmness (but not the resistance against breaking) of soft granular matter can be mimicked. The movement of gel particles in suspensions, their resulting deformation and ultimately, the inflicted damage can be observed with optical methods. The rigidity of the gels may be varied to some extent with the concentrations of the respective hydrocolloids, which, at low concentrations, have no apparent effect on the refractive index. Introducing ethanol, thickeners or other components may yield more degrees of freedom in modelling their flow behaviour.

Boothe_et_al_eLife_2017_Figure_3a_Source_DataBoothe_et_al_eLife_2017_Figure_3b_Source_DataBoothe_et_al_eLife_2017_Figure_4_Figure_Supplement_1_Source_DataBoothe_et_al_eLife_2017_Figure_4a_Source_DataBoothe_et_al_eLife_2017_Figure_4b_Source_Data

These files are used to compute parameterized cloud models with the Virga Exoplanet Cloud Model. The documentation is available here for specifically how these files are included into virga.

1. REFRACTIVE INDICES: (.refrind): contain the refractive indices of each condensate species. If you are running cloud models utilizing this data, please cite the corresponding source for each species listed below. All files are 4 columns structured as :

index, wavelength (micron), real part, imaginary part.

Virga reads in these files in this routine but you can simply use this python code:

filename = "H2O.refrind"
idummy, wave, nn, kk = np.loadtxt(open(filename,'rt').readlines(), unpack=True, usecols=[0,1,2,3])#[:-1]

2. MIE PARAMETERS: (.mieff): There are specific tutorials and functions in virga that will guide you through computing these on your own. However, we provide them here for completeness. The program that calculates these is demonstrated here. Note that each Mie parameters are averaged 6 points within the wavelength bin. You can use this function here to parse the data. Or, you can simply read the mieff files with this code:

import pandas as pd
gas = "H2O"
df = pd.read_csv(gas+".mieff",names=['wave','qscat','qext','cos_qscat'], delim_whitespace=True)

CITATIONS TO REFERENCE FOR EACH SPECIES:

KCl & ZnS

Querry, Marvin R. Optical constants of minerals and other materials from the millimeter to the ultraviolet. Chemical Research, Development & Engineering Center, US Army Armament Munitions Chemical Command, 1987.

MnS

Huffman, Donald R., and Robert L. Wild. "Optical Properties of α− M n S." Physical Review 156.3 (1967): 989.

Cr

Stashchuk, V. S., M. Ts Dobrovolskaya, and S. N. Tkachenko. "Optical properties and electronic characteristics of chromium." Optics and Spectroscopy 56 (1984): 594-596.

Na2S

Montaner, Antoine, et al. "Optical constants of sodium sulphide." Physica Status Solidi. A, Applied Research 52.2 (1979): 597-601.

Khachai, H., et al. "FP-APW+ lo calculations of the electronic and optical properties of alkali metal sulfides under pressure." Journal of Physics: Condensed Matter 21.9 (2009): 095404.

MgSiO3 & Mg2SiO4

Scott, A., and W. W. Duley. "Ultraviolet and infrared refractive indices of amorphous silicates." The Astrophysical Journal Supplement Series 105 (1996): 401.

Fe

Leksina, I., N. Penkina, and Fizik Metall Metalloved. "Optical characteristics of iron in the visual and near infrared spectral regions." Fizik. Metall. Metalloved 23 (1967): 344-345.

Al2O3

Koike, Chiyoe, et al. "Extinction spectra of corundum in the wavelengths from UV to FIR." Icarus 114.1 (1995): 203-214.

NH3

Martonchik, John V., Glenn S. Orton, and John F. Appleby. "Optical properties of NH 3 ice from the far infrared to the near ultraviolet." Applied optics 23.4 (1984): 541-547.

ALL ELSE

Th. Henning, V.B. Il'in, N.A. Krivova, B. Michel, and N.V. Voshchinnikov (1999) WWW database of optical constants for astronomy. Astron. Astrophys. Suppl. 136, 405.

C. Jaeger, V.B. Il'in, Th. Henning, H. Mutschke, D. Fabian, D.A. Semenov, and N.V. Voshchinnikov (2002) A database of optical constants of cosmic dust analogs. J. Quant. Spectrosc. Rad. Transf., accepted.

https://www.astro.uni-jena.de/Laboratory/Database/jpdoc/f-dbase.html

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)

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

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

Supplemental material to the article: "Capability of commercial trackers as compensators for the absolute refractive index of air," Precision Engineering 77, 46-64 (2022), https://doi.org/10.1016/j.precisioneng.2022.04.011. An archive file of research data which includes: a Python script which implements the calibration procedure of Section 3, together with the respective input experimental data for helium and argon gases. The script is generalized enough that any user supplying p, t_90, phi_i, and phi_f from a specific tracker setup can perform the calibration and establish absolute performance. experimental data for nitrogen gas, together with a Python script which reproduces Fig. 5. experimental data for water vapor, together with a Python script which deduces molar polarizability. This data and analysis are the basis for the reference value stated for A_R of water vapor and Fig. 6. experimental data for the refractive index of air. These data are the basis for the Edlen comparisons of Figs. 7 and 8. The datafiles also contain environmental records of p, t_90, t_dp, and x_CO2.