sesami 2.9

Characterization Tools for Porous Materials Using Nitrogen/Argon Adsorption

SESAMI

Usage

from SESAMI.bet import fitbet

BET_dict, BET_ESW_dict = fitbet(csv_file="example.csv", columns=["Pressure","Loading"],
                                            adsorbate="N2", p0=1e5, T=77,
                                            R2_cutoff=0.9995, R2_min=0.998,
                                            font_size=12, font_type="DejaVu Sans",
                                            legend=True, dpi=600, save_fig=True, verbose=False)
print(BET_dict, BET_ESW_dict)

• csv_file: N2 isotherm csv file
• columns: [Pressure, Loading], 2 columns, one for rpessure (unit: Pa), one for uptake (unit: mmol/g)
• adsorbate: N2, Ar or other
• p0: if other
• T: test temperature if other
• R2_cutoff (default: 0.9995): The value of R2 beyond which we deem R2 ceases to have a bearing on the goodness of the linear region.
• R2_min (default: 0.998): R2 value a chosen region must have to be termed linear
• font_size: word size in figure
• font_type: word type in figure
• legend: with legend in figure or not
• dpi: dpi in figure
• save_fig: save png or not (local folder)
• verbose: print detail or not

from SESAMI.predict import betml

MLBET = betml(csv_file="example.csv", columns=["Pressure","Loading"], verbose=False)
print(MLBET) 

• csv_file: N2 isotherm csv file, we recommend the columns name of pressure and uptake is Pressure and Loading, and the 1st column should be pressure with unit as Pa and 2nd column should be uptake with unit as mmol/g
• columns: [Pressure, Loading], 2 columns, one for rpessure (unit: Pa), one for uptake (unit: mmol/g)
• verbose: print detail or not

Website

SESAMI-APP

Reference

SESAMI-APP: SESAMI APP: An Accessible Interface for Surface Area Calculation of Materials from Adsorption Isotherms
SESAMI 1.0: Surface Area Determination of Porous Materials Using the Brunauer-Emmett-Teller (BET) Method: Limitations and Improvements
SESAMI 2.0 (Machine Learning Model): Beyond the BET Analysis: The Surface Area Prediction of Nanoporous Materials Using a Machine Learning Method