The Molecular Footprint of Peptides on the Surface of Ultrasmall Gold Nanoparticles (2 nm) Is Governed by Steric Demand.

Ultrasmall gold nanoparticles were functionalized with peptides of two to seven amino acids that contained one cysteine molecule as anchor via a thiol-gold bond and a number of alanine residues as nonbinding amino acid. The cysteine was located either in the center of the molecule or at the end (C-terminus). For comparison, gold nanoparticles were also functionalized with cysteine alone. The particles were characterized by UV spectroscopy, differential centrifugal sedimentation (DCS), high-resolution transmission electron microscopy (HRTEM), and small-angle X-ray scattering (SAXS). This confirmed the uniform metal core (2 nm diameter). The hydrodynamic diameter was probed by 1H-DOSY NMR spectroscopy and showed an increase in thickness of the hydrated peptide layer with increasing peptide size (up to 1.4 nm for heptapeptides; 0.20 nm per amino acid in the peptide). 1H NMR spectroscopy of water-dispersed nanoparticles showed the integrity of the peptides and the effect of the metal core on the peptide. Notably, the NMR signals were very broad near the metal surface and became increasingly narrow in a distance. In particular, the methyl groups of alanine can be used as probe for the resolution of the NMR spectra. The number of peptide ligands on each nanoparticle was determined using quantitative 1H NMR spectroscopy. It decreased with increasing peptide length from about 100 for a dipeptide to about 12 for a heptapeptide, resulting in an increase of the molecular footprint from about 0.1 to 1.1 nm2.

Automated analysis of transmission electron micrographs of metallic nanoparticles by machine learning.

Metallic nanoparticles were analysed with respect to size and shape by a machine learning approach. This involved a separation of particles from the background (segmentation), a separation of overlapping particles, and the identification of individual particles. An algorithm to separate overlapping particles, based on ultimate erosion of convex shapes (UECS), was implemented. Finally, particle properties like size, circularity, equivalent diameter, and Feret diameter were computed for each particle of the whole particle population. Thus, particle size distributions can be easily created based on the various parameters. However, strongly overlapping particles are difficult and sometimes impossible to separate because of an a priori unknown shape of a particle that is partially lying in the shadow of another particle. The program is able to extract information from a sequence of images of the same sample, thereby increasing the number of analysed nanoparticles to several thousands. The machine learning approach is well-suited to identify particles at only limited particle-to-background contrast as is demonstrated for ultrasmall gold nanoparticles (2 nm).

Water-Based Synthesis of Ultrasmall Nanoparticles of Platinum Group Metal Oxides (1.8 nm).

Ultrasmall nanoparticles of platinum group metal oxides (core diameter of about 1.8 nm) were prepared by alkaline hydrolysis of metal precursors in the presence of NaBH4 and by colloidal stabilization with tripeptide glutathione. We obtained water-dispersed nanoparticles of Rh2O3, PdO, RuO2, IrO2, Os/OsO2, and Pt/PtO. Their size was probed using high-resolution transmission electron microscopy, differential centrifugal sedimentation, small-angle X-ray scattering, and diffusion-ordered 1H NMR spectroscopy (1H DOSY). Their oxidation state was clearly determined using X-ray photoelectron spectroscopy, X-ray powder diffraction, and electron diffraction. The chemical composition of the nanoparticles, that is, the ratio of the metal oxide core and glutathione capping agent, was quantitatively determined by a combination of these methods.