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Progress in the experimental and computational methods of work function evaluation of materials: A review
O C Olawole1, D K De2, O F Olawole3
1Department of Physics, Covenant University, Ota, Ogun State, Nigeria.
Heliyon
|November 7, 2022
Summary
This review highlights Kelvin probe force microscopy (KPFM) as the top experimental method for measuring work function. Advanced computational tools like HTDFTML and CBMLFP offer cost-effective predictions for complex materials.
Area of Science:
- Surface Science
- Materials Science
- Condensed Matter Physics
Background:
- Work function is critical for electron behavior, influencing corrosion rates and device engineering.
- Understanding work function is essential for developing photosensitive and electron-emitting devices.
- Various experimental and theoretical methods exist for work function measurement, each with pros and cons.
Purpose of the Study:
- To review and compare experimental methods and theoretical models for work function measurement.
- To identify the most suitable techniques for accurate and efficient work function determination.
- To discuss the computational cost and predictive capabilities of different theoretical models.
Main Methods:
- Review of existing literature on experimental work function measurement techniques.
- Analysis of theoretical models including Stabilized Jellium Model (SJM), Metallic Plasma Model (MPM), and Density Functional Theory (DFT).
- Evaluation of advanced computational approaches: High-throughput DFT and Machine Learning (HTDFTML), and Combined Bayesian Machine Learning and First Principle (CBMLFP).
Main Results:
- Kelvin probe force microscopy (KPFM) is identified as the most suitable experimental technique.
- Computational methods for predicting work functions often incur high computational costs.
- SJM, MPM, and DFT show varying capabilities and costs for predicting work functions across different metal types.
- HTDFTML and CBMLFP offer efficient and cost-effective prediction of work functions, especially for extreme material surfaces.
Conclusions:
- HTDFTML and CBMLFP are recommended for exploring work functions and surface energy in complex materials due to their low computational cost.
- These advanced methods provide a pathway for efficient materials discovery and design.
- Further research utilizing HTDFTML and CBMLFP can accelerate advancements in surface science and device engineering.
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