Guide for Atomic Force Microscopy Image Analysis To Discriminate Heteroatoms in Aromatic Molecules

Abstract

Heteroatoms are essential for functional groups in organic structures and are complementary to hydrocarbon molecules in reactivities and properties. However, it is still a challenge to quickly identify heteroatoms with only non-contact atomic force microscopy (nc-AFM) to resolve chemical structures in complex unknown mixtures. This study aimed to understand the effect of elemental types on the contrast of atomic force microscopy (AFM) images using a few selected model heterocycles, including dibenzothiophene (DBT), acridine (ACR), and carbazole (CBZ). We identified several features that can be used to find common heteroatoms (S and N) and discriminated them from carbon atoms (C) using nc-AFM images alone. The mechanism of the atom and bond contrast was studied with image simulations and was found to be mostly correlated to van der Waals radii, but other factors, such as bonding geometry, electron density, and substrate interaction, need to be considered. This work will allow for rapid identification of these common heteroatoms in petroleum with AFM.

Imaging Titan’s Organic Haze at Atomic Scale

“…We assign these distortions to significant non-planarity of the molecule and how it affects the tilting of the CO at the tip apex (Gross et al 2012;Pavliček et al 2012;Hamalainen et al 2014;Hapala et al 2014). Our assignment of Titan's haze analogs is based on the AFM measurements of the model compounds and previously published AFM studies (Kawai et al 2018;Kocic et al 2016;van der Heijden et al 2016;Schuler et al 2017b;Zahl & Zhang 2019). In addition, comparison with high-resolution mass spectrometry (HR-MS) data (Ruger et al 2019) is used to qualitatively confirm the AFM-based molecular structures.…”

“…Model compounds containing different linear alkyl chains and cycloaliphatic moieties were studied in detail previously (Schuler et al 2017b). There are also some studies regarding the AFM image contrast of different heteroatoms in polycyclic hydrocarbons (Ellner et al 2017;Gross et al 2010;Kawai et al 2018;Kocic et al 2016;van der Heijden et al 2016;Zahl & Zhang 2019).…”

A Deep Learning Approach for Molecular Classification Based on AFM Images

“…Based on this analysis, we develop a specific architecture for molecular identification that shows an excellent performance in its application to theoretical images. Finally, we test the model, trained exclusively with theoretical images, with experimental images [ 47 ], trying to understand the differences between experimental and simulated AFM images that hamper a proper classification. We solve this problem with a variational autoencoder (VAE) [ 48 , 49 ] that allows us to generate, from very few experimental results, a small set of images that incorporate some of their characteristic features.…”

“…Figure 3. Experimental images for acridine, carbazole, and dibenzothiophene[47,53]. Each row shows, for the same molecule, the large variability introduced in the AFM images by different experimental conditions.…”

“…This variability represents a challenge for molecular classification based on AFM. The three images in the first column were reprinted with permission from ref [47]…”