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.
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.
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.
