Towards Quantifiable Evaluation of Contributors to the Electromechanical Signal in Piezoresponse Force Microscopy

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NON-TECHNICAL DESCRIPTION: Coupling between electrical and mechanical impulses underlies the basic behavior of many sensors and actuators. At macroscale, identification of the physical phenomena resulting in electromechanical coupling is often straightforward. At smaller length scales (i.e. tens of nanometer and below) a multiplicity of contributors can emerge even in otherwise well-identified materials. However, separation of such contributors is not achievable except through costly and time-intensive experiments, not always viable due to time or resolution constraints. The resulting dearth of understanding of functional materials at the nanoscale has often limited miniaturization of engineering devices. This project leverages and integrates big data analytics approaches into advancement of the scientific discovery in functional materials: specifically, data science is used to analyze multi-dimensional datasets of complex and coupled parameters. The approach ultimately identifies different “signatures” for the different contributors to the electromechanical response of materials at the nanoscale. The strategies developed could be equally impactful for understanding and design of next-generation microelectronic, photovoltaic and quantum computing materials; miniaturized sensors, actuators, and healthcare transducers; organic semiconductors and rechargeable batteries among others. The students trained through this project gain expertise in data science, materials science and microelectronics, typically finding employment in high-tech companies, space industry, and data analytics across different disciplines.TECHNICAL DETAILS: This research aims to probe and quantify the different contributions to the nanoscale electromechanical response of dielectric materials (including piezoelectric, electrochemical, and charge transport effects), and identifying the respective electro-chemo-mechanical and viscoelastic “fingerprints”. The approach is based on a combination of resonant, voltage-modulated atomic force microscopy (VM-AFM) techniques, resulting in multi-dimensional data sets tracking different functional parameters, and use of big data analytics approaches to analyze the above. The methodologies developed are applicable to polarization switching mechanisms (ferroelectricity), piezoelectricity, electrochemical deformations, and electronic/ionic flows in a wide range of materials. The work is of significant importance for probing of any material where interplay of multiple physical and chemical phenomena results in a measured surface displacement. Hence, the strategies developed are particularly impactful for materials with small electromechanical signatures or particularly reduced dimensions, i.e., two-dimensional, organic and/or biological ferroelectric and piezoelectric materials, organic-inorganic photovoltaics, organic semiconductors, and Li-ion batteries. An integral part of this project is the recruitment and retention of women and minorities in science and engineering. This objective is achieved through outreach, mentorship, and research and education activities targeted for graduate and undergraduate students in cutting-edge research techniques at the interface of data and materials science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.