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High Throughput Light Absorber Discovery, Part 1: An Algorithm for Automated Tauc Analysis.

Santosh K Suram¹, Paul F Newhouse¹, John M Gregoire¹

¹Joint Center for Artificial Photosynthesis, California Institute of Technology , Pasadena California 91125, United States.

ACS Combinatorial Science

|September 24, 2016

Summary

This summary is machine-generated.

An automated Tauc analysis algorithm efficiently estimates band gap energies from optical spectroscopy data. This method accelerates the discovery of novel optical materials for solar energy applications.

Keywords:

UV−vis spectroscopy band gap combinatorial science high-throughput screening optical spectroscopy solar fuels

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Area of Science:

Materials Science
Optical Materials
Spectroscopy

Background:

High-throughput experimentation accelerates the discovery of materials with desired properties.
Automated data processing is crucial for handling large experimental datasets in materials science.
Accurate band gap energy determination is vital for optical materials used in solar energy and other technologies.

Purpose of the Study:

To develop and validate an automated Tauc analysis algorithm for estimating band gap energies.
To enable efficient data processing in high-throughput optical spectroscopy.
To mimic expert scientist judgment in analyzing optical spectroscopy data.

Main Methods:

Development of an automated Tauc analysis algorithm.
Application of the algorithm to high-throughput optical spectroscopy data.
Comparison of automated band gap energy estimations with expert analysis for 60 optical spectra.

Main Results:

The automated Tauc analysis algorithm accurately estimates band gap energies.
The algorithm successfully identified direct and indirect band gaps in Fe2O3, Cu2V2O7, and BiVO4.
A strong agreement was observed between automated and expert estimations for direct-allowed band gaps.

Conclusions:

The developed automated Tauc analysis algorithm is a reliable tool for high-throughput band gap determination.
This automation facilitates faster discovery and characterization of optical materials.
The algorithm enhances the efficiency of materials research for applications like solar energy.