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The role of vascular niche and endothelial cells in organogenesis and regeneration
Highlights: • The vascular niche creates a permissive environment that realize developmental or regenerative programs. • The proximity between endothelium and cells of organs suggests a role of endothelial cells in the organs maturation. • Organs provide cues shaping vascular network structure. The term vascular niche indicate the physical and biochemical microenvironment around blood vessel where endothelial cells, pericytes, and smooth muscle cells organize themselves to form blood vessels and release molecules involved in the recruitment of hematopoietic stem cells, endothelial progenitor cells and mesenchymal stem cells. The vascular niche creates a permissive environment that enables different cell types to realize their developmental or regenerative programs. In this context, the proximity between the endothelium and the new-forming cellular components of organs suggests an essential role of endothelial cells in the organs maturation. Dynamic interactions between specific organ endothelial cells and different cellular conponents are crucial for different organ morphogenesis and function. Conversely, organs provide cues shaping vascular network structure.
Bayesian-based response expansion and uncertainty quantification using sparse measurement sets
Systems subjected to dynamic loads often require monitoring of their vibrational response, but limitations on the total number and placement of the measurement sensors can hinder the data-collection process. Here, we present an indirect approach to estimate a system’s full-field dynamic response, including all uninstrumented locations, using response measurements from sensors sparsely located on the system. This approach relies on Bayesian inference that utilizes a system model to estimate the full-field response and quantify the uncertainty in these estimates. By casting the estimation problem in the frequency domain, this approach utilizes the modal frequency response functions as a natural, frequency-dependent weighting scheme for the system mode shapes to perform the expansion. This frequency-dependent weighting scheme enables an accurate expansion, even with highly correlated mode shapes that may arise from spatial aliasing due to the limited number of sensors, provided these correlated modes do not have natural frequencies that are closely spaced. Furthermore, the inherent regularization mechanism that arises in this Bayesian-based procedure enables the utilization of the full set of system mode shapes for the expansion, rather than any reduced subset. This approach can produce estimates when considering a single realization of the measured responses, and with some modification, it can also produce estimates for power spectral density matrices measured from many realizations of the responses from statistically stationary random processes. A simply supported beam provides an initial numerical validation, and a cylindrical test article excited by acoustic loads in a reverberation chamber provides experimental validation.
Thermal equilibrium of collisional non-neutral plasma in a magnetic dipole trap
This paper discusses thermal equilibrium states of single-species plasmas, such as pure electron plasmas and pure positron plasmas, that are confined in a dipole trap. Thermal equilibrium states for such plasmas are routinely realized in the homogeneous magnetic field of Penning–Malmberg traps. We generalize the theory of these states to include inhomogeneous magnetic dipole fields. The approach to thermal equilibrium takes place in two stages with well separated time scales. On the collision time scale, thermal equilibrium is established along each magnetic field line. On the much longer transport time scale, heat conduction and viscosity bring the plasmas on different flux contours into thermal equilibrium, we call this a state of global thermal equilibrium. We present numerical results for local and global thermal equilibria. These results agree with the analytic predictions for charge collections that are large compared with the Debye length. There is, in principle, no limit to the confinement time of a single-species plasma in a global thermal equilibrium state. Experiments with hot electron–ion plasmas performed in the LDX and RT1 devices give us confidence that, in contrast to a Penning–Malmberg trap, a magnetic dipole field can also confine cold quasi-neutral electron–positron pair plasmas on the time scale of the phenomena of interest. Such pair plasmas are assumed to form in the magnetosphere of neutron stars but have so far not been realized in a laboratory. The creation of an electron–positron pair plasma is the main goal of the APEX collaboration.
