Photonics.com

Using scanning tunneling microscopy (STM), researchers demonstrated extremely high resolution imaging of the molecule-covered surface structures of silver nanoparticles, even down to the recognition of individual parts of the molecules protecting the surface. Left: High-resolution STM image of a silver nanoparticle of 374 silver atoms covered by 113 TBBT molecules. Right: a simulated STM image from one orientation of the particle. Center: the atomic structure of the particle. Courtesy of Hannu Häkkinen. Researchers used previously characterized silver nanoparticles, with a known atomic structure. The metal core of the particles had 374 silver atoms and the surface was protected by a set of 113 TBTT molecules. TBBT (tert-butyl-benzene thiol) is a molecule with three separate carbon groups on its end. The particle’s outer surface has a total of 339 such groups. When this type of nanoparticle sample was imaged at low temperatures in the experiment using STM, clear sequential modulations were observed in the tunneling current that was formed by the image. Similar modulations were noted when individual TBBT molecules were imaged on a flat surface. Based on density functional theory (DFT), the simulations performed by the research team at the University of Jyväskylä showed that each of the three carbon groups of the TBBT molecule provided its own current maximum in the STM image, and that the distances between the maxima corresponded to the STM measurement results. This confirmed that measurement was successful at sub-molecular level. “This is the first time that STM imaging of nanoparticle surface structures has been able to ‘see’ the individual parts of molecules. Our computational work was important to verifying the experimental results,” said professor Hannu Häkkinen.  “However, we wanted to go one step further. As the atomic structure of particles is well known, we had grounds for asking whether the precise orientation of the imaged particle could be identified using simulations,” he said. To this end, Häkkinen's group computed a simulated STM image of the silver particle from 1,665 different orientations and developed a pattern recognition algorithm to determine which simulated images best matched the experimental data. “We believe that our work demonstrates a new useful strategy for the imaging of nanostructures. In the future, pattern recognition algorithms and artificial intelligence based on machine learning will become indispensable to the interpretation of images of nanostructures. Our work represents the first step in that direction. “That’s why we have also decided to openly distribute the pattern recognition software we had developed to other researchers," said Häkkinen. High-resolution real-space imaging of nanoparticle surfaces is desirable for achieving a better understanding of surface composition and morphology, molecular interactions at the surface, and nanoparticle chemical functionality in its environment. However, until now, achieving molecular or sub-molecular resolution has proven to be very challenging, due to highly curved nanoparticle surfaces and insufficient knowledge of the monolayer composition. This work demonstrates a working methodology for investigations of structure and composition of organic monolayers on curved nanoparticle surfaces, which could help in the design of functionalities for nanoparticle-based applications. The nanoparticle synthesis was performed in Xiamen University by Professor Nanfeng Zheng's research group and the STM measurements were carried out at Dalian Institute of Chemical Physics under the direction of Professor Zhibo Man. The research was published in Nature Communications (doi: https://doi.org/10.1038/s41467-018-05372-5).