D2O:C3H5(OH)3 (Heavy water:Glycerol)
5 datasets, together covering 0.4–0.71 µm. Default: Sarkar et al. 2022: n 0.40–0.71 µm; 25 wt% glycerol.
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Use it in Lightsmith
Load this data in Python, simulate in one more line
pip install lightsmith
import lightsmith as ls
# D2O:C3H5(OH)3 (Heavy water:Glycerol): Sarkar et al. 2022: n 0.40–0.71 µm; 25 wt% glycerol
d2o_c3h5_oh_3 = ls.fetch_refractiveindex("other", "D2O-C3H5_OH_3", "Sarkar-25", ref="c5c2f188e848453def5970e347399d653df2ffc2")
print(d2o_c3h5_oh_3.range_nm) # (400, 710) nm
eps = d2o_c3h5_oh_3.eps([633]) # complex permittivityDownloads the original record once (pinned to the imported snapshot) and caches it.
import json, urllib.request
import lightsmith as ls
url = "https://materials.lightsmith.dev/v1/datasets/ds_a59dbcf88c1d/lightsmith"
d2o_c3h5_oh_3 = ls.Tabulated.model_validate(json.load(urllib.request.urlopen(url)))
print(d2o_c3h5_oh_3.source) # citation and provenance travel with the dataAny dataset here, as the library's Tabulated material (d2o-c3h5-oh-3/sarkar-2022-25).
# ... load d2o_c3h5_oh_3 as above, then:
sim = ls.Simulation(
particles=[ls.Particle(shape=ls.Sphere(radius_nm=50), material=d2o_c3h5_oh_3)],
medium_index=1.0,
wavelengths=ls.WavelengthRange(start_nm=400, stop_nm=710, num=81),
)
res = ls.solve(sim) # exact Mie for a sphere
print(res.q_ext) # extinction efficiency at each wavelengthSame spec runs with DDA, FDTD, BEM or T-matrix: ls.compare(sim, ["mie", "dda"]).
Lightsmith is open source (MIT): Mie, T-matrix, BEM, DDA, FDTD, RCWA, TMM, mode solvers and inverse design behind one spec, on CPU or GPU. GitHub.
About D2O:C3H5(OH)3 (Heavy water:Glycerol)
Other names: D2O-C3H5_OH_3, OH)3 (Heavy water:Glycerol
API: /v1/materials/mat_7767fe3ff467 · slug d2o-c3h5-oh-3