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.

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

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

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)

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.

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.

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

Reliable, directly measured optical properties of astrophysical ice analogs in the infrared (IR) and terahertz (THz) range are missing. These parameters are of great importance to model the dust continuum radiative transfer in dense and cold regions, where thick ice mantles are present, and are necessary for the interpretation of future observations planned in the far-IR region. Coherent THz radiation allows direct measurement of the complex dielectric function (refractive index) of astrophysically relevant ice species in the THz range. The time-domain waveforms and the frequency-domain spectra of reference samples of CO ice, deposited at a temperature of 28.5K and annealed to 33K at different thicknesses, have been recorded. A new algorithm is developed to reconstruct the real and imaginary parts of the refractive index from the time-domain THz data. The complex refractive index in the wavelength range of 1mm-150um (0.3-2.0THz) has been determined for the studied ice samples, and compared with available data found in the literature. The developed algorithm of reconstructing the real and imaginary parts of the refractive index from the time-domain THz data enables, for the first time, the determination of optical properties of astrophysical ice analogs without using the Kramers-Kronig relations. The obtained data provide a benchmark to interpret the observational data from current ground based facilities as well as future space telescope missions, and have been used to estimate the opacities of the dust grains in presence of CO ice mantles.

Dataset of optical constants in the extreme ultraviolet (EUV) spectral range, including 13.5nm, obtained from reflectivity measurements.

Refractive index of ITO (Indium Tin Oxidide) provided by Optics Balzers. Dataset contains the raw spectroscopic ellipsometry data as well as the complex refractive index obtained by fitting.

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.

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.

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)

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

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.

Brown mushroom data set 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.

This dataset contains results presented in the following manuscript: Kim and Guck, 'The relative densities of cell cytoplasm, nucleoplasm, and nucleoli are robustly conserved during cell cycle and drug perturbations,' available on bioRxiv (https://doi.org/10.1101/2020.04.14.040774) and includes two comma separated text files ('*.txt') containing analysis data from optical diffraction tomography. The text files includes 1. Cell Type (HeLa FUCCI or RPE FUCCI)2. Treatment (control, cytoD, nocodazole, TSA, and anacardic acid)3. Cell cycle (G1, Early S, G2)4-7. Mean RI of cytoplasm, nucleoplasm, perinuclear cytoplasm, and nucleoli of individual cell8-9. Dry mass and volume of nucleoplasm10-11. Dry mass and volume of cytoplasm12-13. Dry mass and volume of nucleolus14-15. Sphericity of cell and nucleus (Fig4_5_HeLa_Conditions.txt only)

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.