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Nielsen MR, March S, Sainju R, Zhu C, Gao PX, Suib SL, Zhu Y. In-situ ETEM Observation of Competing Mechanisms for Filamentous Carbon Gasification. Microsc Microanal 2023; 29:1296-1297. [PMID: 37613727 DOI: 10.1093/micmic/ozad067.663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Affiliation(s)
- Monia R Nielsen
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA
| | - Seth March
- Institute of Materials Science, University of Connecticut, Storrs, CT, USA
- Department of Chemistry, University of Connecticut, Storrs, CT, USA
| | - Rajat Sainju
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA
| | - Chunxiang Zhu
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA
- Institute of Materials Science, University of Connecticut, Storrs, CT, USA
| | - Pu-Xian Gao
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA
- Institute of Materials Science, University of Connecticut, Storrs, CT, USA
| | - Steven L Suib
- Institute of Materials Science, University of Connecticut, Storrs, CT, USA
- Department of Chemistry, University of Connecticut, Storrs, CT, USA
| | - Yuanyuan Zhu
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA
- Institute of Materials Science, University of Connecticut, Storrs, CT, USA
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Sainju R, Roberts G, Chen WY, Hutchinson B, Yang Q, Ding C, Edwards DJ, Li M, Zhu Y. Deep Learning for Automated Quantification of Irradiation Defects in TEM Data: Relating Pixel-level Errors to Defect Properties. Microsc Microanal 2023; 29:1559-1560. [PMID: 37613789 DOI: 10.1093/micmic/ozad067.802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Affiliation(s)
- Rajat Sainju
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA
| | - Graham Roberts
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA
| | - Wei-Ying Chen
- Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL, USA
| | - Brian Hutchinson
- Computer Science Department, Western Washington University, Bellingham, WA, USA
- National Security Directorate, AI and Data Analytics Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Qian Yang
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA
| | - Caiwen Ding
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA
| | - Danny J Edwards
- Energy and Environment Directorate, Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Meimei Li
- Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL, USA
| | - Yuanyuan Zhu
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA
- Energy and Environment Directorate, Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
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Sainju R, Togaru M, Zhang L, Jiang W, Setyawan W, Atwani OE, Zhu Y. In-situ Thermal Oxidation of Fusion PFM Tungsten Using Atmospheric Environmental TEM. Microsc Microanal 2023; 29:1462-1463. [PMID: 37613640 DOI: 10.1093/micmic/ozad067.751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Affiliation(s)
- Rajat Sainju
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA
| | - Maanas Togaru
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA
| | - Lichun Zhang
- Institute of Materials Science, University of Connecticut, Storrs, CT, USA
| | - Weilin Jiang
- Reactor Materials and Mechanical Design Group, Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Wahyu Setyawan
- Reactor Materials and Mechanical Design Group, Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Osman El Atwani
- Materials Science and Technology, Los Alamos National Lab, Los Alamos, NM, USA
| | - Yuanyuan Zhu
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA
- Institute of Materials Science, University of Connecticut, Storrs, CT, USA
- Reactor Materials and Mechanical Design Group, Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
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Sainju R, Chen WY, Schaefer S, Yang Q, Ding C, Li M, Zhu Y. DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time. Sci Rep 2022; 12:15705. [PMID: 36127375 PMCID: PMC9489724 DOI: 10.1038/s41598-022-19697-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 09/02/2022] [Indexed: 11/10/2022] Open
Abstract
In-situ irradiation transmission electron microscopy (TEM) offers unique insights into the millisecond-timescale post-cascade process, such as the lifetime and thermal stability of defect clusters, vital to the mechanistic understanding of irradiation damage in nuclear materials. Converting in-situ irradiation TEM video data into meaningful information on defect cluster dynamic properties (e.g., lifetime) has become the major technical bottleneck. Here, we present a solution called the DefectTrack, the first dedicated deep learning-based one-shot multi-object tracking (MOT) model capable of tracking cascade-induced defect clusters in in-situ TEM videos in real-time. DefectTrack has achieved a Multi-Object Tracking Accuracy (MOTA) of 66.43% and a Mostly Tracked (MT) of 67.81% on the test set, which are comparable to state-of-the-art MOT algorithms. We discuss the MOT framework, model selection, training, and evaluation strategies for in-situ TEM applications. Further, we compare the DefectTrack with four human experts in quantifying defect cluster lifetime distributions using statistical tests and discuss the relationship between the material science domain metrics and MOT metrics. Our statistical evaluations on the defect lifetime distribution suggest that the DefectTrack outperforms human experts in accuracy and speed.
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Affiliation(s)
- Rajat Sainju
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, 06269, USA
| | - Wei-Ying Chen
- Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Samuel Schaefer
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, 06269, USA
| | - Qian Yang
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, 06269, USA
| | - Caiwen Ding
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, 06269, USA
| | - Meimei Li
- Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Yuanyuan Zhu
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, 06269, USA.
