The helium ion microscope (HIM) has emerged as an instrument of choice for patterning, imaging and, more recently, analytics at the nanoscale. Here, we review secondary electron imaging on the HIM and the various methodologies and hardware components that have been developed to confer analytical capabilities to the HIM. Secondary electron–based imaging can be performed at resolutions down to 0.5 nm with high contrast, with high depth of field, and directly on insulating samples. Analytical methods include secondary electron hyperspectral imaging (SEHI), scanning transmission ion microscopy (STIM), backscattering spectrometry and, in particular, secondary ion mass spectrometry (SIMS). The SIMS system that was specifically designed for the HIM allows the detection of all elements, the differentiation between isotopes, and the detection of trace elements. It provides mass spectra, depth profiles, and 2D or 3D images with lateral resolutions down to 10 nm.

This work reports the strong correlation between the conductivity of fused porphyrins thin films and the porphyrin substituents.

This paper is a review on the combination between Helium Ion Microscopy (HIM) and Secondary Ion Mass Spectrometry (SIMS), which is a recently developed technique that is of particular relevance in the context of the quest for high-resolution high-sensitivity nano-analytical solutions. We start by giving an overview on the HIM-SIMS concept and the underlying fundamental principles of both HIM and SIMS. We then present and discuss instrumental aspects of the HIM and SIMS techniques, highlighting the advantage of the integrated HIM-SIMS instrument. We give an overview on the performance characteristics of the HIM-SIMS technique, which is capable of producing elemental SIMS maps with lateral resolution below 20 nm, approaching the physical resolution limits, while maintaining a sub-nanometric resolution in the secondary electron microscopy mode. In addition, we showcase different strategies and methods allowing to take profit of both capabilities of the HIM-SIMS instrument (high-resolution imaging using secondary electrons and mass filtered secondary sons) in a correlative approach. Since its development HIM-SIMS has been successfully applied to a large variety of scientific and technological topics. Here, we will present and summarise recent applications of nanoscale imaging in materials research, life sciences and geology.

and are important in various technological fields such as energy, electronics, medicine, and many more. [1][2][3][4][5] However, as a consequence of industrial processes and man-made pollution, unwanted nanoparticle size distributions and concentrations [6] give rise to concerns with respect to human health and environmental pollution. While the nanoparticles' physicochemical properties (size, shape, surface chemistry, etc.) determine the quality of products, [7,8] such characteristics are also important in order to evaluate the biological impact of nanoparticles at a molecular, cellular, and systemic level for any risk assessment for environmental and human health. [9] Characterizing nanoparticles in a dynamic context and on a case-by-case basis, microscopic imaging techniques including those that use focused electron or ion beams in scanning electron microscopes (SEMs) or helium ion microscopes [10] (HIMs) to generate nanometer scale spatial resolution are frequently applied in the scientific community. Given the substantial information content of digital images, these techniques often benefit from, or require, automated high-throughput data analysis that enables the accurate identification of large numbers of particles in a robust way.Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, a segmentation accuracy can be derived that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, highthroughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro-and nanoplastic particles in water and tissue samples.