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DIONISOS - Research project

Diffusion Of NanoparticleS On Surfaces

Post-doctoral fellow: Khuram FARAZ

ABSTRACT

DiONisOS deals with the characterization of a population of nanoparticles (NPs) dispersed on a surface, as in the case of heterogeneous catalysis. We use advanced Transmission Electron Microscopes (TEMs) available at Villeurbanne and St-Etienne in the frame of the CLYM Federation to track in 2D and 3D the evolution of metallic NPs diffusing on a crystallographic surface under the action of the temperature and gas, i.e. their mobility and coalescence of growth. Dedicated image processing approaches based on machine learning are implemented to identify these objects during time lapse sequences and measure their trajectories with the highest accuracy and the largest statistical relevance. Perspectives are to provide quantitative information to serve as an input to a thermodynamical modelling of surface diffusion processes, including the influence of the topography of the support.
 
Dionisos
  • Dionisos_GraphicalAbstract
    Dionisos_GraphicalAbstract
  • illustration2_DIONISOS_synoptic of the machine Learning procedure
    illustration2_DIONISOS_synoptic of the machine Learning procedure
  • illustration4_DIONISOS_topographic imaging
    DIONISOS project - Topographic imaging © Hubert Curien Laboratory
    illustration4_DIONISOS_topographic imaging
  • Portrait_Khuram FARAZ
    Portrait_Khuram FARAZ
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©MATEIS
 
RESULTS


Experimental sequence Annular Dark Field Scanning TEM (ADF-STEM) images acquired in situ during 140 minutes in an Environmental TEM (FEI Titan ETEM, CLYM – Lyon): calcination of PdOx NPs on -Al2O3 (rapid heating to 250°C, 2.2 mbar O2. Sample provided by IFPEN). Left: raw unregistered sequence. Centre: segmentation of NPs with UNET deep neural network; colour coding: green = ground truth (GT) correctly detected in blue, yellow = undetected false negative GT, red = false positive NPs. Right: trajectories identified by a home-made tracking algorithm.



Tracking of particles of a simulated TEM sequence used for the training step of UNET neural network. From left to right: raw unregistered sequence; UNET detection of NPs; trajectories identified by a home-made tracking algorithm.

 
PUBLICATIONS
  • Faraz, K., Grenier, T., Ducottet, C. et al. Deep learning detection of nanoparticles and multiple object tracking of their dynamic evolution during in situ ETEM studies. Sci Rep 12, 2484 (2022).
    https://doi.org/10.1038/s41598-022-06308-2

CONFERENCES
  • K. Faraz, T. Grenier, C. Ducottet, T. Epicier. Intelligent tracking of catalytic nanoparticles trajectories during in situ ETEM experiments. Webinar invité, DENSsolutions, 22/06/2022.
  • K. Faraz, T. Grenier, C. Ducottet, T. Epicier. Intelligent tracking of catalytic nanoparticles trajectories during in situ ETEM experiments. Conférence invitée à MRS Spring Meeting, 8-13 May 2022, Honolulu, United States.
  • K. Faraz, T. Grenier, C. Ducottet, T. Epicier. A machine Learning pipeline to track dynamics of a population of nanoparticles during in situ Environmental Transmission Electron Microscopy in gases. M&M2021, Microscopy Society of America (MSA), Aug 2021, Pittsburgh, United States
  • T Epicier, K Faraz, T Grenier, C Ducottet. Multiple Object Tracking of Supported Nanoparticles during in situ Environmental TEM Studies of Nanocatalysts. Microscience Microscopy Congress (mmc) 2021, Royal Microscopy Society (RMS, UK), Jul 2021, Manchester, United Kingdom. ⟨hal-03271194⟩2 (mmc2021, M&M2021)
  • T. Epicier, Nanomaterials ‘Alive’ under Gas in the Environmental Transmission Electron Microscope (ETEM), conférence invitée à ICMAT 2019 https://icmat2019.mrs.org.sg/symp-list/symp-a/#1517143199497-8f3a7c50-d8b6, 23 - 28 June 2019, Marina Bay Sands, Singapore.
  • T. Epicier et al., Quantitative tracking of catalytic metallic nanoparticles on their support during in situ gaseous ETEM. Présentation orale, E-MRS 2019 Spring Meeting, Nice, F, 27-31/05/2019.