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Prospecting for pluripotency in metamaterial design
Full text linkFrom self-assembly and protein folding to combinatorial metamaterials, a key challenge in material design is finding the right combination of interacting building blocks that yield targeted properties. Such structures are fiendishly difficult to find—not only are they rare, but often the design space is so rough that gradients are useless and direct optimization is hopeless. Here, we design ultrarare combinatorial metamaterials, capable of multiple desired deformations, by introducing a twofold strategy that avoids the drawbacks of direct optimization. We first combine convolutional neural networks with genetic algorithms to prospect for metamaterial designs with a potential for high performance; in our case, these metamaterials have a high number of spatially extended modes—they are pluripotent. Second, we exploit this library of pluripotent designs to generate metamaterials with multiple target deformations, which we finally refine by strategically placing defects. Our multishape metamaterials would be impossible to design through trial-and-error or standard optimization. Instead, our data-driven approach is systematic and ideally suited to tackling the large and intractable combinatorial problems that are pervasive in material science
Roadmap for Animate Matter
Full text linkHumanity has long sought inspiration from nature to innovate materials and devices. As science advances, nature-inspired materials are becoming part of our lives. Animate materials, characterized by their activity, adaptability, and autonomy, emulate properties of living systems. While only biological materials fully embody these principles, artificial versions are advancing rapidly, promising transformative impacts in the circular economy, health and climate resilience within a generation. This roadmap presents authoritative perspectives on animate materials across different disciplines and scales, highlighting their interdisciplinary nature and potential applications in diverse fields including nanotechnology, robotics and the built environment. It underscores the need for concerted efforts to address shared challenges such as complexity management, scalability, evolvability, interdisciplinary collaboration, and ethical and environmental considerations. The framework defined by classifying materials based on their level of animacy can guide this emerging field to encourage cooperation and responsible development. By unravelling the mysteries of living matter and leveraging its principles, we can design materials and systems that will transform our world in a more sustainable manner
Reprogrammable, In-Materia Matrix-Vector Multiplication with Floppy Modes
Full text linkMatrix-vector multiplications are a fundamental building block of artificial intelligence; this essential role has motivated their implementation in a variety of physical substrates, from memristor crossbar arrays to photonic-integrated circuits. Yet their realization in soft-matter intelligent systems remains elusive. Herein, A reprogrammable elastic metamaterial that computes matrix-vector multiplications using floppy modes—deformations with near-zero stored elastic energy is experimentally demonstrated. Floppy modes allow to program complex deformations without being hindered by the natural stiffness of the material; but their practical application is challenging, as their existence depends on global topological properties of the system. To overcome this challenge, a continuously parameterized unit cell design with well-defined compatibility characteristics is introduced. This unit cell is then combined to form arbitrary matrix-vector multiplications that can even be reprogrammed after fabrication. The results demonstrate that floppy modes can act as key enablers for embodied intelligence, smart micro electro mechanical systems(MEMS) devices, and in-sensor edge computing
Information Processing in Biochemical Networks
Full text linkLiving systems are characterized by controlled flows of matter, energy, and information. While the biophysics community has productively engaged with the first two, addressing information flows has been more challenging, with some scattered success in evolutionary theory and a more coherent track record in neuroscience. Nevertheless, interdisciplinary work of the past two decades at the interface of biophysics, quantitative biology, and engineering has led to an emerging mathematical language for describing information flows at the molecular scale. This is where the central processes of life unfold: from detection and transduction of environmental signals to the readout or copying of genetic information and the triggering of adaptive cellular responses. Such processes are coordinated by complex biochemical reaction networks that operate at room temperature, are out of equilibrium, and use low copy numbers of diverse molecular species with limited interaction specificity. Here we review how flows of information through biochemical networks can be formalized using information-theoretic quantities, quantified from data, and computed within various modeling frameworks. Optimization of information flows is presented as a candidate design principle that navigates the relevant time, energy, crosstalk, and metabolic constraints to predict reliable cellular signaling and gene regulation architectures built of individually noisy components
Interferon-responsive intestinal BEST4/CA7+ cells are targets of bacterial diarrheal toxins
Full text linkBEST4/CA7+ cells of the human intestine were recently identified by single-cell RNA sequencing. While their gene expression profile predicts a role in electrolyte balance, BEST4/CA7+ cell function has not been explored experimentally owing to the absence of BEST4/CA7+ cells in mice and the paucity of human in vitro models. Here, we establish a protocol that allows the emergence of BEST4/CA7+ cells in human intestinal organoids. Differentiation of BEST4/CA7+ cells requires activation of Notch signaling and the transcription factor SPIB. BEST4/CA7+ cell numbers strongly increase in response to the cytokine interferon-γ, supporting a role in immunity. Indeed, we demonstrate that BEST4/CA7+ cells generate robust CFTR-mediated fluid efflux when stimulated with bacterial diarrhea-causing toxins and find the norepinephrine-ADRA2A axis as a potential mechanism in blocking BEST4/CA7+ cell-mediated fluid secretion. Our observations identify a central role of BEST4/CA7+ cells in fluid homeostasis in response to bacterial infections
Accessing Beyond-Light Line Dispersion and High-Q Resonances of Dense Plasmon Lattices by Bandfolding
