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Hollenbach JD, Pate CM, Jia H, Hart JL, Clancy P, Taheri ML. Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning. Sci Rep 2024; 14:17881. [PMID: 39095485 PMCID: PMC11297154 DOI: 10.1038/s41598-024-66902-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 07/05/2024] [Indexed: 08/04/2024] Open
Abstract
In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale.
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Affiliation(s)
- Jonathan D Hollenbach
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Cassandra M Pate
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Haili Jia
- Department of Chemical and Biomolecular and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - James L Hart
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Paulette Clancy
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemical and Biomolecular and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Mitra L Taheri
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
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2
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Park JH, Wang CPJ, Lee HJ, Hong KS, Ahn JH, Cho YW, Lee JH, Seo HS, Park W, Kim SN, Park CG, Lee W, Kim TH. Uniform Gold Nanostructure Formation via Weakly Adsorbed Gold Films and Thermal Annealing for Reliable Localized Surface Plasmon Resonance-Based Detection of DNase-I. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2302023. [PMID: 37246275 DOI: 10.1002/smll.202302023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/04/2023] [Indexed: 05/30/2023]
Abstract
Deoxyribonuclease-I (DNase-I), a representative endonuclease, is an important biomarker for the diagnosis of infectious diseases and cancer progression. However, enzymatic activity decreases rapidly ex vivo, which highlights the need for precise on-site detection of DNase-I. Here, a localized surface plasmon resonance (LSPR) biosensor that enables the simple and rapid detection of DNase-I is reported. Moreover, a novel technique named electrochemical deposition and mild thermal annealing (EDMIT) is applied to overcome signal variations. By taking advantage of the low adhesion of gold clusters on indium tin oxide substrates, both the uniformity and sphericity of gold nanoparticles are increased under mild thermal annealing conditions via coalescence and Ostwald ripening. This ultimately results in an approximately 15-fold decrease in LSPR signal variations. The linear range of the fabricated sensor is 20-1000 ng mL-1 with a limit of detection (LOD) of 127.25 pg mL-1 , as demonstrated by spectral absorbance analyses. The fabricated LSPR sensor stably measured DNase-I concentrations from samples collected from both an inflammatory bowel disease (IBD) mouse model, as well as human patients with severe COVID-19 symptoms. Therefore, the proposed LSPR sensor fabricated via the EDMIT method can be used for early diagnosis of other infectious diseases.
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Affiliation(s)
- Joon-Ha Park
- School of Integrative Engineering, Chung-Ang University, 06974, Seoul, Republic of Korea
| | - Chi-Pin James Wang
- Department of Biomedical Engineering, SKKU Institute for Convergence, Sungkyunkwan University (SKKU), 16419, Suwon, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, SKKU Institute for Convergence, Sungkyunkwan University (SKKU), 16419, Suwon, Republic of Korea
| | - Hye-Jin Lee
- Department of Chemistry, Sungkyunkwan University, 16419, Suwon, Republic of Korea
| | - Kyung Soo Hong
- Division of Pulmonology and Allergy, Department of Internal Medicine, College of Medicine, Yeungnam University, Regional Center for Respiratory Diseases, Yeungnam University Medical Center, 42415, Daegu, Republic of Korea
| | - Jung Hong Ahn
- Division of Pulmonology and Allergy, Department of Internal Medicine, College of Medicine, Yeungnam University, Regional Center for Respiratory Diseases, Yeungnam University Medical Center, 42415, Daegu, Republic of Korea
| | - Yeon-Woo Cho
- School of Integrative Engineering, Chung-Ang University, 06974, Seoul, Republic of Korea
| | - Jeong-Hyeon Lee
- School of Integrative Engineering, Chung-Ang University, 06974, Seoul, Republic of Korea
| | - Hee Seung Seo
- Department of Biomedical Engineering, SKKU Institute for Convergence, Sungkyunkwan University (SKKU), 16419, Suwon, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, SKKU Institute for Convergence, Sungkyunkwan University (SKKU), 16419, Suwon, Republic of Korea
| | - Wooram Park
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Seoburo 2066, Suwon, Gyeonggi, 16419, Republic of Korea
| | - Se-Na Kim
- Research and Development Center, MediArk Inc., Cheongju, Chungbuk, 28644, Republic of Korea
- Department of Industrial Cosmetic Science, College of Bio-Health University System, Chungbuk National University, Cheongju, Chungbuk, 28644, Republic of Korea
| | - Chun Gwon Park
- Department of Biomedical Engineering, SKKU Institute for Convergence, Sungkyunkwan University (SKKU), 16419, Suwon, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, SKKU Institute for Convergence, Sungkyunkwan University (SKKU), 16419, Suwon, Republic of Korea
- Biomedical Institute for Convergence at SKKU (BICS), Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Wonhwa Lee
- Department of Chemistry, Sungkyunkwan University, 16419, Suwon, Republic of Korea
| | - Tae-Hyung Kim
- School of Integrative Engineering, Chung-Ang University, 06974, Seoul, Republic of Korea
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3
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Yaman MY, Kalinin SV, Guye KN, Ginger DS, Ziatdinov M. Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023:e2205893. [PMID: 36942857 DOI: 10.1002/smll.202205893] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/27/2023] [Indexed: 06/18/2023]
Abstract
The application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.