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Sainju R, Rathnayake D, Tan H, Bollas G, Dongare AM, Suib SL, Zhu Y. In Situ Studies of Single-Nanoparticle-Level Nickel Thermal Oxidation: From Early Oxide Nucleation to Diffusion-Balanced Oxide Thickening. ACS Nano 2022; 16:6468-6479. [PMID: 35413193 DOI: 10.1021/acsnano.2c00742] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
High-temperature oxidation mechanisms of metallic nanoparticles have been extensively investigated; however, it is challenging to determine whether the kinetic modeling is applicable at the nanoscale and how the differences in nanoparticle size influence the oxidation mechanisms. In this work, we study thermal oxidation of pristine Ni nanoparticles ranging from 4 to 50 nm in 1 bar 1%O2/N2 at 600 °C using in situ gas-cell environmental transmission electron microscopy. Real-space in situ oxidation videos revealed an unexpected nanoparticle surface refacetting before oxidation and a strong Ni nanoparticle size dependence, leading to distinct structural development during the oxidation and different final NiO morphology. By quantifying the NiO thickness/volume change in real space, individual nanoparticle-level oxidation kinetics was established and directly correlated with nanoparticle microstructural evolution with specified fast and slow oxidation directions. Thus, for the size-dependent Ni nanoparticle oxidation, we propose a unified oxidation theory with a two-stage oxidation process: stage 1: dominated by the early NiO nucleation (Avrami-Erofeev model) and stage 2: the Wagner diffusion-balanced NiO shell thickening (Wanger model). In particular, to what extent the oxidation would proceed into stage 2 dictates the final NiO morphology, which depends on the Ni starting radius with respect to the critical thickness under given oxidation conditions. The overall oxidation duration is controlled by both the diffusivity of Ni2+ in NiO and the Ni in Ni self-diffusion. We also compare the single-particle kinetic curve with the collective one and discuss the effects of nanoparticle size differences on kinetic model analysis.
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Roberts G, Haile SY, Sainju R, Edwards DJ, Hutchinson B, Zhu Y. Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels. Sci Rep 2019; 9:12744. [PMID: 31484940 PMCID: PMC6726638 DOI: 10.1038/s41598-019-49105-0] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 08/14/2019] [Indexed: 11/22/2022] Open
Abstract
Crystalline materials exhibit long-range ordered lattice unit, within which resides nonperiodic structural features called defects. These crystallographic defects play a vital role in determining the physical and mechanical properties of a wide range of material systems. While computer vision has demonstrated success in recognizing feature patterns in images with well-defined contrast, automated identification of nanometer scale crystallographic defects in electron micrographs governed by complex contrast mechanisms is still a challenging task. Here, building upon an advanced defect imaging mode that offers high feature clarity, we introduce DefectSegNet - a new convolutional neural network (CNN) architecture that performs semantic segmentation of three common crystallographic defects in structural alloys: dislocation lines, precipitates and voids. Results from supervised training on a small set of high-quality defect images of steels show high pixel-wise accuracy across all three types of defects: 91.60 ± 1.77% on dislocations, 93.39 ± 1.00% on precipitates, and 98.85 ± 0.56% on voids. We discuss the sources of uncertainties in CNN prediction and the training data in terms of feature density, representation and homogeneity and their effects on deep learning performance. Further defect quantification using DefectSegNet prediction outperforms human expert average, presenting a promising new workflow for fast and statistically meaningful quantification of materials defects.
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Affiliation(s)
- Graham Roberts
- Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Simon Y Haile
- Computer Science Department, Western Washington University, Bellingham, WA, 98225, USA
| | - Rajat Sainju
- Department of Materials Science and Engineering, Institute of Materials Science, University of Connecticut, Storrs, CT, 06269, USA
| | - Danny J Edwards
- Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Brian Hutchinson
- Computer Science Department, Western Washington University, Bellingham, WA, 98225, USA
- Computing and Analytics Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Yuanyuan Zhu
- Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
- Department of Materials Science and Engineering, Institute of Materials Science, University of Connecticut, Storrs, CT, 06269, USA.
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Adhikari RC, Sainju R, Sayami G, Dali S, Shrestha HG, Basnet RB, Pandey JS. INTRAOCULAR MALIGNANT TERATOID MEDULLOEPITHELIOMA. JNMA J Nepal Med Assoc 2003. [DOI: 10.31729/jnma.762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
Abstract
Malignant teratoid medulloepithelioma is an uncommon unilateral intraocular tumor,occuring typically in children. This report concerns a 4-year-old boy, who presentedwith loss of vision, pain and proptosis of the left eye and showed mass in betweeneyelids. Histopathologically, the tumor was composed of pseudostratified primitive-appearing epithelium dispersed in cords, strands, tubules & glands, which wereseparated by a cystic spaces, filled with pale eosinophilic material. Foci of glial tissue,cartilage , bone, skeletal muscle and fatty tissue were recognized. In addition, scleralextension of tumor, Homer-Wright like and Flexner-Wintersteiner like rosettes andfoci of necrosis were also present. The differentiation from retinoblastoma wasdiscussed.Key Words: Malignant medulloepithelioma, eyeball, heteroplasia.
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