Fermer le menu

Events

 

Seminar PLUM: Monday, 16th June 2025 at 11:00 am

 

Murilo Martinez Moreira (CEMES Toulouse)

 

Title: Quantitative EDS Analysis supported by unsupervised machine learning. Chemical gradients in small nanoalloy and a taste in nanoplasmonics 

 
Institut Néel, Room F418 (Erwin Bertaut)
 
 
Abstract:

Advancements in Energy-dispersive X-ray Spectroscopy (EDS) combined with Scanning Transmission Electron Microscope (STEM) have significantly improved the analysis of chemical composition in nanoscale objects, particularly bimetallic nanoparticles (BNPs) smaller than 10 nm. This breakthrough enables precise quantitative analysis of individual particles. BNPs exhibit diverse physico- chemical properties based on their chemical structure (e.g., random, gradient, or core-shell). Achieving high signal-to-noise ratios (SNR) at such small scales is challenging, requiring robust data acquisition, treatment methods, and rigorous error analysis for a complete comprehension of the BNPs chemical structure.
In this presentation, the study of the chemical composition of AgAu benchmark BNPs will be discussed. The results reveal chemical gradients featuring Ag enrichment at the particle surface, resolving decades of controversy about the miscibility of gold-silver nanoalloys at the ground state. To further validate these findings, unsupervised machine learning methods (Principal Component Analysis-PCA and Non-negative Matrix Factorization-NMF) were applied, providing statistically reliable verification of radial composition changes observed in EDS spectra. This study demonstrates the ability to quantify chemical composition within 3-10 nm BNPs, offering opportunities for further research on segregation effects and chemical reactivity. The particles were coated with a thin carbon film (~20 nm), annealed, and analyzed after structural relaxation to the ground state. Notably, these findings show that the two elements are well mixed in the BNP volume, with Ag segregation mainly occurring in the last atomic layers (~1 nm) of the BNP surface. Finally, using similar methodologies of unsupervised machine learning, as was done with EDS, spectral and spatial correlations can also be found in low-loss Electron Energy Loss Spectroscopy (EELS) experiments. These preliminary results may help improve the understanding of the various plasmon modes present in small metallic nanoparticles.