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Register a new userQuantifying structural damage from self-irradiation in a plutonium superconductor
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The 18.5 K superconductor PuCoGa5 has many unusual properties, including those due to damage induced by self-irradiation. The superconducting transition temperature decreases sharply with time, suggesting a radiation-induced Frenkel defect concentration much larger than predicted by current radiation damage theories. Extended x-ray absorption fine-structure measurements demonstrate that while the local crystal structure in fresh material is well ordered, aged material is disordered much more strongly than expected from simple defects, consistent with strong disorder throughout the damage cascade region. These data highlight the potential impact of local lattice distortions relative to defects on the properties of irradiated materials and underscore the need for more atomic-resolution structural comparisons between radiation damage experiments and theory.
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Acting as artificial synapses, two-terminal memristive devices are considered fundamental building blocks for the realization of artificial neural networks. Organized into large arrays with a top-down approach, memristive devices in conventional crossbar architecture demonstrated the implementation of brain-inspired computing for supervised and unsupervised learning. Alternative way using unconventional systems consisting of many interacting nano-parts have been proposed for the realization of biologically plausible architectures where the emergent behavior arises from a complexity similar to that of biological neural circuits. However, these systems were unable to demonstrate bio-realistic implementation of synaptic functionalities with spatio-temporal processing of input signals similarly to our brain. Here we report on emergent synaptic behavior of biologically inspired nanoarchitecture based on self-assembled and highly interconnected nanowire (NW) networks realized with a bottom up approach. The operation principle of this system is based on the mutual electrochemical interaction among memristive NWs and NW junctions composing the network and regulating its connectivity depending on the input stimuli. The functional connectivity of the system was shown to be responsible for heterosynaptic plasticity that was experimentally demonstrated and modelled in a multiterminal configuration, where the formation of a synaptic pathway between two neuron terminals is responsible for a variation in synaptic strength also at non-stimulated terminals. These results highlight the ability of nanowire memristive architectures for building brain-inspired intelligent systems based on complex networks able to physically compute the information arising from multi-terminal inputs.
Dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D Fast Fourier Transforms (FFT), correlation and pair distribution function are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. The deep learning (DL)-based workflow is developed to analyze detailed particle dynamics on the particle-by-particle level. Beyond the macroscopic descriptors, we utilize the knowledge of local particle geometries and configurations to explore the evolution of local geometries and reconstruct the interaction potential between the particles. Finally, we use the machine learning-based feature extraction to define particle neighborhood free of physics constraints. This approach allowed separating the possible classes of particle behavior, identify the associated transition probabilities, and further extend this analysis to identify slow modes and associated configurations, allowing for systematic exploration and predictive modeling of the time dynamics of the system. Overall, this work establishes the DL based workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.
The evolution of porous structure and mechanical properties of binary glasses under tensile loading were examined using molecular dynamics simulations. We consider vitreous systems obtained in the process of phase separation after a rapid isochoric quench of a glass-forming liquid to a temperature below the glass transition. The porous structure in undeformed samples varies from a connected porous network to a random distribution of isolated pores upon increasing average glass density. We find that at small strain, the elastic modulus follows a power-law dependence on the average glass density and the pore size distribution remains nearly the same as in quiescent samples. Upon further loading, the pores become significantly deformed and coalesce into larger voids that leads to formation of system-spanning empty regions associated with breaking of the material.
Experiments on bilayer graphene unveiled a fascinating realization of stacking disorder where triangular domains with well-defined Bernal stacking are delimited by a hexagonal network of strain solitons. Here we show by means of numerical simulations that this is a consequence of a structural transformation of the moir{e} pattern inherent of twisted bilayer graphene taking place at twist angles $theta$ below a crossover angle $theta^{star}=1.2^{circ}$. The transformation is governed by the interplay between the interlayer van der Waals interaction and the in-plane strain field, and is revealed by a change in the functional form of the twist energy density. This transformation unveils an electronic regime characteristic of vanishing twist angles in which the charge density converges, though not uniformly, to that of ideal bilayer graphene with Bernal stacking. On the other hand, the stacking domain boundaries form a distinct charge density pattern that provides the STM signature of the hexagonal solitonic network.
Disorder can have a dominating influence on correlated and quantum materials leading to novel behaviors which have no clean limit counterparts. In magnetic systems, spin and exchange disorder can provide access to quantum criticality, frustration, and spin dynamics, but broad tunability of these responses and a deeper understanding of strong limit disorder is lacking. In this work, we demonstrate that high entropy oxides present an unexplored route to designing quantum materials in which the presence of strong local compositional disorder hosted on a positionally ordered lattice can be used to generate highly tunable emergent magnetic behavior--from macroscopically ordered states to frustration-driven dynamic spin interactions. Single crystal La(Cr0.2Mn0.2Fe0.2Co0.2Ni0.2)O3 films are used as a structurally uniform model system hosting a magnetic sublattice with massive microstate disorder in the form of site-to-site spin and exchange type inhomogeneity. A classical Heisenberg model is found to be sufficient to describe how compositionally disordered systems can paradoxically host long-range magnetic uniformity and demonstrates that balancing the populating elements based on their discrete quantum parameters can be used to give continuous control over ordering types and critical temperatures. Theory-guided experiments show that composite exchange values derived from the complex mix of microstate interactions can be used to design the required compositional parameters for a desired response. These predicted materials are synthesized and found to possess an incipient quantum critical point when magnetic ordering types are designed to be in direct competition; this leads to highly controllable exchange bias sensitivity in the monolithic single crystal films previously accessible only in intentionally designed bilayer heterojunctions.
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