Full text linkDense plasmon lattices are promising as experimentally accessible implementations of seminal tight-binding Hamiltonians, but the plasmonic dispersion of interest lies far beyond the light line and is thereby inaccessible in far-field optical experiments. In this work, we make the guided mode dispersion of dense hexagonal plasmon antenna lattices visible by bandfolding induced by perturbative scatterer size modulations that introduce supercell periodicity. We present fluorescence enhancement experiments and reciprocity-based T-matrix simulations for a systematic variation of perturbation strength. We evidence that folding the K-point into the light cone gives rise to a narrow plasmon mode, achieving among the highest reported quality factors for plasmon lattice resonances in the visible wavelength range despite a doubled areal density of plasmon antennas. We finally show K-point lasing and spontaneous symmetry breaking between the bandfolded K- and K′-modes, signifying that intrinsic symmetry properties of the dense plasmon lattice are maintained and can be observed upon band folding
Integrated artificial neurons from metal halide perovskites
Full text linkHardware neural networks could perform certain computational tasks orders of magnitude more energy-efficiently than conventional computers. Artificial neurons are a key component of these networks and are currently implemented with electronic circuits based on capacitors and transistors. However, artificial neurons based on memristive devices are a promising alternative, owing to their potentially smaller size and inherent stochasticity. But despite their promise, demonstrations of memristive artificial neurons have so far been limited. Here we demonstrate a fully on-chip artificial neuron based on microscale electrodes and halide perovskite semiconductors as the active layer. By connecting a halide perovskite memristive device in series with a capacitor, the device demonstrates stochastic leaky integrate-and-fire behavior, with an energy consumption of 20 to 60 pJ per spike, lower than that of a biological neuron. We simulate populations of our neuron and show that the stochastic firing allows the detection of sub-threshold inputs. The neuron can easily be integrated with previously-demonstrated halide perovskite artificial synapses in energy-efficient neural networks
Ultrafast Switching of Whispering Gallery Modes in Quantum Dot Superparticles
Full text linkMicroscopic dielectric structures can leverage geometry and photophysics to confine light, acting as microresonators. However, the use of light to reversibly manipulate the spectral pattern of photonic resonances on ultrafast time scales has hardly been explored. Here, we use femtosecond light pulses to drive reversible changes in the photonic resonances of optical microresonators over a broad spectral range. We employ pump-probe microscopy to investigate the dynamic modulation of the photonic response of whispering-gallery microresonator superparticles self-assembled from colloidal quantum dots. Our findings provide crucial insight into the photophysics of semiconductor superstructures, paving the way to their prospective application as ultrafast optical switches for photonics, optoelectronics, and communication technologies. In particular, we demonstrate that ultrafast photoexcitation can initiate ultrafast excitation transfer between neighboring superparticles, forming a dimer, and induce electronically and thermally driven changes in the refractive index of individual superparticles, dynamically modulating their resonances on distinctive time scales
Comparing kinetic proofreading and kinetic segregation for T cell receptor activation
Full text linkThe T cell receptor (TCR) is a key component of the adaptive immune system, recognizing foreign antigens (ligands) and triggering an immune response. To explain the high sensitivity and selectivity of the TCR in discriminating "self"from "non-self"ligands, most models evoke kinetic proofreading (KP) schemes, however it is unclear how competing models used for TCR triggering, such as the kinetic segregation (KS) model, influence KP performance. In this paper, we consider two different TCR triggering models and their influence on subsequent KP-based ligand discrimination by the TCR: a classic conformational change model (CC-KP), where ligand-TCR binding is strictly required for activation, and the kinetic segregation model (KS-KP), where only residence of the TCR within a close contact devoid of kinases is required for its activation. Building on previous work, our computational model permits a head-to-head comparison of these models in silico. While we find that both models can be used to explain the probability of TCR activation across much of the parameter space, we find biologically important regions in the parameter space where significant differences in performance can be expected. Furthermore, we show that the available experimental evidence may favor the KS-KP model over CC-KP. Our results may be used to motivate and guide future experiments to determine accurate mathematical models of TCR function
Effects of lamella preparation on InGaN quantum well luminescence
Full text linkDespite it being a popular sample preparation technique, the optoelectronic effects of thinning bulk samples into lamella using a Ga-based focused ion beam (FIB), as is commonly performed for studies in a transmission electron microscope, have been seldom studied systematically. In this work, we confront this using correlative cathodoluminescence spectroscopy to investigate the optical properties of high In content c-plane InGaN/GaN quantum wells (QWs), fabricated including a growth-interrupting step that forms regions of quantum disk InGaN islands that behave as localized emitters and, furthermore, reduce the strain-induced quantum Stark effect present in the majority of such heterostructures. Using picosecond electrostatically beam-blanked electron pulses, we measured the decay transients as a function of position and wavelength. The sample was studied before and after undergoing preparation as a lamella, demonstrating that FIB preparation affects both the spectral and temporal luminescence properties despite measures undertaken to protect the sample during fabrication. Non-radiative defects introduced by the ion beam quenched the luminescence as well as reduced the lifetime of emission, with the QW luminescence component particularly affected due to the emission being weakly localized and hence allowing carriers to migrate to defect areas. These findings underscore the importance of correlating bulk and lamella properties to accurately interpret optical measurements