Authors: Cheng Zhao | Alireza Sadri | Bryan D. Esser | Zhen‐Shen Liew | Alison M. Funston | Scott D. Findlay | Joanne Etheridge
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Counting Atoms in Gold Nanoparticles Using Low Dose Four-Dimensional Scanning Electron Microscopy with Supervised Machine Learning <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d3393e119">Due to their unique nanoscale properties, metal nanoparticles are of great interest for applications such as drug delivery, biological imaging, and chemical sensing [ <a class="xref-link" href="#r1">1</a>]. Although wet chemical synthesis is a common technique for synthesising these nanoparticles, the precise mechanisms responsible for symmetry breaking and subsequent growth into a specific shape still remains under debate, despite extensive progress in synthetic methods over the years. It is well established that varying surfactants can have a significant impact on the morphology of these particles. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d3393e124">In this work, we aim to study the influence of surfactants on the surface energies of nanoparticle facets. Our approach uses quantitative scanning transmission electron microscopy (STEM) to obtain a measure of surface mobility, as a proxy to estimate surface stability and hence energy [ <a class="xref-link" href="#r2">2</a>], by calculating the difference in number of atoms between two consecutive scans with known electron doses. This requires an accurate measurement of atom locations and numbers under the lowest possible dose, in order to minimize the impact of the electron beam (see <a class="xref-link" href="#fg001">Fig.1</a>). Such low dose datasets are comprised of noisy and sparse diffraction patterns which makes thickness estimation of the sample very challenging. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d3393e132">To improve robustness against noise, we employed a modified Capsule Neural Network (CapsNet) architecture and trained the network with diffraction patterns within Au unit cells of various thicknesses and noise realizations. The training considers invariance to prior knowledge of location of unit cells by using histograms of binned data as the input. We then applied the trained network to estimate the number of atoms in different datasets to investigate the atom mobility on a gold nanoparticle, with the use of double aberration-corrected microscopes (FEI Titan3 80-300 FEG TEM and a recently installed Thermo Fisher Scientific Spectra φ FEG TEM). <div xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="fig panel" id="fg001"> <a class="named-anchor" id="fg001"> <!-- named anchor --> </a> <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/f51a694d-b6d6-47f2-a73a-a83fdf49a7c2/ScienceOpen/image/142_6736daf52dd47-ChengZhaoAPMC2025_fig1.jpg"/> </div> <div class="panel-content"> <div class="label">Fig. 1</div> <div class="caption" id="d3393e137"> <p class="first" dir="auto" id="d3393e138">Schematics for the determination of facet mobility. a) and b) two consecutive 4D-STEM scans with known electron doses, and c) difference in number of atoms in each atom column. The mobility of the atoms on this facet can be measured as the energy of one scan under which one atom movement can be detected. </div> </div> </div> Colloidal gold ; Scanning electron microscope ; Nanoparticle ; Materials science ; Electron microscope ; Microscopy ; Artificial intelligence ; Nanotechnology ; Gold standard (test) ; Computer science ; Pattern recognition (psychology) ; Machine learning ; Optics ; Physics ; Mathematics ; Statistics ; Composite material Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques