2018, Ando, Toshio, Bhamidimarri, Satya Prathyusha, Brending, Niklas, Colin-York, H, Collinson, Lucy, De Jonge, Niels, de Pablo, P J, Debroye, Elke, Eggeling, Christian, Franck, Christian, Fritzsche, Marco, Gerritsen, Hans, Giepmans, Ben N G, Grunewald, Kay, Hofkens, Johan, Hoogenboom, Jacob P, Janssen, Kris P F, Kaufmann, Rainer, Klumpermann, Judith, Kurniawan, Nyoman, Kusch, Jana, Liv, Nalan, Parekh, Viha, Peckys, Diana B, Rehfeldt, Florian, Reutens, David C, Roeffaers, Maarten B J, Salditt, Tim, Schaap, Iwan A T, Schwarz, Ulrich S, Verkade, Paul, Vogel, Michael W, Wagner, Richard, Winterhalter, Mathias, Yuan, Haifeng, Zifarelli, Giovanni

Developments in microscopy have been instrumental to progress in the life sciences, and many new techniques have been introduced and led to new discoveries throughout the last century. A wide and diverse range of methodologies is now available, including electron microscopy, atomic force microscopy, magnetic resonance imaging, small-angle x-ray scattering and multiple super-resolution fluorescence techniques, and each of these methods provides valuable read-outs to meet the demands set by the samples under study. Yet, the investigation of cell development requires a multi-parametric approach to address both the structure and spatio-temporal organization of organelles, and also the transduction of chemical signals and forces involved in cell–cell interactions. Although the microscopy technologies for observing each of these characteristics are well developed, none of them can offer read-out of all characteristics simultaneously, which limits the information content of a measurement. For example, while electron microscopy is able to disclose the structural layout of cells and the macromolecular arrangement of proteins, it cannot directly follow dynamics in living cells. The latter can be achieved with fluorescence microscopy which, however, requires labelling and lacks spatial resolution. A remedy is to combine and correlate different readouts from the same specimen, which opens new avenues to understand structure–function relations in biomedical research. At the same time, such correlative approaches pose new challenges concerning sample preparation, instrument stability, region of interest retrieval, and data analysis. Because the field of correlative microscopy is relatively young, the capabilities of the various approaches have yet to be fully explored, and uncertainties remain when considering the best choice of strategy and workflow for the correlative experiment. With this in mind, the Journal of Physics D: Applied Physics presents a special roadmap on the correlative microscopy techniques, giving a comprehensive overview from various leading scientists in this field, via a collection of multiple short viewpoints.

2018, Heidemann, Knut M., Sageman-Furnas, Andrew O., Sharma, Abhinav, Rehfeldt, Florian, Schmidt, Christoph F., Wardetzky, Max

Filamentous polymer networks govern the mechanical properties of many biological materials. Force distributions within these networks are typically highly inhomogeneous, and, although the importance of force distributions for structural properties is well recognized, they are far from being understood quantitatively. Using a combination of probabilistic and graph-theoretical techniques, we derive force distributions in a model system consisting of ensembles of random linear spring networks on a circle. We show that characteristic quantities, such as the mean and variance of the force supported by individual springs, can be derived explicitly in terms of only two parameters: (i) average connectivity and (ii) number of nodes. Our analysis shows that a classical mean-field approach fails to capture these characteristic quantities correctly. In contrast, we demonstrate that network topology is a crucial determinant of force distributions in an elastic spring network. Our results for 1D linear spring networks readily generalize to arbitrary dimensions.

2018, Heidemann, Knut M., Sageman-Furnas, Andrew O., Sharma, Abhinav, Rehfeldt, Florian, Schmidt, Christoph F., Wardetzky, Max

Networks of elastic fibers are ubiquitous in biological systems and often provide mechanical stability to cells and tissues. Fiber-reinforced materials are also common in technology. An important characteristic of such materials is their resistance to failure under load. Rupture occurs when fibers break under excessive force and when that failure propagates. Therefore, it is crucial to understand force distributions. Force distributions within such networks are typically highly inhomogeneous and are not well understood. Here we construct a simple one-dimensional model system with periodic boundary conditions by randomly placing linear springs on a circle. We consider ensembles of such networks that consist of N nodes and have an average degree of connectivity z but vary in topology. Using a graph-theoretical approach that accounts for the full topology of each network in the ensemble, we show that, surprisingly, the force distributions can be fully characterized in terms of the parameters (N,z). Despite the universal properties of such (N,z) ensembles, our analysis further reveals that a classical mean-field approach fails to capture force distributions correctly. We demonstrate that network topology is a crucial determinant of force distributions in elastic spring networks.

2019, Delsing, Per, Cleland, Andrew N., Schuetz, Martin J.A., Knörzer, Johannes, Giedke, Géza, Cirac, J. Ignacio, Srinivasan, Kartik, Wu, Marcelo, Balram, Krishna Coimbatore, Bäuerle, Christopher, Meunier, Tristan, Ford, Christopher J.B., Santos, Paulo V., Cerda-Méndez, Edgar, Wang, Hailin, Krenner, Hubert J., Nysten, Emeline D.S., Weiß, Matthias, Nash, Geoff R., Thevenard, Laura, Gourdon, Catherine, Rovillain, Pauline, Marangolo, Max, Duquesne, Jean-Yves, Fischerauer, Gerhard, Ruile, Werner, Reiner, Alexander, Paschke, Ben, Denysenko, Dmytro, Volkmer, Dirk, Wixforth, Achim, Bruus, Henrik, Wiklund, Martin, Reboud, Julien, Cooper, Jonathan M., Fu, YongQing, Brugger, Manuel S., Rehfeldt, Florian, Westerhausen, Christoph

Today, surface acoustic waves (SAWs) and bulk acoustic waves are already two of the very few phononic technologies of industrial relevance and can been found in a myriad of devices employing these nanoscale earthquakes on a chip. Acoustic radio frequency filters, for instance, are integral parts of wireless devices. SAWs in particular find applications in life sciences and microfluidics for sensing and mixing of tiny amounts of liquids. In addition to this continuously growing number of applications, SAWs are ideally suited to probe and control elementary excitations in condensed matter at the limit of single quantum excitations. Even collective excitations, classical or quantum are nowadays coherently interfaced by SAWs. This wide, highly diverse, interdisciplinary and continuously expanding spectrum literally unites advanced sensing and manipulation applications. Remarkably, SAW technology is inherently multiscale and spans from single atomic or nanoscopic units up even to the millimeter scale. The aim of this Roadmap is to present a snapshot of the present state of surface acoustic wave science and technology in 2019 and provide an opinion on the challenges and opportunities that the future holds from a group of renown experts, covering the interdisciplinary key areas, ranging from fundamental quantum effects to practical applications of acoustic devices in life science. © 2019 IOP Publishing Ltd.