NOTICIAS DIARIAS

Scanning Tunneling Microscopy Imaging Molecule-Covered Surface Structures

Anaesthesiology

A study determined that by using scanning tunneling microscopy (STM), extremely high-resolution imaging of the molecule-covered surface structures of silver nanoparticles is possible, even down to the recognition of individual parts of the molecules protecting the surface. Real-space imaging with pattern recognition of a ligand-protected Ag374nanocluster at a sub-molecular resolution. The study was published in Nature Communications.

Scanning Tunneling Microscopy 

Using scanning tunneling microscopy (STM), extremely high-resolution imaging of the molecule-covered surface structures of silver nanoparticles is possible, even down to the recognition of individual parts of the molecules protecting the surface.

This was the finding of joint research between China and Finland, led in Finland by Academy Professor Hannu Häkkinen of the University of Jyväskylä. Studying the surface structures of nanoparticles at atomic resolution is vital to understanding the chemical properties of their structures, molecular interactions and the functioning of particles in their environments. 

Experimental research on surface structures has long involved imaging techniques suitable for nanometer-level resolution, the most common of which are based on electron tunneling, the abovementioned scanning tunneling microscopy (STM), and atomic force microscopy (AFM) based on the measurement of small, atomic-scale forces.

However, achieving molecular resolution in imaging has proven highly challenging, for example, because the curvature of the object to be imaged, i.e. the nanoparticle's surface, is of the same order as the curvature of the scanning tip. Measurements are also sensitive to environmental disturbances, which may affect the thermal movement of molecules.

The researchers used previously characterized silver nanoparticles, with a known atomic structure. The metal core of the particles has 374 silver atoms and the surface is protected by a set of 113 TBTT molecules.

TBBT (tertbutyl-benzenethiol) 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 STM experiment, clear sequential modulations were observed in the tunneling current formed by the image (see left part of 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 Häkkinen's research team showed that each of the three carbon groups of the TBBT molecule provides its own current maximum in the STM image (see the right part of the image) and that the distances between the maxima corresponded to the STM measurement results. This confirmed that measurement was successful at the sub-molecular level.

The simulations also predicted that accurate STM measurement can no longer be successful at room temperature, as the thermal movement of the molecules is so high that the current maxima of individual carbon groups blend into the background.

Scanning Tunneling Microscopy Imaging

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 verify the experimental results. 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.

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.

Nanostructures Imaging

They 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.