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Affiliation(s)
- Muammer Y Yaman
- Department of Chemistry, University of Washington, Seattle, WA, 98195, USA
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN, 37996, USA
| | - Kathryn N Guye
- Department of Chemistry, University of Washington, Seattle, WA, 98195, USA
| | - David S Ginger
- Department of Chemistry, University of Washington, Seattle, WA, 98195, USA
- Physical Sciences Division, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
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4
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Ziatdinov M, Ghosh A, Wong CY, Kalinin SV. AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00555-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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5
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Liu Y, Kelley KP, Vasudevan RK, Zhu W, Hayden J, Maria JP, Funakubo H, Ziatdinov MA, Trolier-McKinstry S, Kalinin SV. Automated Experiments of Local Non-Linear Behavior in Ferroelectric Materials. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2204130. [PMID: 36253123 DOI: 10.1002/smll.202204130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/19/2022] [Indexed: 06/16/2023]
Abstract
An automated experiment in multimodal imaging to probe structural, chemical, and functional behaviors in complex materials and elucidate the dominant physical mechanisms that control device function is developed and implemented. Here, the emergence of non-linear electromechanical responses in piezoresponse force microscopy (PFM) is explored. Non-linear responses in PFM can originate from multiple mechanisms, including intrinsic material responses often controlled by domain structure, surface topography that affects the mechanical phenomena at the tip-surface junction, and the presence of surface contaminants. Using an automated experiment to probe the origins of non-linear behavior in ferroelectric lead titanate (PTO) and ferroelectric Al0.93 B0.07 N films, it is found that PTO shows asymmetric nonlinear behavior across a/c domain walls and a broadened high nonlinear response region around c/c domain walls. In contrast, for Al0.93 B0.07 N, well-poled regions show high linear piezoelectric responses, when paired with low non-linear responses regions that are multidomain show low linear responses and high nonlinear responses. It is shown that formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses allows for establishing the preponderant physical mechanisms behind the non-linear behaviors, suggesting that automated experiments can potentially discern between competing physical mechanisms. This technique can also be extended to electron, probe, and chemical imaging.
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Affiliation(s)
- Yongtao Liu
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Kyle P Kelley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Rama K Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Wanlin Zhu
- Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
| | - John Hayden
- Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Jon-Paul Maria
- Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
- Center for Dielectrics and Piezoelectrics, Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Hiroshi Funakubo
- Department of Material Science and Engineering, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
| | - Maxim A Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Susan Trolier-McKinstry
- Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
- Center for Dielectrics and Piezoelectrics, Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN, 37916, USA
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Roccapriore KM, Kalinin SV, Ziatdinov M. Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203422. [PMID: 36344455 PMCID: PMC9798976 DOI: 10.1002/advs.202203422] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/08/2022] [Indexed: 06/16/2023]
Abstract
Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here, this work develops and experimentally implements a deep kernel learning (DKL) workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics-based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization (BO) methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure-property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk- and edge plasmon discovery in MnPS3 , a lesser-known beam-sensitive layered 2D material. This approach is universal, can be directly used as-is with any specimen, and is expected to be applicable to any probe-based microscopic techniques including other STEM modalities, scanning probe microscopies, chemical, and optical imaging.
