Alignment of Au nanorods along <i>de novo</i> designed protein nanofibers studied with automated image analysis

Yaman, Muammer Yusuf; Guye, Kathryn N.; Ziatdinov, Maxim; Shen, Hongfang; Baker, David; Kalinin, Sergei V.; Ginger, David S.

doi:10.1039/d1sm00645b

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…Once in a regime of energetically favorable nanoparticle adsorption, only then can other solution conditions, such as pH or ionic strength, be investigated for tuning nanoparticle density. The shape of the nanoparticle may also be taken into consideration, such as the recent work presented by Yaman et al 59 demonstrating automated image analysis of gold nanorod attachment to similar protein nanofibers. Here, despite the length of nanorods being significantly larger than the diameter of the protein, the width of the nanorod was similar to the diameter of the protein, thus resulting in well-aligned, parallel nanorod attachment to the protein.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Importance of Substrate–Particle Repulsion for Protein-Templated Assembly of Metal Nanoparticles

et al. 2021

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We study the protein-directed assembly of colloidal gold nanoparticles on de novo designed protein nanofiber templates. Using sequential assembly on glass substrates, we attach positively charged gold nanoparticles to protein nanofibers engineered to have a high density of negatively charged surface residues. Using a combination of electron and optical microscopy, we measure the density of particle attachment and characterize binding specificity. By varying nanoparticle size and pH of the solution, we explore the importance of charge-dependent particle−fiber and particle−substrate interactions. We find an inverse correlation between particle size and attachment density to protein nanofibers, attributed to the balance between size-dependent electrostatic particle−fiber attraction and particle−substrate repulsion. We show pH-dependent particle attachment density and binding specificity in relation to the protonation fraction of each assembly layer. Finally, we employ hyperspectral scattering microscopy to draw conclusions about particle density and interparticle spacings of optically observable particle assemblies.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Importance of Substrate–Particle Repulsion for Protein-Templated Assembly of Metal Nanoparticles

et al. 2021

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“…609,610 For example, FCN has been used to segment the low-contrast protein nanofibers from Au NR-protein nanofiber assemblies in SEM images. 610 A few initial images were manually labeled and augmented as the training dataset. Combined with the conventional thresholding-based image segmentation of high-contrast Au NRs, the identification of Au NR and protein fiber orientations showed a preference of Au NRs to parallelly align to the nanofibers, the extent of which decreased with increasing salt concentration and was weakly sensitive to the rod aspect ratio.…”

Section: Overview Of ML Methods Used In Em Datamentioning

confidence: 99%

“…CNN also facilitates the analysis of assembled nanostructures. , For example, FCN has been used to segment the low-contrast protein nanofibers from Au NR-protein nanofiber assemblies in SEM images . A few initial images were manually labeled and augmented as the training dataset.…”

Section: Application Of ML In Em Data Analysismentioning

confidence: 99%

Electron Microscopy Studies of Soft Nanomaterials

et al. 2023

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This review highlights recent efforts on applying electron microscopy (EM) to soft (including biological) nanomaterials. We will show how developments of both the hardware and software of EM have enabled new insights into the formation, assembly, and functioning (e.g., energy conversion and storage, phonon/photon modulation) of these materials by providing shape, size, phase, structural, and chemical information at the nanometer or higher spatial resolution. Specifically, we first discuss standard real-space two-dimensional imaging and analytical techniques which are offered conveniently by microscopes without special holders or advanced beam technology. The discussion is then extended to recent advancements, including visualizing three-dimensional morphology of soft nanomaterials using electron tomography and its variations, identifying local structure and strain by electron diffraction, and recording motions and transformation by in situ EM. On these advancements, we cover state-of-the-art technologies designed for overcoming the technical barriers for EM to characterize soft materials as well as representative application examples. The even more recent integration of machine learning and its impacts on EM are also discussed in detail. With our perspectives of future opportunities offered at the end, we expect this review to inspire and stimulate more efforts in developing and utilizing EM-based characterization methods for soft nanomaterials at the atomic to nanometer length scales in academic research and industrial applications.

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“…For example, computer vision techniques and DNN are efficient tools for image analysis including transmission electron microscopy (TEM), scanning electron microscopy (SEM), atomic force microscopy, as well as spectral two-dimensional (2D) maps. [ 45 – 47 ] Moreover, for data mining of important aspects of NMs, such as their experimental design and properties, the texts of scientific articles can be analyzed using NN models optimized for language and natural language processing. [ 48 , 49 ] Furthermore, spectral outputs such as absorption/emission intensity dependence on wavelength, including those with circular dichroism (CD), can be analyzed by neural models for time series forecasting and digital signal processing techniques.…”

Section: What Is Machine Learning?mentioning

confidence: 99%

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Kuznetsova,

Coogan,

Botov

et al. 2024

Advanced Materials

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Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating the design of novel materials, and reducing the need for time‐consuming and labour‐intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials and nanostructures is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarised luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, we provide an analysis of machine learning methods used in studying achiral nanomaterials, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. We present an overview of chiral nanomaterials within the framework of synthesis‐structure‐property‐application relationships and provide insights on how to leverage machine learning for the study of these highly complex relationships. We also review and discuss some key recent publications on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.This article is protected by copyright. All rights reserved

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Alignment of Au nanorods along de novo designed protein nanofibers studied with automated image analysis

Abstract: In this study, we focus on exploring the directional assembly of anisotropic Au nanorods along de novo designed 1D protein nanofiber templates using automated image analysis tool.

Cited by 7 publications

References 34 publications

Importance of Substrate–Particle Repulsion for Protein-Templated Assembly of Metal Nanoparticles

Importance of Substrate–Particle Repulsion for Protein-Templated Assembly of Metal Nanoparticles

Electron Microscopy Studies of Soft Nanomaterials

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

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