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Python Notebook Download

Useful FTIR Functions for Density and Molar Absorptivity

• This Jupyter notebook demonstrates the use of useful Python functions for calculating density and molar absorptivity.

• The Jupyter notebook and data can be accessed here: https://github.com/SarahShi/PyIRoGlass/blob/main/docs/examples/ftir_functions/.

• You need to have the PyIRoGlass PyPi package on your machine once. If you have not done this, please uncomment (remove the #) symbol and run the cell below.

#!pip install PyIRoGlass

Load Python Packages and Data

Load Python Packages

# Import packages

import os
import sys
import glob
import numpy as np
import pandas as pd

import PyIRoGlass as pig

from IPython.display import Image

import matplotlib
from matplotlib import pyplot as plt
from matplotlib import rc, cm

%matplotlib inline
%config InlineBackend.figure_format = 'retina'

pig.__version__

'0.6.1'

Set paths to data

# Change paths to direct to folder with transmission FTIR spectra

CHEMTHICK_PATH = 'ChemThick.csv'
print(CHEMTHICK_PATH)

ChemThick.csv

Load composition and thickness data

The file names from the spectra (what comes before the .CSV) are important when we load in melt compositions and thicknesses. Unique identifiers identify the same samples. Make sure that this ChemThick.CSV file has the same sample names as the spectra you load in.

loader = pig.SampleDataLoader(chemistry_thickness_path=CHEMTHICK_PATH)
chemistry, thickness = loader.load_chemistry_thickness()

Display the dataframe of glass compositions

chemistry

	SiO2	TiO2	Al2O3	Fe2O3	FeO	MnO	MgO	CaO	Na2O	K2O	P2O5
Sample
AC4_OL49_021920_30x30_H2O_a	52.34	1.04	17.92	1.93	7.03	0.20	3.63	7.72	4.25	0.78	0.14
AC4_OL53_101220_256s_30x30_a	47.95	1.00	18.88	2.04	7.45	0.19	4.34	9.84	3.47	0.67	0.11
STD_D1010_012821_256s_100x100_a	51.41	1.26	16.58	0.00	7.58	0.00	7.57	10.98	3.01	0.37	0.18

We’re ready to use the Density_Calculation function now. We input the arguments:

• chemistry: Glass composition loaded from ChemThick

• T_ROOM: Room temperature at time of FTIR analysis, given the sensitivity of density to T.

• P_ROOM: Room pressure at time of FTIR analysis, given the sensitivity of density to P.

and output:

• mol - dataframe of moles of oxide

• density - dataframe of densities

T_ROOM = 25 # C
P_ROOM = 1 # Bar
mol, density = pig.calculate_density(chemistry, T_ROOM, P_ROOM)

mol

	SiO2	TiO2	Al2O3	Fe2O3	FeO	MnO	MgO	CaO	Na2O	K2O	P2O5
Sample
AC4_OL49_021920_30x30_H2O_a	0.871172	0.013022	0.175755	0.012086	0.097851	0.002819	0.090065	0.137667	0.068572	0.008280	0.000986
AC4_OL53_101220_256s_30x30_a	0.798103	0.012521	0.185171	0.012775	0.103697	0.002678	0.107681	0.175472	0.055987	0.007113	0.000775
STD_D1010_012821_256s_100x100_a	0.855692	0.015776	0.162613	0.000000	0.105506	0.000000	0.187821	0.195801	0.048565	0.003928	0.001268

density

Sample
AC4_OL49_021920_30x30_H2O_a        2748.987195
AC4_OL53_101220_256s_30x30_a       2820.556711
STD_D1010_012821_256s_100x100_a    2831.885143
dtype: float64

We’re ready to use the Epsilon_Calculation function now. We input the arguments:

• chemistry: Glass composition loaded from ChemThick.

• T_ROOM: Room temperature at time of FTIR analysis, given the sensitivity of density to T.

• P_ROOM: Room pressure at time of FTIR analysis, given the sensitivity of density to P.

and output:

• epsilon - dataframe of molar absorptivities with their uncertainties.

T_ROOM = 25 # C
P_ROOM = 1 # Bar
epsilon = pig.calculate_epsilon(chemistry, T_ROOM, P_ROOM)
epsilon

	Tau	Eta	epsilon_H2Ot_3550	sigma_epsilon_H2Ot_3550	epsilon_H2Om_1635	sigma_epsilon_H2Om_1635	epsilon_CO2	sigma_epsilon_CO2	epsilon_H2Om_5200	sigma_epsilon_H2Om_5200	epsilon_OH_4500	sigma_epsilon_OH_4500
AC4_OL49_021920_30x30_H2O_a	0.705990	0.499048	66.142594	7.503769	37.322108	8.645060	258.429949	18.362798	1.009458	0.300803	0.861196	0.279571
AC4_OL53_101220_256s_30x30_a	0.682895	0.389547	64.493655	7.380834	34.452486	8.504269	293.261300	16.287120	0.901474	0.295829	0.779611	0.274924
STD_D1010_012821_256s_100x100_a	0.658501	0.331580	62.751984	7.251813	31.421482	8.354348	311.700491	15.127604	0.787418	0.290510	0.693438	0.270004

There are a few things to note. Tau references the \(\mathrm{(Si^{4+}+Al^{3+})/Total Cations}\) value and eta references the \(\mathrm{(Na^{+}/(Na^{+}+Ca^{2+}))}\) value. Each column with the prefix epsilon represents the mean molar absorptivity, sigma_epsilon represents the uncertainty on the molar absorptivity.