Defects by design: synthesis of palladium nanoparticles with extended twin defects and corrugated surfaces

Abstract

Recent catalytic work has highlighted the importance of grain boundaries in the design of highly active catalyst materials due to the high energy of atoms at strained defect sites. In addition, undercoordinated atoms have long been known to contribute to the catalytic performance of metal nanoparticles. In this work, we describe a method for deliberately increasing the coverage of defect boundaries and undercoordinated atoms at the surfaces of well-defined, symmetric palladium nanoparticles. Careful control of the competitive interactions of chloride and bromide ions with the surface of twinned palladium nanoparticles is used to drive the growth of fin-like structures to extend the area of exposed twin boundaries while also inducing corrugation at the particle surface to add further undercoordinated sites. Mechanistic studies show surface passivation by bromide and etching by chloride in the presence of a low concentration of surfactant to be the key factors that tailor the surface of these nanoparticles, while the internal defect structure is controlled by reaction kinetics. Importantly, these basic principles of competition between surface passivation and etching as well as kinetic control of twin structure are not unique to palladium, and thus this method has the potential to be extended to the enhancement of surface defect density for nanoparticles composed of other catalytically relevant metals.

An Integrated Electrochemistry Approach to the Design and Synthesis of Polyhedral Noble Metal Nanoparticles

“…We used this method to directly replicate on an electrode surface and in the absence of a chemical reducing agenta previously reported colloidal synthesis from our group for corrugated Pd nanoparticles. 39 This electrochemical method was further applied to understand the effects of changing the growth solution conditions in the electrochemical environment. Modification of most growth solution components showed similar effects to what is observed in the colloidal reaction, but the differencessuch as a limited effect of changing surfactant concentration and independence from pH effectsare explored.…”

Potential‐Controlled (R)Evolution: Electrochemical Synthesis of Nanoparticles with Well‐Defined Shapes

“…synthesized shaped palladium nanoparticles via a seeded method by directly applying a current – rather than a voltage – in a chemical growth environment that closely mimicked that of the colloidal nanoparticle syntheses [24] . The authors specifically designed electrochemical synthesis conditions for multiply twinned corrugated palladium nanoparticles in which the composition of the growth solution mirrored a published colloidal synthesis [40] with the exception of the chemical reducing agent. The multiply twinned corrugated particles were produced beginning with palladium seeds synthesized via a standard colloidal synthesis in a cetyltrimethylammonium bromide (CTAB) surfactant using sodium borohydride (NaBH 4 ) as a strong reducing agent [24] .…”

AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles

“…The shape attributes of a metal nanoparticle (mNP) determine many of its physical, chemical, and functional properties, including plasmonic behavior, − catalytic efficiency, − and biological activity. , Significant research effort has focused on developing methods for the shape-controlled synthesis of mNPs, and an accurate and efficient method for their characterization has been indispensable for the success achieved by these studies. − To date, transmission electron microscopy (TEM) remains the most reliable and widely used method for characterizing the morphology of NPs, for which rapid advancement in automated high-throughput electron microscopy has drastically increased both the acquisition rate and the quality of TEM data. − Increased efficiency in TEM data acquisition now enables the scale of NP shape characterization to increase from tens or hundreds of particles to orders of magnitude more, extracting information at the level of more informative statistical distributions . The information from TEM data at such a scale far exceeds the capability of a human analyst, and hence, the development of automated methods for TEM image analysis is imperative.…”