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Affiliation(s)
- Kevin M. Roccapriore
- Center for Nanophase Materials SciencesOak Ridge National LaboratoryOak RidgeTN37831USA
| | - Sergei V. Kalinin
- Center for Nanophase Materials SciencesOak Ridge National LaboratoryOak RidgeTN37831USA
- Department of Materials Science and EngineeringUniversity of TennesseeKnoxvilleTN37916USA
| | - Maxim Ziatdinov
- Center for Nanophase Materials SciencesOak Ridge National LaboratoryOak RidgeTN37831USA
- Computational Sciences and Engineering DivisionOak Ridge National LaboratoryOak RidgeTN37831USA
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7
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Roccapriore KM, Dyck O, Oxley MP, Ziatdinov M, Kalinin SV. Automated Experiment in 4D-STEM: Exploring Emergent Physics and Structural Behaviors. ACS NANO 2022; 16:7605-7614. [PMID: 35476426 DOI: 10.1021/acsnano.1c11118] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Automated experiments in 4D scanning transmission electron microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials. Deep kernel learning enables active learning of the relationship between local structure and 4D-STEM-based descriptors. With this, efficient and "intelligent" probing of dissimilar structural elements to discover desired physical functionality is made possible. This approach allows effective navigation of the sample in an automated fashion guided by either a predetermined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. We verify the approach first on preacquired 4D-STEM data and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene and subsequently extended toward a lesser-known layered 2D van der Waals material, MnPS3. This approach establishes a pathway for physics-driven automated 4D-STEM experiments that enable probing the physics of strongly correlated systems and quantum materials and devices, as well as exploration of beam-sensitive materials.
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Affiliation(s)
- Kevin M Roccapriore
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Ondrej Dyck
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Mark P Oxley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37916, United States
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8
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Experimental discovery of structure–property relationships in ferroelectric materials via active learning. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00460-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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9
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Xu X, Aggarwal D, Shankar K. Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:633. [PMID: 35214962 PMCID: PMC8874423 DOI: 10.3390/nano12040633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 02/06/2022] [Accepted: 02/08/2022] [Indexed: 02/06/2023]
Abstract
Advances in plasmonic materials and devices have given rise to a variety of applications in photocatalysis, microscopy, nanophotonics, and metastructures. With the advent of computing power and artificial neural networks, the characterization and design process of plasmonic nanostructures can be significantly accelerated using machine learning as opposed to conventional FDTD simulations. The machine learning (ML) based methods can not only perform with high accuracy and return optical spectra and optimal design parameters, but also maintain a stable high computing efficiency without being affected by the structural complexity. This work reviews the prominent ML methods involved in forward simulation and inverse design of plasmonic nanomaterials, such as Convolutional Neural Networks, Generative Adversarial Networks, Genetic Algorithms and Encoder-Decoder Networks. Moreover, we acknowledge the current limitations of ML methods in the context of plasmonics and provide perspectives on future research directions.
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Affiliation(s)
| | | | - Karthik Shankar
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; (X.X.); (D.A.)
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10
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Roccapriore KM, Cho SH, Lupini AR, Milliron DJ, Kalinin SV. Sculpting the Plasmonic Responses of Nanoparticles by Directed Electron Beam Irradiation. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2105099. [PMID: 34761528 DOI: 10.1002/smll.202105099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 09/09/2021] [Indexed: 06/13/2023]
Abstract
Spatial confinement of matter in functional nanostructures has propelled these systems to the forefront of nanoscience, both as a playground for exotic physics and quantum phenomena and in multiple applications including plasmonics, optoelectronics, and sensing. In parallel, the emergence of monochromated electron energy loss spectroscopy (EELS) has enabled exploration of local nanoplasmonic functionalities within single nanoparticles and the collective response of nanoparticle assemblies, providing deep insight into associated mechanisms. However, modern synthesis processes for plasmonic nanostructures are often limited in the types of accessible geometry, and materials and are limited to spatial precisions on the order of tens of nm, precluding the direct exploration of critical aspects of the structure-property relationships. Here, the atomic-sized probe of the scanning transmission electron microscope is used to perform precise sculpting and design nanoparticle configurations. Using low-loss EELS, dynamic analyses of the evolution of the plasmonic response are provided. It is shown that within self-assembled systems of nanoparticles, individual nanoparticles can be selectively removed, reshaped, or patterned with nanometer-level resolution, effectively modifying the plasmonic response in both space and energy. This process significantly increases the scope for design possibilities and presents opportunities for unique structure development, which are ultimately the key for nanophotonic design.
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Affiliation(s)
- Kevin M Roccapriore
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Shin-Hum Cho
- Department of Chemical Engineering, Keimyung University, Dalseo-gu, Daegu, 42601, Republic of Korea
| | - Andrew R Lupini
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Delia J Milliron
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
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11
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Vasudevan RK, Kelley KP, Hinkle J, Funakubo H, Jesse S, Kalinin SV, Ziatdinov M. Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics. ACS NANO 2021; 15:11253-11262. [PMID: 34228427 DOI: 10.1021/acsnano.0c10239] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO3, with results showing that, with just 20% of the area sampled, most high-response clusters were captured. This approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.
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Affiliation(s)
- Rama K Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Kyle P Kelley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Jacob Hinkle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Hiroshi Funakubo
- Department of Material Science and Engineering, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
| | - Stephen Jesse
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Maxim Ziatdinov
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
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