First-Principles Simulation of Beam-Induced Processes Underlying Atomic Manipulation in Electron Microscopes
The development of experimental methods and apparatuses capable of promoting atomically precise material manipulations holds great promise for realizing the ultimate limit of feature miniaturization in materials and devices. The ability to modify materials atom by atom is anticipated to usher in new technologies in areas as diverse as separation science, medicine, and quantum information science. Historically, scanning probe-based techniques have been the most prominent approaches in this space. However, these methods are best suited for the manipulation of surface-exposed regions of materials, as the strong perturbations required for bond scission are delivered most effectively to atoms in the near-proximity to the scanning probe. In contrast, convergent electron beams with energies tuned slightly below the threshold for inducing irreversible knock-on damage have recently been employed (within scanning transmission electron microscopy) to promote atomic-scale bond rearrangements in various beam-stable solids. Currently, however, the efficiency and selectivity of beam-induced atomic manipulation processes with focused electron beams are such that long irradiation times are required to induce a desired atomic rearrangement. With a better understanding of the underlying physics dictating the outcome of a given irradiation event, methods can be devised to improve the efficiency of these techniques so that their promise can be fully realized through widespread adoption.To this end, this Account details our recent efforts to develop and apply tractable first-principles simulation approaches for studying the response of materials to electric beam-like external electric potentials applied in real space. We briefly review the concepts and capabilities in the area of atomically precise materials manipulation and review the early demonstrations of accomplishments in this area, focusing on studies using scanned convergent electron beam probes in particular. We expound upon the depth of the challenge and identify critical shortcomings of theoretical methods that have previously been employed in the simulation of beam-induced processes. We then describe the computational methods that we have generalized from the concepts and tools most commonly applied to the study of molecular photochemistry and how our adaptations of these methods can be employed to capture the relevant dynamical phenomena for beam-induced processes ranging from the initial electron scattering to the ensuing multistate reactions. Here, we contextualize these methods within the current state of the art in this area, which has historically focused primarily on the simulation of inelastic image formation in the electron microscope for the purpose of interpreting the results of quantitative electron microscopy experiments. We demonstrate that the spatial distribution of state-specific excitation rates due to the presence of an external (probe) electric charge is inhomogeneous, such that irradiation at particular locations in materials can favor specific electronic transitions (and disallow others). In addition to the potential for excited-state reaction pathways to be accessed through the initial inelastic scattering of the tightly focused electron beam from the targeted atoms, we also identify favorable conditions for the electronically nonadiabatic evolution of the highly vibrationally excited system to open complex multistate reaction pathways. Implications of the early results for understanding the mechanisms and potential routes to improved efficiency and selectivity in beam-induced reactions are discussed. We conclude with a summary of the current state of theory and modeling capabilities in this area and provide our perspective on future directions for theoretical and experimental developments that we view as crucial to advancing the use of convergent electron beams in mode-specific, atomically precise platforms for direct-write materials modifications.
Data-Driven Strategies for Accelerated Materials Design
The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.
Chemical Control of Magnetism in the Kagome Metal CoSn 1 – x In x : Magnetic Order from Nonmagnetic Substitutions
As a two-dimensional structural motif, the kagome net produces many interesting magnetic and electronic properties. In particular, this lattice produces flat electronic bands with a large density of states. When the chemical potential is positioned within these flat bands, electronic instabilities can result. For the kagome metal CoSn, this alignment is not realized, the flat bands are completely filled, and the compound is a Pauli paramagnet. For this study, we have grown crystals and powders of CoSn 1 – x In x and shown that replacing Sn with In moves the chemical potential into the flat band region, as expected from simple electron counting. This is supported by band structure calculations, heat capacity measurements, and angle-resolved photoemission spectroscopy. The increased density of states results in the emergence of antiferromagnetic order evidenced by magnetic susceptibility, Mössbauer spectroscopy, and neutron diffraction data. The Néel temperature reaches a maximum of 32 K. The emergence of magnetic order when introducing a nonmagnetic element into a nonmagnetic kagome metal is striking. This work provides clear evidence that flat bands arising from electronically frustrated lattices in bulk crystals provide a new and powerful way to realize correlated ground states controlled by crystal chemistry.
