Since the inception of the atomic force microscope (AFM) in 1986, influential papers have been presented by the community and tremendous advances have been reported. Being able to routinely image conductive and non-conductive surfaces in air, liquid and vacuum environments with nanoscale, and sometimes atomic, resolution, the AFM has long been perceived by many as the instrument to unlock the nanoscale. From exploiting a basic form of Hooke's law to interpret AFM data to interpreting a seeming zoo of maps in the more advanced multifrequency methods however, an inflection point has been reached. Here, we discuss this evolution, from the fundamental dilemmas that arose in the beginning, to the exploitation of computer sciences, from machine learning to big data, hoping to guide the newcomer and inspire the experimenter.

All surfaces exposed to ambient conditions are covered by a thin film of water. Other than at high humidity conditions, i.e., relative humidity higher than 80%, those water films have nanoscale thickness. Nevertheless, even the thinnest film can profoundly affect the physical and chemical properties of the substrate. Information on the structure of these water films can be obtained from spectroscopic techniques based on photons, but these usually have poor lateral resolution. When information with nanometer resolution in the three dimensions is needed, for example for surfaces showing heterogeneity in water affinity at the nanoscale, Atomic Force Microscopy (AFM) is the preferred tool since it can provide such resolution while being operated in ambient conditions. A complication in the interpretation of the data arises when using AFM, however, since, in most cases, direct interaction between a solid probe and a solid surface occurs. This induces strong perturbations of the liquid by the probe that should be controlled or avoided. The aim of this review is to provide an overview of different AFM methods developed to overcome this problem, measuring different interactions between the AFM probe and the water films, and to discuss the type of information about the water film that can be obtained from these interactions.

We describe fundamental energy dissipation in dynamic nanoscale processes in terms of the localization of the interactions. In this respect, the areal density of the energy dissipated per cycle and the effective area of interaction in which each process occurs are calculated for four elementary dissipative processes. It is the ratio between these two, which we term M, that provides information about how localized the interactions are. While our results are general, we use concepts from dynamic atomic force microscopy to describe the physical phenomenon. We show that neither the phase lag, nor the magnitude of the energy dissipated alone provide information about how dissipative processes are localized. Instead, M has to be considered.

During their synthesis, multi-walled carbon nanotubes can be aligned and impregnated in a polymer matrix to form an electrically conductive and flexible nanocomposite with high backing density. The material exhibits the highest reported electrical conductivity of CNT-epoxy composites (350 S/m). Here, we show how conductive atomic force microscopy can be used to study the electrical transport mechanism in order to explain the enhanced electrical properties of the composite. The high spatial resolution and versatility of the technique allows us to further decouple the two main contributions to the electrical transport: (1) the intrinsic resistance of the tube and (2) the tunneling resistance due to nanoscale gaps occurring between the epoxy-coated tubes along the composite. The results show that the material behaves as a conductive polymer, and the electrical transport is governed by electron tunneling at interconnecting CNT-polymer junctions. We also point out the theoretical formulation of the nanoscale electrical transport between the AFM tip and the sample in order to derive both the composite conductivity and the CNT intrinsic properties. The enhanced electrical properties of the composite are attributed to high degree of alignment, the CNT purity, and the large tube diameter which lead to low junction resistance. By controlling the tube diameter and using other polymers, the nanocomposite electrical conductivity can be improved.

Since the inception of the atomic force microscope (AFM), dynamic methods (dynamic atomic force microscopy) have been very fruitful by establishing methods to quantify dissipative and conservative forces in the nanoscale and by providing a means to apply gentle forces to the samples with high resolution. Here, we discuss developments that cover over a decade of our work on energy dissipation, phase contrast, and the extraction of relevant material properties from observables. We describe the attempts to recover material properties via one-dimensional amplitude and phase curves from force models and explore the evolution of these methods in terms of force reconstruction, fits of experimental measurements, and the more recent advances in multifrequency AFM. We further discuss open questions and key possible paths to advance the field.

In dynamic atomic force microscopy, nanoscale properties are encoded in the higher harmonics. Nevertheless, when gentle interactions and minimal invasiveness are required, these harmonics are typically undetectable. Here, we propose to externally drive an arbitrary number of exact higher harmonics above the noise level. In this way, multiple contrast channels that are sensitive to compositional variations are made accessible. Numerical integration of the equation of motion shows that the external introduction of exact harmonic frequencies does not compromise the fundamental frequency. Thermal fluctuations are also considered within the detection bandwidth of interest and discussed in terms of higher-harmonic phase contrast in the presence and absence of an external excitation of higher harmonics. Higher harmonic phase shifts further provide the means to directly decouple the true topography from that induced by compositional heterogeneity.