Activity and autonomous motion are fundamental in living and engineering systems. This has stimulated the new field of ‘active matter’ in recent years, which focuses on the physical aspects of propulsion mechanisms, and on motility-induced emergent collective behavior of a larger number of identical agents. The scale of agents ranges from nanomotors and microswimmers, to cells, fish, birds, and people. Inspired by biological microswimmers, various designs of autonomous synthetic nano- and micromachines have been proposed. Such machines provide the basis for multifunctional, highly responsive, intelligent (artificial) active materials, which exhibit emergent behavior and the ability to perform tasks in response to external stimuli. A major challenge for understanding and designing active matter is their inherent nonequilibrium nature due to persistent energy consumption, which invalidates equilibrium concepts such as free energy, detailed balance, and time-reversal symmetry. Unraveling, predicting, and controlling the behavior of active matter is a truly interdisciplinary endeavor at the interface of biology, chemistry, ecology, engineering, mathematics, and physics. The vast complexity of phenomena and mechanisms involved in the self-organization and dynamics of motile active matter comprises a major challenge. Hence, to advance, and eventually reach a comprehensive understanding, this important research area requires a concerted, synergetic approach of the various disciplines. The 2020 motile active matter roadmap of Journal of Physics: Condensed Matter addresses the current state of the art of the field and provides guidance for both students as well as established scientists in their efforts to advance this fascinating area.

Active colloids are microscopic particles, which self-propel through viscous fluids by converting energy extracted from their environment into directed motion. We first explain how artificial microswimmers move forward by generating near-surface flow fields via self-phoresis or the self-induced Marangoni effect. We then discuss generic features of the dynamics of single active colloids in bulk and in confinement, as well as in the presence of gravity, field gradients, and fluid flow. In the third section, we review the emergent collective behavior of active colloidal suspensions, focusing on their structural and dynamic properties. After summarizing experimental observations, we give an overview of the progress in modeling collectively moving active colloids. While active Brownian particles are heavily used to study collective dynamics on large scales, more advanced methods are necessary to explore the importance of hydrodynamic and phoretic particle interactions. Finally, the relevant physical approaches to quantify the emergent collective behavior are presented.

1. Introduction 3082. Electron microscopy 3112.1 Specimen preparation 3112.2 The electron microscope 3112.3 Acceleration voltage, defocus, and the electron gun 3122.4 Magnification and data collection 3133. Digitisation and CTF correction 3173.1 The patchwork densitometer 3183.2 Particle selection 3203.3 Position dependent CTF correction 3213.4 Precision of CTF determination 3214. Single particles and angular reconstitution 3234.1 Preliminary filtering and centring of data 3234.2 Alignments using correlation functions 3244.3 Choice of first reference images 3244.4 Multi-reference alignment of data 3254.5 MSA eigenvector/eigenvalue data compression 3284.6 MSA classification 3304.7 Euler angle determination (‘angular reconstitution’) 3324.8 Sinograms and sinogram correlation functions 3324.9 Exploiting symmetry 3354.10 Three-dimensional reconstruction 3374.11 Euler angles using anchor sets 3394.12 Iterative refinements 3395. Computational hardware/software aspects 3415.1 The (IMAGIC) image processing workstation 3425.2 Operating systems and GUIs 3425.3 Computational logistics 3445.4 Shared memory machines 3445.5 Farming on loosely coupled computers 3465.6 Implementation using MPI protocol 3475.7 Software is what it's all about 3476. Interpretation of results 3486.1 Assessing resolution: the Fourier Shell Correlation 3486.2 Influence of filtering 3516.3 Rendering 3516.4 Searching for known sub-structures 3526.5 Interpretation 3537. Examples 3537.1 Icosahedral symmetry: TBSV at 5·9 Å resolution 3547.2 The D6 symmetrical worm hemoglobin at 13 Å resolution 3567.3 Functional states of the 70S E. coli ribosome 3577.4 The 50S E. coli ribosomal subunit at 7·5 Å resolution 3598. Perspectives 3619. Acknowledgements 36410. References 364In the past few years, electron microscopy (EM) has established itself as an important – still upcoming – technique for studying the structures of large biological macromolecules. EM is a very direct method of structure determination that complements the well-established techniques of X-ray crystallography and NMR spectroscopy. Electron micrographs record images of the object and not just their diffraction patterns and thus the classical ‘phase’ problem of X-ray crystallography does not exist in EM. Modern microscopes may reach resolution levels better than ∼ 1·5 Å, which is more than sufficient to elucidate the polypeptide backbone in proteins directly. X-ray structures at such resolution levels are considered ‘excellent’. The fundamental problem in biological EM is not so much the instrumental resolution of the microscopes, but rather the radiation sensitivity of the biological material one wants to investigate. Information about the specimen is collected in the photographic emulsion with the arrival of individual electrons that have (elastically) interacted with the specimen. However, many electrons will damage the specimen by non-elastic interactions. By the time enough electrons have passed through the object to produce a single good signal-to-noise (SNR) image, the biological sample will have been reduced to ashes. In contrast, stable inorganic specimens in material science often show interpretable details down to the highest possible instrumental resolution.