Electrochemical Random-Access Memory: Progress, Perspectives, and Opportunities
Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. Further, in this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.
Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials
As a corollary of the rapid advances in computing, ab initio simulation is playing an increasingly important role in modeling materials at the atomic scale. Two strategies are possible, ab initio Monte Carlo (AIMC) and molecular dynamics (AIMD) simulation. The former benefits from exact sampling from the correct thermodynamic distribution, while the latter is typically more efficient with its collective all-atom coordinate updates. In this study, using a relatively simple test model comprised of helium and argon, we show that AIMC can be brought up to, and even above, the performance levels of AIMD via a hybrid nested sampling/machine learning (ML) strategy. Here, ML provides an accurate classical reference potential (up to three-body explicit interactions) that can pilot long collective Monte Carlo moves that are accepted or rejected in toto à la nested Monte Carlo (NMC); this is in contrast to the single move nature of a naive implementation. Our proposed method only requires a small up front expense from evaluating the ab initio energies and forces of (100) random configurations for training. Importantly, our method does not totally rely on the trained, assuredly imperfect, interaction. We show that high performance and exact sampling at the desired level of theory can be realized even when the trained interaction has appreciable differences from the ab initio potential. Remarkably, at the highest levels of performance realized via our approach, a pair of statistically uncorrelated atomic configurations can be generated with (1) ab initio calculations.
Evidence for Field-Dependent Charge Separation Caused by Mixed Phases in Polymer–Fullerene Organic Solar Cells
As organic photovoltaic performance approaches 20% efficiencies, causal structure–performance relationships must be established for devices to realize theoretical limits and become commercially competitive. Here, we reveal evidence of a causal relationship between mixed donor–acceptor interfaces and charge generation in polymer–fullerene solar cells. To do this, we combine a holistic loss analysis of device performance with quantitative synchrotron X-ray nanocharacterization to identify a >98% anticorrelation between field-dependent geminate recombination and nanodomain purity. Importantly, our analysis eliminates other possible explanations of the performance trends, a requirement to establish causality. The unprecedented granular level of our analysis also separates field-dependent and field-independent recombination at the interface, where we find for the first time that this system is free of field-independent recombination, a loss channel that plagues high-performance systems, including those with non-fullerene acceptors. Furthermore, this result broadens the case that minimizing mixed phases to promote sharp interfaces between pure aggregated domains is the ideal nanostructure for realizing theoretical efficiency limits of organic photovoltaics.
Robust Topological Nodal-Line Semimetals from Periodic Vacancies in Two-Dimensional Materials
A nodal-line semimetal (NLSM) is suppressed in the presence of spin–orbit coupling unless it is protected by a nonsymmorphic symmetry. We show that two-dimensional (2D) materials can realize robust NLSMs when vacancies are introduced on the lattice. As a case study we investigate borophene, a boron honeycomb-like sheet. While the Dirac cones of pristine borophene are shown to be gapped out by spin–orbit coupling and by magnetic exchange, robust nodal lines (NLs) emerge in the spectrum when selected atoms are removed. We propose an effective 2D model and a symmetry analysis to demonstrate that these NLs are topological and protected by a nonsymmorphic glide plane. Finally, our findings offer a paradigm shift to the design of NLSMs: instead of searching for nonsymmorphic materials, robust NLSMs may be realized simply by removing atoms from ordinary symmorphic crystals.
Tunable Kondo Resonance at a Pristine Two-Dimensional Dirac Semimetal on a Kondo Insulator
The proximity of two different materials leads to an intricate coupling of quasiparticles so that an unprecedented electronic state is often realized at the interface. Here, we demonstrate a resonance-type many-body ground state in graphene, a nonmagnetic two-dimensional Dirac semimetal, when grown on SmB 6 , a Kondo insulator, via thermal decomposition of fullerene molecules. This ground state is typically observed in three-dimensional magnetic materials with correlated electrons. Above the characteristic Kondo temperature of the substrate, the electron band structure of pristine graphene remains almost intact in this work. As temperature decreases, however, the Dirac Fermions of graphene become hybridized with the Sm 4 f states. Remarkable enhancement of the hybridization and Kondo resonance is observed with further cooling and increasing charge-carrier density of graphene, evidencing the Kondo screening of the Sm 4 f local magnetic moment by the conduction electrons of graphene at the interface. These findings manifest the realization of the Kondo effect in graphene by the proximity of SmB 6 that is tuned by the temperature and charge-carrier density of graphene.
Proximate Quantum Spin Liquid on Designer Lattice
Complementary to bulk synthesis, here we propose a designer lattice with extremely high magnetic frustration and demonstrate the possible realization of a quantum spin liquid state from both experiments and theoretical calculations. In an ultrathin (111) CoCr 2 O 4 slice composed of three triangular and one kagome cation planes, the absence of a spin ordering or freezing transition is demonstrated down to 0.03 K, in the presence of strong antiferromagnetic correlations in the energy scale of 30 K between Co and Cr sublattices, leading to the frustration factor of similar to 1000. Persisting spin fluctuations are observed at low temperatures via low-energy muon spin relaxation. Our calculations further demonstrate the emergence of highly degenerate magnetic ground states at the 0 K limit, due to the competition among multiply altered exchange interactions. Furthermore, these results collectively indicate the realization of a proximate quantum spin liquid state on the synthetic lattice.
Imaging Quantum Interference in Stadium-Shaped Monolayer and Bilayer Graphene Quantum Dots
Experimental realizations of graphene-based stadium-shaped quantum dots (QDs) have been few and have been incompatible with scanned probe microscopy. Yet, the direct visualization of electronic states within these QDs is crucial for determining the existence of quantum chaos in these systems. Here we report the fabrication and characterization of electrostatically defined stadium-shaped QDs in heterostructure devices composed of monolayer graphene (MLG) and bilayer graphene (BLG). To realize a stadium-shaped QD, we utilized the tip of a scanning tunneling microscope to charge defects in a supporting hexagonal boron nitride flake. The stadium states visualized are consistent with tight-binding-based simulations but lack clear quantum chaos signatures. The absence of quantum chaos features in MLG-based stadium QDs is attributed to the leaky nature of the confinement potential due to Klein tunneling. In contrast, for BLG-based stadium QDs (which have stronger confinement) quantum chaos is precluded by the smooth confinement potential which reduces interference and mixing between states.
Manipulation of Exciton Dynamics in Single-Layer WSe 2 Using a Toroidal Dielectric Metasurface
Recent advances in emerging atomically thin transition metal dichalcogenide semiconductors with strong light–matter interactions and tunable optical properties provide novel approaches for realizing new material functionalities. Coupling two-dimensional semiconductors with all-dielectric resonant nanostructures represents an especially attractive opportunity for manipulating optical properties in both the near-field and far-field regimes. In this work, by integrating single-layer WSe 2 and titanium oxide (TiO 2 ) dielectric metasurfaces with toroidal resonances, we realized robust exciton emission enhancement over 1 order of magnitude at both room and low temperatures. Furthermore, we could control exciton dynamics and annihilation by using temperature to tailor the spectral overlap of excitonic and toroidal resonances, allowing us to selectively enhance the Purcell effect. Our results provide rich physical insight into the strong light–matter interactions in single-layer TMDs coupled with toroidal dielectric metasurfaces, with important implications for optoelectronics and photonics applications.
