Scanning Probe Microscopy Techniques for Surface Chemistry

Scanning probe microscopy (SPM) has emerged as an indispensable suite of tools for probing and manipulating chemical phenomena at solid interfaces with atomic precision. By bringing a sharp tip into close proximity with a surface, techniques such as scanning tunnelling microscopy (STM) and atomic force microscopy (AFM) allow direct visualisation of individual atoms, molecules and their electronic states, while specialised modalities yield insights into local chemical reactivity, bonding geometries and energy landscapes. Advances in tip functionalisation, non-contact detection and simultaneous spectroscopic measurement have extended the chemical sensitivity of SPM, enabling identification of reaction intermediates, mapping of force fields in hydrogen-bonded networks and in situ monitoring of on-surface synthesis. Recent progress in combining SPM with theoretical modelling has deepened understanding of frontier-orbital interactions and substrate-molecule hybridisation, guiding the rational design of surface-confined polymers, catalytic active sites and molecular spin systems. Automated control schemes driven by machine learning are now opening the way to high-throughput imaging, nanolithography and adaptive experiment design. Together, these developments are transforming surface science, underpinning advances in heterogeneous catalysis, energy storage interfaces, two-dimensional material engineering and quantum technologies, and offering direct, real-space views of chemistry at the ultimate spatial limit.

Research from Nature Portfolio

Recent studies have revealed that the symmetry of frontier molecular orbitals can be harnessed to tune magnetic exchange interactions in open-shell nanographenes. By correlating scanning probe measurements with theoretical calculations, researchers demonstrated that variations in orbital shape lead to exchange energies ranging from tens to over a hundred millielectronvolts, offering a strategy for designing graphene-based spintronic elements. A separate investigation introduced an artificial-intelligence framework for autonomous SPM operation, integrating a convolutional neural network to assess image quality and a deep reinforcement learning agent to maintain tip condition. This approach enables continuous, multi-day scanning routines and paves the way for large-scale data acquisition and precision nanolithography previously impractical under manual control.

Research from all publishers

Independent work has showcased the power of STM and AFM in the structural and electronic characterisation of single biomolecules at solid interfaces. High-resolution SPM imaging of DNA bases, amino acids, proteins and glycans has provided unprecedented real-space views of molecular conformations and local structure–property relationships, informing our understanding of biomolecular function on surfaces. Complementary studies have exploited STM and spectroscopy to monitor on-surface synthesis of graphene nanoribbons and nanographenes on titanium dioxide substrates. By combining Ullmann-type polymerisation with cyclodehydrogenation, researchers achieved bottom-up fabrication of well-defined carbon nanostructures and elucidated their electronic band structures through combined experimental and theoretical analysis, pointing to new routes for semiconductor and optoelectronic device integration.

Scanning Probe Microscopy Techniques for Surface Chemistry publication trend

The graph below shows the total number of articles in scanning probe microscopy techniques for surface chemistry across all publications each year (not limited to Nature Index journals).

Technical terms

Scanning probe microscopy (SPM): A family of techniques that image and manipulate surfaces using a nanoscale tip to probe electronic or force interactions.

Scanning tunnelling microscopy (STM): An SPM method that maps surface electronic density by measuring tunnelling current between a conductive tip and sample.

Atomic force microscopy (AFM): An SPM approach that detects deflection of a cantilevered tip to measure force interactions with surface atoms and molecules.

Frontier orbitals: The highest occupied and lowest unoccupied molecular orbitals, whose symmetry and energy govern chemical reactivity and electronic coupling.

Convolutional neural network (CNN): A machine-learning architecture that processes image data to recognise features such as tip resolution quality in SPM images.

Deep reinforcement learning: A machine-learning approach in which an agent learns optimal control policies—here for tip conditioning—through trial-and-error feedback.

References

1. Advances in probing single biomolecules: From DNA bases to glycans. Interdisciplinary Materials (2023).
2. Orbital-symmetry effects on magnetic exchange in open-shell nanographenes. Nature Communications (2023).
3. On-Surface Synthesis of Nanographenes and Graphene Nanoribbons on Titanium Dioxide. ACS Nano (2023).
4. Artificial-intelligence-driven scanning probe microscopy. Communications Physics (2020).
5. Mapping the force field of a hydrogen-bonded assembly. Nature Communications (2014).