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

对压电响应力显微镜中机电信号贡献者的量化评估

• 批准号: 2026976
• 负责人: Vanessa Smet
• 金额: $ 44.01万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026976&HistoricalAwards=false
• 关键词: Towards; Quantifiable; Evaluation; Contributors; Electromechanical

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.

非技术描述：电脉冲和机械脉冲之间的耦合是许多传感器和执行器的基本行为的基础。在宏观尺度上，识别导致机电耦合的物理现象通常是简单的。在较小的长度尺度（即数十纳米及以下），即使在其他识别良好的材料中也可以出现多种贡献者。然而，除非通过昂贵且耗时的实验，否则无法实现此类贡献者的分离，但由于时间或分辨率的限制，这些实验并不总是可行的。由此导致的对纳米级功能材料理解的缺乏往往限制了工程设备的小型化。该项目利用并整合了大数据分析方法，以推进功能材料的科学发现：具体而言，数据科学用于分析复杂和耦合参数的多维数据集。该方法最终确定不同的“签名”的不同贡献者的机电响应的材料在纳米级。所开发的策略对于理解和设计下一代微电子、光伏和量子计算材料、小型化传感器、致动器和医疗传感器、有机半导体和可充电电池等都具有同样的影响力。通过该项目培训的学生将获得数据科学、材料科学和微电子方面的专业知识，通常会在高科技公司、航天工业和不同学科的数据分析中找到工作。本研究旨在探讨及量化介电材料奈米机电响应的不同贡献（包括压电、电化学和电荷传输效应），并识别相应的电化学机械和粘弹性“指纹”。该方法基于共振电压调制原子力显微镜（VM-AFM）技术的组合，从而产生跟踪不同功能参数的多维数据集，并使用大数据分析方法来分析上述内容。开发的方法适用于极化开关机制（铁电性），压电性，电化学变形，和电子/离子流在广泛的材料。这项工作对于探测任何材料都具有重要意义，其中多种物理和化学现象的相互作用导致测量的表面位移。因此，所开发的策略对于具有小机电特征或特别减小的尺寸的材料特别有效，即，二维、有机和/或生物铁电和压电材料、有机-无机光致发光材料、有机半导体和锂离子电池。该项目的一个组成部分是招募和留住科学和工程领域的女性和少数族裔。这一目标是通过针对研究生和本科生在数据和材料科学界面的尖端研究技术方面的推广、指导以及研究和教育活动来实现的。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Mask or Enhance: Data Curation Aiding the Discovery of Piezoresponse Force Microscopy Contributors

掩盖或增强：数据管理有助于发现压电响应力显微镜贡献者

• DOI: 10.1002/apxr.202200090
• 发表时间: 2023
• 期刊: Advanced Physics Research
• 作者: Ligonde, Gardy Kevin;Williams, Kerisha N.;Gaponenko, Iaroslav;Bassiri‐Gharb, Nazanin
• 通讯作者: Bassiri‐Gharb, Nazanin

Maximizing Information: A Machine Learning Approach for Analysis of Complex Nanoscale Electromechanical Behavior in Defect‐Rich PZT Films

• DOI: 10.1002/smtd.202100552
• 发表时间: 2021-10
• 期刊: Small Methods
• 影响因子: 12.4
• 作者: Fengyuan Zhang;Kerisha N. Williams;David Edwards;A. Naden;Yulian Yao;S. Neumayer;Amit Kumar;B. Rodriguez;N. Bassiri‐Gharb