Replication data for : Extending the use of normal hemispherical transmittance (T_NH) measurements by modelling 3D multiple scattering radiative transfer

Résumé 0

This archive contains the data allowing to reproduce the results in the article referenced elsewhere in the present metadata, whose abstract is the following : Spectrophotometers or optical benches using integrating spheres to measure normal-hemispherical transmittance T_NH are widespread laboratory equipments. Although it is known that they cannot be used for "highly turbid" samples, because multiple scattering may lead transmitted radiation to miss the entrance of the integrating sphere, very little is generally known about their exact validity range. Here we present a method to characterize the validity range of any such spectrophotometer and observe that most of them fail to measure T_NH for scattering optical thickness above 0.2 (i.e. for T_NH < 0.9 in the case of non absorbing media). We also show how it is possible to continue using spectrophotometers even outside their T_NH measurement validity range, without any calibration, thanks to a proper simulation of radiative transfer and geometrical optics. We make available the corresponding radiative transfer simulation tools as open access codes, that have been developed for a straightforward implementation on a wide range of experimental setups. The method is validated on three different spectrophotometers or optical benches using standardized latex microspheres, then its practical implementation is illustrated in the case of semi-conductor particles and photosynthetic microalgae. Errors in analysis arising from the misuse of such optical devices are discussed throughout the article. Here we provide both the configuration files to run the radiative transfer simulations and some codes generating these files. Any publication using these data or codes should make a proper reference to the above article. The data in the present archive are .xml files that have to be used as input files for the starlyx program (see http://starlyx.org and https://gitlab.com/photonlyx/starlyx) that computes the signal received by the sensor for each wavelength specified in the source. Content: ======== * The data corresponding to each experimental setup presented in the above article are gathered in separate folder named experimental-setup-[#] * Each folder experimental-setup-[#] contains: - the .obj files defining the geometry of the experimental setup, - in the folder section4-Methods, the .xml files used in the section 4 of the article; - in the folder Figure7, a program named scene_generator generating the .xml files used to produce the Figure 7 of the article, the .xml files it has generated, and a blank.xml file used to compute the baseline; - in the folder Table1, a program named scene_generator generating the .xml files used to produce the Table 1 of the article, the .xml files it has generated, and a blank.xml file used to compute the baseline; - in the folder Figures-8-9-10-11, the standard .xml file used to produce the remaining figures of the article. Users only require to modify the line 31 of the .xml file, where radiative properties of the sample are defined, according to the scattering optical thickness they want to simulate. In the folders Figure7 and Table1, the convention for naming the .xml files is the following: sample_diameter[particle dimameter in micrometer]um_concentration[particle concentration in g/l, with the letter p separating units and decimals]gL.xml Usage: ====== After installation of starlyx by following the procedure explained in https://gitlab.com/photonlyx/starlyx, open a ternimal in one folder containing a file [file_name].xml and invoke starlyx [file_name].xml Outputs of the computation are written in the file sensors.csv, containing the value of the signal for each wavelength of the source. Except for the folder section4-Methods, results presented in the article use a baseline using a blank sample. Is this case invoke first starlyx sample_[...].xml and save the results that have been written in sensors.csv, then invoke starlyx blank.xml and save the results that have been written in sensors.csv. Finally, divide the results obtained with the first command by the results obtained with the second one to obtain what is defined as the device model in the paper.