Multimodal imaging of cubic Cu2O@Au nanocage formation via galvanic replacement using X-ray ptychography and nano diffraction

Abstract

Being able to observe the formation of multi-material nanostructures in situ, simultaneously from a morphological and crystallographic perspective, is a challenging task. Yet, this is essential for the fabrication of nanomaterials with well-controlled composition exposing the most active crystallographic surfaces, as required for highly active catalysts in energy applications. To demonstrate how X-ray ptychography can be combined with scanning nanoprobe diffraction to realize multimodal imaging, we study growing Cu2O nanocubes and their transformation into Au nanocages. During the growth of nanocubes at a temperature of 138 °C, we measure the crystal structure of an individual nanoparticle and determine the presence of (100) crystallographic facets at its surface. We subsequently visualize the transformation of Cu2O into Au nanocages by galvanic replacement. The nanocubes interior homogeneously dissolves while smaller Au particles grow on their surface and later coalesce to form porous nanocages. We finally determine the amount of radiation damage making use of the quantitative phase images. We find that both the total surface dose as well as the dose rate imparted by the X-ray beam trigger additional deposition of Au onto the nanocages. Our multimodal approach can benefit in-solution imaging of multi-material nanostructures in many related fields.

A review of recent advances in the use of complex metal nanostructures for biomedical applications from diagnosis to treatment

“…The chemical and physical properties of nanocages can be tailored by adjusting pore size, shell thickness, and overall shape (Skrabalak et al, 2007). One commonly employed technique for nanocage synthesis involves galvanic replacement, which relies on oxidation and dissolution of atoms from one material and a reduction and deposition of a second material from a salt precursor onto the first (Grote et al, 2023; Guo et al, 2022; Song et al, 2022). Here, the template material is often nanosilver or another metal with a low reduction potential.…”

An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model

“… 6 , 7 , 8 Benefiting from the high brightness and excellent coherence nature of next-generation synchrotron sources, ptychography is reaching new levels of application scenarios. For example, the emergence of new ptychography-based imaging technologies, including resonant ptychography, 9 , 10 ptycho-tomography, 11 , 12 , 13 and in situ ptychography, 14 , 15 allows for multi-dimensional analysis, fine structure study and functional characterization of large-volume samples with improved temporal resolution. Despite the potential of these new ptychography methods, significant challenges remain in the algorithmic and software domain to address their online data analysis requirements.…”