PyNanospacing: TEM image processing tool for strain analysis and
visualization
- URL: http://arxiv.org/abs/2311.15751v1
- Date: Mon, 27 Nov 2023 12:08:46 GMT
- Title: PyNanospacing: TEM image processing tool for strain analysis and
visualization
- Authors: Mehmet Ali Sarsil, Mubashir Mansoor, Mert Saracoglu, Servet Timur,
Mustafa Urgen, Onur Ergen
- Abstract summary: This paper develops a Python code for TEM image processing that can handle a wide range of materials.
It converts local differences in interplanar spacings into contour maps allowing for a visual representation of lattice expansion and compression.
The tool is very generic and can significantly aid in analyzing material properties using TEM images.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The diverse spectrum of material characteristics including band gap,
mechanical moduli, color, phonon and electronic density of states, along with
catalytic and surface properties are intricately intertwined with the atomic
structure and the corresponding interatomic bond-lengths. This interconnection
extends to the manifestation of interplanar spacings within a crystalline
lattice. Analysis of these interplanar spacings and the comprehension of any
deviations, whether it be lattice compression or expansion, commonly referred
to as strain, hold paramount significance in unraveling various unknowns within
the field. Transmission Electron Microscopy (TEM) is widely used to capture
atomic-scale ordering, facilitating direct investigation of interplanar
spacings. However, creating critical contour maps for visualizing and
interpreting lattice stresses in TEM images remains a challenging task. Here we
developed a Python code for TEM image processing that can handle a wide range
of materials including nanoparticles, 2D materials, pure crystals and solid
solutions. This algorithm converts local differences in interplanar spacings
into contour maps allowing for a visual representation of lattice expansion and
compression. The tool is very generic and can significantly aid in analyzing
material properties using TEM images, allowing for a more in-depth exploration
of the underlying science behind strain engineering via strain contour maps at
the atomic level.
Related papers
- Invariant Discovery of Features Across Multiple Length Scales: Applications in Microscopy and Autonomous Materials Characterization [3.386918190302773]
Variational Autoencoders (VAEs) have emerged as powerful tools for identifying underlying factors of variation in image data.
We introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales.
arXiv Detail & Related papers (2024-08-01T01:48:46Z) - Hyperspectral Dataset and Deep Learning methods for Waste from Electric and Electronic Equipment Identification (WEEE) [0.0]
We evaluate the performance of diverse deep learning architectures for hyperspectral image segmentation.
Results show that incorporating spatial information alongside spectral data leads to improved segmentation results.
We contribute to the field by cleaning and publicly releasing the Tecnalia WEEE Hyperspectral dataset.
arXiv Detail & Related papers (2024-07-05T13:45:11Z) - Tracing and segmentation of molecular patterns in 3-dimensional cryo-et/em density maps through algorithmic image processing and deep learning-based techniques [0.0]
dissertation focuses on developing sophisticated computational techniques for tracing actin filaments.
Three novel methodologies have been developed: BundleTrac, for tracing bundle-like actin filaments found in Stereocilium, Spaghetti Tracer, for tracing filaments that move individually with loosely cohesive movements, and Struwwel Tracer, for tracing randomly orientated actin filaments in the actin network.
The second component of the dissertation introduces a convolutional neural network (CNN) based segmentation model to determine the location of protein secondary structures, such as helices and beta-sheets, in medium-resolution (5-10 Angstrom) 3-dimensional cryo-electron microscopy
arXiv Detail & Related papers (2024-03-26T00:41:54Z) - Datacube segmentation via Deep Spectral Clustering [76.48544221010424]
Extended Vision techniques often pose a challenge in their interpretation.
The huge dimensionality of data cube spectra poses a complex task in its statistical interpretation.
In this paper, we explore the possibility of applying unsupervised clustering methods in encoded space.
A statistical dimensional reduction is performed by an ad hoc trained (Variational) AutoEncoder, while the clustering process is performed by a (learnable) iterative K-Means clustering algorithm.
arXiv Detail & Related papers (2024-01-31T09:31:28Z) - Instance Segmentation of Dislocations in TEM Images [0.0]
In materials science, the knowledge about the location and movement of dislocations is important for creating novel materials with superior properties.
In this work, we quantitatively compare state-of-the-art instance segmentation methods, including Mask R-CNN and YOLOv8.
The dislocation masks as the results of the instance segmentation are converted to mathematical lines, enabling quantitative analysis of dislocation length and geometry.
arXiv Detail & Related papers (2023-09-07T06:17:31Z) - Multi-Spectral Image Stitching via Spatial Graph Reasoning [52.27796682972484]
We propose a spatial graph reasoning based multi-spectral image stitching method.
We embed multi-scale complementary features from the same view position into a set of nodes.
By introducing long-range coherence along spatial and channel dimensions, the complementarity of pixel relations and channel interdependencies aids in the reconstruction of aligned multi-view features.
arXiv Detail & Related papers (2023-07-31T15:04:52Z) - Object Detection in Hyperspectral Image via Unified Spectral-Spatial
Feature Aggregation [55.9217962930169]
We present S2ADet, an object detector that harnesses the rich spectral and spatial complementary information inherent in hyperspectral images.
S2ADet surpasses existing state-of-the-art methods, achieving robust and reliable results.
arXiv Detail & Related papers (2023-06-14T09:01:50Z) - Disentangling multiple scattering with deep learning: application to
strain mapping from electron diffraction patterns [48.53244254413104]
We implement a deep neural network called FCU-Net to invert highly nonlinear electron diffraction patterns into quantitative structure factor images.
We trained the FCU-Net using over 200,000 unique dynamical diffraction patterns which include many different combinations of crystal structures.
Our simulated diffraction pattern library, implementation of FCU-Net, and trained model weights are freely available in open source repositories.
arXiv Detail & Related papers (2022-02-01T03:53:39Z) - Qubit-photon bound states in topological waveguides with long-range
hoppings [62.997667081978825]
Quantum emitters interacting with photonic band-gap materials lead to the appearance of qubit-photon bound states.
We study the features of the qubit-photon bound states when the emitters couple to the bulk modes in the different phases.
We consider the coupling of emitters to the edge modes appearing in the different topological phases.
arXiv Detail & Related papers (2021-05-26T10:57:21Z) - Robust Feature Disentanglement in Imaging Data via Joint Invariant
Variational Autoencoders: from Cards to Atoms [0.0]
We introduce a joint rotationally (and translationally) invariant variational autoencoder (j-trVAE)
The performance of this method is validated on several synthetic data sets and extended to high-resolution imaging data of electron and scanning probe microscopy.
We show that latent space behaviors directly comport to the known physics of ferroelectric materials and quantum systems.
arXiv Detail & Related papers (2021-04-20T18:01:55Z) - Data-Driven Discovery of Molecular Photoswitches with Multioutput
Gaussian Processes [51.17758371472664]
Photoswitchable molecules display two or more isomeric forms that may be accessed using light.
We present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning.
We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules.
arXiv Detail & Related papers (2020-06-28T20:59:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.