Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning

Yao, Lehan; An, Hyosung; Zhou, Shan; Kim, Ah Young; Luijten, Erik; Chen, Qian

doi:10.1039/d2nr03712b

Cited by 11 publications

(9 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the U-Net with three output channels is capable of segmenting both the high-contrast Au NP core and weak-contrast polymer patches in the patchy Au nanoprism (Figure 44e). 5 The careful design of output channels can also achieve the segmentation of overlaying NPs. As shown in Figure 44f, when assigned to predict the overlaying region and nonoverlaying region respectively, the two output channels in U-Net can be combined to reconstruct the contour of partially overlaying tetrahedral Au NPs in TEM images.…”

Section: Overview Of ML Methods Used In Em Datamentioning

confidence: 99%

Section: Application Of ML In Em Data Analysismentioning

confidence: 99%

“…Some studies utilize more universal descriptors such as the NP contour data with dimension reduction as input to group algorithms, which avoids the manual descriptor selection and can be widely applied to different shapes. For example, the center-to-perimeter distance as a function of angle can be used as a fingerprint describing the nanomorphology and serve as inputs of dimension reduction and clustering algorithms (Figure e,f) . With the ability to prevent clustering discrepancies introduced by the choice of descriptors, this method has been proven to be compatible with nanomaterials with various shapes, in both 2D and 3D.…”

Section: Application Of ML In Em Data Analysismentioning

confidence: 99%

“…Illustration showing the application of EM, with assistance of ML, in the characterization of soft nanomaterials. The EM images and illustrations are adapted with permission from refs − . Copyrights 2020 Springer Nature under CC-BY 4.0 [], 2022 The Royal Society of Chemistry, 2022 AAAS under CC BY-NC 4.0 [], 2017 American Chemical Society, 2019 American Chemical Society, 2019 American Chemical Society.…”

Section: Standard Real-space Two-dimensional (2d) Imaging By Emmentioning

confidence: 99%

“…Moreover, the multiple output channels of CNNs can facilitate the segmentation of different components in the hybrid nanomaterials. For example, the U-Net with three output channels is capable of segmenting both the high-contrast Au NP core and weak-contrast polymer patches in the patchy Au nanoprism (Figure e) . The careful design of output channels can also achieve the segmentation of overlaying NPs.…”

Section: Application Of ML In Em Data Analysismentioning

confidence: 99%

See 4 more Smart Citations

Electron Microscopy Studies of Soft Nanomaterials

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Overview Of ML Methods Used In Em Datamentioning

confidence: 99%

Section: Application Of ML In Em Data Analysismentioning

confidence: 99%

Section: Application Of ML In Em Data Analysismentioning

confidence: 99%

Section: Standard Real-space Two-dimensional (2d) Imaging By Emmentioning

confidence: 99%

Section: Application Of ML In Em Data Analysismentioning

confidence: 99%

See 3 more Smart Citations

Electron Microscopy Studies of Soft Nanomaterials

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

Progress in the Computer‐Aided Analysis in Multiple Aspects of Nanocatalysis Research

Fan,

Shen,

Lou

et al. 2024

Adv Healthcare Materials

View full text Add to dashboard Cite

Making the utmost of the differences and advantages of multiple disciplines, interdisciplinary integration breaks the science boundaries and accelerates the progress in mutual quests. As an organic connection of material science, enzymology, and biomedicine, nanozyme‐related research is further supported by computer technology, which injects in new vitality, and contributes to in‐depth understanding, unprecedented insights, and broadened application possibilities. Utilizing computer‐aided first‐principles method, high‐speed and high‐throughput mathematic, physic, and chemic models are introduced to perform atomic‐level kinetic analysis for nanocatalytic reaction process, and theoretically illustrate the underlying nanozymetic mechanism and structure‐function relationship. On this basis, nanozymes with desirable properties can be designed and demand‐oriented synthesized without repeated trial‐and‐error experiments. Besides that, computational analysis and device also play an indispensable role in nanozyme‐based detecting methods to realize automatic readouts with improved accuracy and reproducibility. Here, this work focuses on the crossing of nanocatalysis research and computational technology, to inspire the research in computer‐aided analysis in nanozyme field to a greater extent.

show abstract

Observing the evolution of 1D nanostructures in liquids: Advances and application

Zhang,

Fu,

Xiao

et al. 2024

Chemical Engineering Journal

View full text Add to dashboard Cite

Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning

Abstract: Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the...

Cited by 11 publications

References 82 publications

Electron Microscopy Studies of Soft Nanomaterials

Electron Microscopy Studies of Soft Nanomaterials

Progress in the Computer‐Aided Analysis in Multiple Aspects of Nanocatalysis Research

Observing the evolution of 1D nanostructures in liquids: Advances and application

Contact Info

Product

Resources

About