H2O:C3H5(OH)3 (Water:Glycerol)
4 datasets, together covering 0.4–0.71 µm. Default: Gupta et al. 2022: n 0.40–0.71 µm; 0 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
# H2O:C3H5(OH)3 (Water:Glycerol): Gupta et al. 2022: n 0.40–0.71 µm; 0 wt% glycerol
h2o_c3h5_oh_3 = ls.fetch_refractiveindex("other", "H2O-C3H5_OH_3", "Gupta-0", ref="c5c2f188e848453def5970e347399d653df2ffc2")
print(h2o_c3h5_oh_3.range_nm) # (400, 710) nm
eps = h2o_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_7988160b7cb2/lightsmith"
h2o_c3h5_oh_3 = ls.Tabulated.model_validate(json.load(urllib.request.urlopen(url)))
print(h2o_c3h5_oh_3.source) # citation and provenance travel with the dataAny dataset here, as the library's Tabulated material (h2o-c3h5-oh-3/gupta-2022-0).
# ... load h2o_c3h5_oh_3 as above, then:
sim = ls.Simulation(
particles=[ls.Particle(shape=ls.Sphere(radius_nm=50), material=h2o_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 H2O:C3H5(OH)3 (Water:Glycerol)
Other names: H2O-C3H5_OH_3, OH)3 (Water:Glycerol
API: /v1/materials/mat_fa782f47b228 · slug h2o-c3h5-oh-3