Automated quantification of one-dimensional nanostructure alignment on surfaces

Dong, Jianjin; Goldthorpe, Irene A.; Abukhdeir, Nasser Mohieddin

doi:10.1088/0957-4484/27/23/235701

Cited by 9 publications

(4 citation statements)

References 40 publications

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“…Due to the large number of nanowires in individual images, this method is time consuming and tedious. Thus, alternative image processing methods have been developed 21 , 22 . For example, the Fast Fourier Transform method has been used to identify the periodic behaviour in an image 20 , 23 .…”

Section: Resultsmentioning

confidence: 99%

Neural Network for Nanoscience Scanning Electron Microscope Image Recognition

Modarres

Aversa

Cozzini

et al. 2017

Sci Rep

114

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In this paper we applied transfer learning techniques for image recognition, automatic categorization, and labeling of nanoscience images obtained by scanning electron microscope (SEM). Roughly 20,000 SEM images were manually classified into 10 categories to form a labeled training set, which can be used as a reference set for future applications of deep learning enhanced algorithms in the nanoscience domain. The categories chosen spanned the range of 0-Dimensional (0D) objects such as particles, 1D nanowires and fibres, 2D films and coated surfaces, and 3D patterned surfaces such as pillars. The training set was used to retrain on the SEM dataset and to compare many convolutional neural network models (Inception-v3, Inception-v4, ResNet). We obtained compatible results by performing a feature extraction of the different models on the same dataset. We performed additional analysis of the classifier on a second test set to further investigate the results both on particular cases and from a statistical point of view. Our algorithm was able to successfully classify around 90% of a test dataset consisting of SEM images, while reduced accuracy was found in the case of images at the boundary between two categories or containing elements of multiple categories. In these cases, the image classification did not identify a predominant category with a high score. We used the statistical outcomes from testing to deploy a semi-automatic workflow able to classify and label images generated by the SEM. Finally, a separate training was performed to determine the volume fraction of coherently aligned nanowires in SEM images. The results were compared with what was obtained using the Local Gradient Orientation method. This example demonstrates the versatility and the potential of transfer learning to address specific tasks of interest in nanoscience applications.

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Section: Resultsmentioning

confidence: 99%

Neural Network for Nanoscience Scanning Electron Microscope Image Recognition

Modarres

Aversa

Cozzini

et al. 2017

Sci Rep

114

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“…Image processing of SEM images was used to assess the NW alignment through the Open Computer Vision Library (OpenCV), implemented by the Python programming language, which has been elaborated upon in our past work [33]. An orientational order parameter S is used to quantitatively assess the quality of alignment [40].…”

Section: Quantification Of Nanowire Alignmentmentioning

confidence: 99%

Exploiting both optical and electrical anisotropy in nanowire electrodes for higher transparency

Dong

Goldthorpe

2017

Nanotechnology

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Transparent electrodes such as indium tin oxide and random meshes of silver nanowires (AgNWs) have isotropic in-plane properties. However, we show that imparting some alignment to AgNWs can create anisotropic transparency and electrical conductivity characteristics that may benefit many applications. For example, liquid crystal displays and the touch sensors on top of them often only need to be transparent to one type of polarized light as well as predominantly conductive in only one direction. Herein, AgNWs are slightly preferentially aligned during their deposition by rod coating. Compared to randomly oriented AgNW films, the alignment boosts the transparency to perpendicularly polarized light, as well as achieves a higher transparency for a given sheet resistance in one direction compared to randomly oriented AgNWs films. These factors together increase the transparency of a 16 Ω/sq electrode by 7.3 percentage points. The alignment technique is cheap and scalable, compatible with roll-to-roll processes, and most importantly does not require extra processing steps, as rod coating is already a standard process for AgNW electrode fabrication.

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“…This is consistent with order parameter representations of local alignment in liquid crystalline phases [22], where order parameters are composed of two components, (order) magnitude and phase. Higher-order contributions to local order have also been shown to be important for quantification of alignment of nanostructures, such as nanorods and nanowires [23]. Topological defects, such as dislocations and disclinations, correspond to regions with non-zero order magnitude and degenerate phase [4].…”

Section: Higher-order Shapelets For Pattern Analysismentioning

confidence: 99%

Shapelet-based orientation and defect identification method for nanostructured surface imaging

Tino,

Suderman,

Abukhdeir

2024

Nanotechnology

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Structure-property relations are of fundamental importance for continued progress in materials research. Determining these relationships for nanomaterials introduces additional challenges, especially when nanostructure is present, either through self-assembly or nano-lithographic processes. Recent advances have been made for quantification of nanostructured surfaces, for which many robust experimental imaging methods exist. One promising approach is based on the use of shapelet functions for image analysis, which may be used as a reduced basis for surface pattern structure resulting from a broad range of phenomena (e.g. self-assembly). These shapelet-based methods enable automated quantification of nanostructured images, guided by the user/researcher, providing pixel-level information of local order without requiring detailed knowledge of order symmetries. In this work, enhancements to the existing shapelet-based response distance method are developed which enable further analysis of local order, including quantification of local orientation and identification of topological defects. The presented shapelet-based methods are applied to a representative set of images of self-assembled surfaces from experimental characterization techniques including scanning electron microscopy, atomic force microscopy and transmission electron microscopy. These methods are shown to be complementary in implementation and, importantly, provide researchers with a robust and generalized computational approach to comprehensively quantify nanostructure order, including local orientation and boundaries within well-aligned grains.

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Automated quantification of one-dimensional nanostructure alignment on surfaces

Cited by 9 publications

References 40 publications

Neural Network for Nanoscience Scanning Electron Microscope Image Recognition

Neural Network for Nanoscience Scanning Electron Microscope Image Recognition

Exploiting both optical and electrical anisotropy in nanowire electrodes for higher transparency

Shapelet-based orientation and defect identification method for nanostructured surface imaging

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