It has recently been established that the high temperature (high-Tc(between atomic and macroscopic scale). Here we report micro X-ray diffraction imaging of the spatial distribution of both the charge-density-wave 'puddles' (domains with only a few wavelengths) and quenched disorder in HgBa 2 CuO 4+y , the single layer cuprate with the highest T c , 95 kelvin [26][27][28] . We found that the charge-density-wave puddles, like the steam bubbles in boiling water, have a fat-tailed size distribution that is typical of selforganization near a critical point 19 . However, the quenched disorder, which arises from oxygen interstitials, has a distribution that is contrary to the usual assumed random, uncorrelated distribution 12, 13 . The interstitials-oxygen-rich domains are spatially anticorrelated with the charge-density-wave domains, leading to a complex emergent geometry of the spatial landscape for superconductivity. 2Although it is known that the incommensurate charge-density-wave (CDW) order in cuprates (copper oxides) is made of ordered, stripy, nanoscale puddles with an average of only 3-4 oscillations, information about the size distribution and spatial organization of these puddles has so far not been available. We present experiments that demonstrate that CDW puddles, have a complex spatial distribution and coexist with, but are spatially anticorrelated to, quenched disorder in HgBa 2 CuO 4+y (Hg1201). The sample we studied is a layered perovskite at optimum doping with oxygen interstitials y=0.12, tetragonal symmetry P4/mmm and a low misfit strain [14][15][16] . The X-ray diffraction (XRD) measurements (see Methods) show diffuse CDW satellites (secondary peaks surrounding a main peak) at q CDW =(0.23a*, 0.16c*), in the b*=0 plane and q CDW =(0.23b*, 0.16c*) in the a*=0 plane (where a*, b*, and c* are the reciprocal lattice units) around specific Bragg peaks, such as (1 0 8), below the onset temperature T CDW =240 K (see Fig. 1a). The component of the momentum transfer q CDW in the CuO 2 plane (0.23a*) in this case is smaller than it is in the underdoped case (0.28a*) 5 . The temperature evolution of CDW-peak profile along a* (in the h direction; Fig. 1b) shows a smeared, glassy-like evolution below T CDW .The CDW-peak intensity reaches a maximum at T=100 K, followed by a drop associated with the onset of superconductivity at T=T c . We investigated the isotropic character of the CDW, in the a-b plane using azimuthal scans, as shown in Fig. 1c. We observed an equal probability of vertical and horizontally striped CDW puddles.Our main result is the discovery of the statistical spatial distribution of the CDW-puddle size and density throughout the sample, which shows an emergent complex network geometry for the superconducting phase. We performed scanning micro X-ray diffraction (SµXRD) measurements (see Methods) to extend the imaging of spatial inhomogeneity previously obtained by scanning tunneling microscopy [7][8][9] , from the surface to the bulk of the sample and from nanoscale to mesoscale spatial inh...

The disposition of defects in metal oxides is a key attribute exploited for applications from fuel cells and catalysts to superconducting devices and memristors. The most typical defects are mobile excess oxygens and oxygen vacancies, and can be manipulated by a variety of thermal protocols as well as optical and dc electric fields. Here we report the X-ray writing of high-quality superconducting regions, derived from defect ordering 1 , in the superoxygenated layered cuprate, La 2 CuO 4+y . Irradiation of a poor superconductor prepared by rapid thermal quenching results first in growth of ordered regions, with an enhancement of superconductivity becoming visible only after a waiting time, as is characteristic of other systems such as ferroelectrics 2,3 where strain must be accommodated for order to become extended. However, in La 2 CuO 4+y , we are able to resolve all aspects of the growth of (oxygen) intercalant order, including an extraordinary excursion from low to high and back to low anisotropy of the ordered regions. We can also clearly associate the onset of high quality superconductivity with defect ordering in two dimensions. Additional experiments with small beams demonstrate a photoresist-free, single-step strategy for writing functional materials.

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The experimental determination of the quantum critical point (QCP) that triggers the self-organization of charged striped domains in cuprate perovskites is reported. The phase diagram of doped cuprate superconductors is determined by a first variable, the hole doping δ, and a second variable, the micro-strain ε of the Cu-O bond length, obtained from the Cu K-edge extended x-ray absorption fine structure. For a fixed optimum doping, δ c = 0.16, we show the presence of the QCP for the onset of local lattice distortions and stripe formation at the critical micro-strain ε c. The critical temperature T c (ε, δ) reaches its maximum at the quantum critical point (ε c , δ c) for the formation of bubbles of superconducting stripes. The critical charge, orbital and spin fluctuations near this strain QCP provide the interaction for the pairing.

Multilayer networks are formed by several networks that interact with each other and co-evolve. Multilayer networks include social networks, financial markets, transportation systems, infrastructures and molecular networks and the brain. The multilayer structure of these networks strongly affects the properties of dynamical and stochastic processes defined on them, which can display unexpected characteristics. For example, interdependencies between different networks of a multilayer structure can cause cascades of failure events that can dramatically increase the fragility of these systems; spreading of diseases, opinions and ideas might take advantage of multilayer network topology and spread even when its single layers cannot sustain an epidemic when taken in isolation; diffusion on multilayer transportation networks can significantly speed up with respect to diffusion on single layers; finally, the interplay between multiplexity and controllability of multilayer networks is a problem with major consequences in financial, transportation, molecular biology and brain networks. This field is one of the most prosperous recent developments of Network Science and Data Science. Multilayer networks include multiplex networks, multi-slice temporal networks, networks of networks, interdependent networks. Multilayer networks are characterized by having a highly correlated multilayer network structure, providing a significant advantage for extracting information from them using multilayer network measures and centralities and community detection methods. The multilayer network dynamics (including percolation, epidemic spreading, diffusion, synchronization, game theory and control) is strongly affected by the multilayer network topology. This book will present a comprehensive account of this emerging field.

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Higher-order networks describe the many-body interactions of a large variety of complex systems, ranging from the the brain to collaboration networks. Simplicial complexes are generalized network structures which allow us to capture the combinatorial properties, the topology and the geometry of higher-order networks. Having been used extensively in quantum gravity to describe discrete or discretized space-time, simplicial complexes have only recently started becoming the representation of choice for capturing the underlying network topology and geometry of complex systems. This Element provides an in-depth introduction to the very hot topic of network theory, covering a wide range of subjects ranging from emergent hyperbolic geometry and topological data analysis to higher-order dynamics. This Elements aims to demonstrate that simplicial complexes provide a very general mathematical framework to reveal how higher-order dynamics depends on simplicial network topology and geometry.

We show the key role of the elastic local strain (or micro-strain) ε of the CuO 2 lattice in the phase diagram of cuprate superconductors. The superconducting critical temperature T c (δ, ε) is shown to be a function of two variables, the doping δ and the microstrain ε.