ArticleScientific reports2022
Deep learning detection of nanoparticles and multiple object tracking of their dynamic evolution during in situ ETEM studies.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
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- Perspective: An outlook on fluorescence tracking.The Journal of chemical physics · 2025Article
- Physically grounded deep learning-enabled gold nanoparticle localization and quantification in photonic resonator absorption microscopy for digital resolution molecular diagnostics.Biosensors & bioelectronics · 2025Article
- Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model.Scientific reports · 2025Article
- The segmentation of nanoparticles with a novel approach of HRUScientific reports · 2025Article
- In Situ Transmission Electron Microscopy of Electrocatalyst Materials: Proposed Workflows, Technical Advances, Challenges, and Lessons Learned.Small methods · 2025Article
- Article
- Applying a Deep-Learning-Based Keypoint Detection in Analyzing Surface Nanostructures.Molecules (Basel, Switzerland) · 2023Article
- A Review of Advanced Multifunctional Magnetic Nanostructures for Cancer Diagnosis and Therapy Integrated into an Artificial Intelligence Approach.Pharmaceutics · 2023Review
- Frustrations of supported catalytic clusters under operando conditions predicted by a simple lattice model.Scientific reports · 2022Article
- DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time.Scientific reports · 2022Article
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Authors and funding
4 authors.
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Abstract
In situ transmission electron microscopy (TEM) studies of dynamic events produce large quantities of data especially under the form of images. In the important case of heterogeneous catalysis, environmental TEM (ETEM) under gas and temperature allows to follow a large population of supported nanoparticles (NPs) evolving under reactive conditions. Interpreting properly large image sequences gives precious information on the catalytic properties of the active phase by identifying causes for its deactivation. To perform a quantitative, objective and robust treatment, we propose an automatic procedure to track nanoparticles observed in Scanning ETEM (STEM in ETEM). Our approach involves deep learning and computer vision developments in multiple object tracking. At first, a registration step corrects the image displacements and misalignment inherent to the in situ acquisition. Then, a deep learning approach detects the nanoparticles on all frames of video sequences. Finally, an iterative tracking algorithm reconstructs their trajectories. This treatment allows to deduce quantitative and statistical features about their evolution or motion, such as a Brownian behavior and merging or crossing events. We treat the case of in situ calcination of palladium (oxide) / delta-alumina, where the present approach allows a discussion of operating processes such as Ostwald ripening or NP aggregative coalescence.
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