# Atomic Force Microscopy beyond Topography: Chemical Sensing of 2D Material Surfaces through Adhesion Measurements

## TL;DR

This paper shows how atomic force microscopy can detect chemical changes on 2D materials by measuring adhesion, offering a new way to study surface properties at the nanoscale.

## Contribution

The study introduces adhesion mapping as a novel method for chemical sensing of 2D materials using AFM.

## Key findings

- AFM adhesion mapping can distinguish between bare and functionalized 2D material layers.
- Adhesion values are independent of layer thickness, indicating surface-specific chemical information.
- Unsupervised k-means clustering improves classification of samples based on adhesion data.

## Abstract

Developing new functionalities of two-dimensional materials
(2Dms)
can be achieved by their chemical modification with a broad spectrum
of molecules. This functionalization is commonly studied by using
spectroscopies such as Raman, IR, or XPS, but the detection limit
is a common problem. In addition, these methods lack detailed spatial
resolution and cannot provide information about the homogeneity of
the coating. Atomic force microscopy (AFM), on the other hand, allows
the study of 2Dms on the nanoscale with excellent lateral resolution.
AFM has been extensively used for topographic analysis; however, it
is also a powerful tool for evaluating other properties far beyond
topography such as mechanical ones. Therefore, herein, we show how
AFM adhesion mapping of transition metal chalcogenide 2Dms (i.e.,
MnPS3 and MoS2) permits a close inspection of
the surface chemical properties. Moreover, the analysis of adhesion
as relative values allows a simple and robust strategy to distinguish
between bare and functionalized layers and significantly improves
the reproducibility between measurements. Remarkably, it is also confirmed
by statistical analysis that adhesion values do not depend on the
thickness of the layers, proving that they are related only to the
most superficial part of the materials. In addition, we have implemented
an unsupervised classification method using k-means clustering, an
artificial intelligence-based algorithm, to automatically classify
samples based on adhesion values. These results demonstrate the potential
of simple adhesion AFM measurements to inspect the chemical nature
of 2Dms and may have implications for the broad scientific community
working in the field.