Proximity-Induced Superconductivity in a 2D Kondo Lattice of an f -Electron-Based Surface Alloy
Realizing hybrids of low-dimensional Kondo lattices and superconducting substrates leads to fascinating platforms for studying the exciting physics of strongly correlated electron systems with induced superconducting pairing. Here, we report a scanning tunneling microscopy and spectroscopy study of a new type of two-dimensional (2D) La–Ce alloy grown epitaxially on a superconducting Re(0001) substrate. We observe the characteristic spectroscopic signature of a hybridization gap evidencing the coherent spin screening in the 2D Kondo lattice realized by the ultrathin La–Ce alloy film on normal conducting Re(0001). Upon lowering the temperature below the critical temperature of rhenium, a superconducting gap is induced exhibiting an energy asymmetry of the coherence peaks that arises from the interaction of residual unscreened magnetic moments with the superconducting substrate. A positive correlation between the Kondo hybridization gap and the asymmetry of the coherence peaks is found.
KTaO 3 -Based Supercurrent Diode
The supercurrent diode effect (SDE), characterized by nonreciprocal critical currents, represents a promising building block for future dissipationless electronics and quantum circuits. Realizing SDE requires breaking both time-reversal and inversion symmetry in the device. Here we use conductive atomic force microscope (c-AFM) lithography to pattern reconfigurable superconducting weak links (WLs) at the LaAlO 3 /KTaO 3 (LAO/KTO) interface. By deliberately engineering the WL geometry at the nanoscale, we realize SDE in these devices in the presence of modest out-of-plane magnetic fields. The SDE polarity can be reversed by simply changing the WL position, and the rectification efficiency reaches up to 13% under optimal magnetic field conditions. Time-dependent Ginzburg–Landau simulations reveal that the observed SDE originates from asymmetric vortex motion in the inversion-symmetry-breaking device geometry. This demonstration of SDE in the LAO/KTO system establishes a versatile platform for investigating and engineering vortex dynamics, forming the basis for engineered quantum circuit elements.
High-Resolution Three-Dimensional Sculpting of Two-Dimensional Graphene Oxide by E-Beam Direct Write
On demand switchable additive/subtractive patterning of 2D nanomaterials is an essential capability for developing new concepts of functional nanomaterials and their device realizations. Traditionally, this is performed via a multi-step process using photoresist coating and patterning by conventional photo or electron beam lithography, which is followed by bulk dry/wet etch or deposition. This limits the range of functionalities and structural topologies that can be achieved, as well as increases the complexity, cost, and possibility of contamination which are significant barriers to device fabrication from highly sensitive 2D materials. Focused electron beam induced processing (FEBIP) enables a material chemistry/site-specific, high-resolution multi-mode atomic scale processing and provides unprecedented opportunities for direct-write, single-step surface patterning of 2D nanomaterials with an in-situ imaging capability. It allows for realizing a rapid multi-scale/multi-mode approach, ranging from an atomic scale manipulation (e.g., via targeted defect introduction as an active site) to a large-area surface modification on nano and micro scales, including patterned doping and material removal/deposition with 2D (in-plane)/3D (out-of-plane) control. In this work, we report on a new capability of FEBIP for nanoscale patterning of graphene oxide via removal of oxygenated carbon moieties with no use of reactive gas required for etching complemented by carbon atom deposition using focused electron beam. The mechanism of experimentally observed phenomena is explored using the density functional theory (DFT) calculations, revealing that interactions of e-beam liberated reactive oxygen radicals with carbon atoms on graphene basal plane lead to creation of atomic vacancies in the material. The reaction by-products are volatile carbon di-oxides, which are dissociated and volatilized from the graphene oxide surface functional groups by interactions with an energetic focused electron beam. Along with selective “subtractive” patterning of graphene oxide, the same electron beam with increased irradiation doses can deposit out-of-plane 3D carbon nanostructures on top of or around the 2D etched pattern, thus forming a hybrid 2D/3D nanocomposite with feature control down to a few nanometers. Furthermore, this in-operando dual nanofabrication capability of FEBIP is unmatched by any other nanopatterning techniques and opens a new design window for forming 2D/3D complex nanostructures and functional nanodevices.