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Dark-State Enhanced Loading of an Optical Tweezer Array
No full textNeutral atoms and molecules trapped in optical tweezers have become a prevalent resource for quantum simulation, computation, and metrology. However, the maximum achievable system sizes of such arrays are often limited by the stochastic nature of loading into optical tweezers, with a typical loading probability of only 50%. Here we present a species-agnostic method for dark-state enhanced loading (DSEL) based on real-time feedback, long-lived shelving states, and iterated array reloading. We demonstrate this technique with a 95-tweezer array of ⁸⁸Sr atoms, achieving a maximum loading probability of 84.02(4)% and a maximum array size of 91 atoms in one dimension. Our protocol is complementary to, and compatible with, existing schemes for enhanced loading based on direct control over light-assisted collisions, and we predict it can enable close-to-unity filling for arrays of atoms or molecules
Input optics systems of the KAGRA detector during O3GK
No full textKAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity
A phenazine-inspired framework for identifying biological functions of microbial redox-active metabolites
No full textWhile the list of small molecules known to be secreted by environmental microbes continues to grow, our understanding of their in situ biological functions remains minimal. The time has come to develop a framework to parse the meaning of these “secondary metabolites,” which are ecologically ubiquitous and have direct applications in medicine and biotechnology. Here, we focus on a particular subset of molecules, redox active metabolites (RAMs), and review the well-studied phenazines as archetypes of this class. We argue that efforts to characterize the chemical, physical and biological makeup of the microenvironments, wherein these molecules are produced, coupled with measurements of the molecules' basic chemical properties, will enable significant progress in understanding the precise roles of novel RAMs
Non-Abelian effects in dissipative photonic topological lattices
No full textTopology is central to phenomena that arise in a variety of fields, ranging from quantum field theory to quantum information science to condensed matter physics. Recently, the study of topology has been extended to open systems, leading to a plethora of intriguing effects such as topological lasing, exceptional surfaces, as well as non-Hermitian bulk-boundary correspondence. Here, we show that Bloch eigenstates associated with lattices with dissipatively coupled elements exhibit geometric properties that cannot be described via scalar Berry phases, in sharp contrast to conservative Hamiltonians with non-degenerate energy levels. This unusual behavior can be attributed to the significant population exchanges among the corresponding dissipation bands of such lattices. Using a one-dimensional example, we show both theoretically and experimentally that such population exchanges can manifest themselves via matrix-valued operators in the corresponding Bloch dynamics. In two-dimensional lattices, such matrix-valued operators can form non-commuting pairs and lead to non-Abelian dynamics, as confirmed by our numerical simulations. Our results point to new ways in which the combined effect of topology and engineered dissipation can lead to non-Abelian topological phenomena
Multi-Modal Mobility Morphobot (M4) with appendage repurposing for locomotion plasticity enhancement
No full textRobot designs can take many inspirations from nature, where there are many examples of highly resilient and fault-tolerant locomotion strategies to navigate complex terrains by recruiting multi-functional appendages. For example, birds such as Chukars and Hoatzins can repurpose wings for quadrupedal walking and wing-assisted incline running. These animals showcase impressive dexterity in employing the same appendages in different ways and generating multiple modes of locomotion, resulting in highly plastic locomotion traits which enable them to interact and navigate various environments and expand their habitat range. The robotic biomimicry of animals’ appendage repurposing can yield mobile robots with unparalleled capabilities. Taking inspiration from animals, we have designed a robot capable of negotiating unstructured, multi-substrate environments, including land and air, by employing its components in different ways as wheels, thrusters, and legs. This robot is called the Multi-Modal Mobility Morphobot, or M4 in short. M4 can employ its multi-functional components composed of several actuator types to (1) fly, (2) roll, (3) crawl, (4) crouch, (5) balance, (6) tumble, (7) scout, and (8) loco-manipulate. M4 can traverse steep slopes of up to 45 deg. and rough terrains with large obstacles when in balancing mode. M4 possesses onboard computers and sensors and can autonomously employ its modes to negotiate an unstructured environment. We present the design of M4 and several experiments showcasing its multi-modal capabilities
Uncovering a population of gravitational lens galaxies with magnified standard candle SN Zwicky
No full textDetecting gravitationally lensed supernovae is among the biggest challenges in astronomy. It involves a combination of two very rare phenomena: catching the transient signal of a stellar explosion in a distant galaxy and observing it through a nearly perfectly aligned foreground galaxy that deflects light towards the observer. Here we describe how high-cadence optical observations with the Zwicky Transient Facility, with its unparalleled large field of view, led to the detection of a multiply imaged type Ia supernova, SN Zwicky, also known as SN 2022qmx. Magnified nearly 25-fold, the system was found thanks to the standard candle nature of type Ia supernovae. High-spatial-resolution imaging with the Keck telescope resolved four images of the supernova with very small angular separation, corresponding to an Einstein radius of only θ_E = 0.167″ and almost identical arrival times. The small θ_E and faintness of the lensing galaxy are very unusual, highlighting the importance of supernovae to fully characterize the properties of galaxy-scale gravitational lenses, including the impact of galaxy substructures
High-speed tunable microwave-rate soliton microcomb
No full textSoliton microcombs are a promising new approach for photonic-based microwave signal synthesis. To date, however, the tuning rate has been limited in microcombs. Here, we demonstrate the first microwave-rate soliton microcomb whose repetition rate can be tuned at a high speed. By integrating an electro-optic modulation element into a lithium niobate comb microresonator, a modulation bandwidth up to 75 MHz and a continuous frequency modulation rate up to 5.0 × 10¹⁴ Hz/s are achieved, several orders-of-magnitude faster than existing microcomb technology. The device offers a significant bandwidth of up to tens of gigahertz for locking the repetition rate to an external microwave reference, enabling both direct injection locking and feedback locking to the comb resonator itself without involving external modulation. These features are especially useful for disciplining an optical voltage-controlled oscillator to a long-term reference and the demonstrated fast repetition rate control is expected to have a profound impact on all applications of frequency combs
Machine learning-assisted crystal engineering of a zeolite
No full textIt is shown that Machine Learning (ML) algorithms can usefully capture the effect of crystallization composition and conditions (inputs) on key microstructural characteristics (outputs) of faujasite type zeolites (structure types FAU, EMT, and their intergrowths), which are widely used zeolite catalysts and adsorbents. The utility of ML (in particular, Geometric Harmonics) toward learning input-output relationships of interest is demonstrated, and a comparison with Neural Networks and Gaussian Process Regression, as alternative approaches, is provided. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of high purity FAU zeolite to the hitherto highest level (i.e., Si/Al = 3.5) achieved via direct (not seeded), and organic structure-directing-agent-free synthesis from sodium aluminosilicate sols. The analysis of the ML algorithms’ results offers the insight that reduced Na2O content is key to formulating FAU materials with high Si/Al ratio. An acid catalyst prepared by partial ion exchange of the high-Si/Al-ratio FAU (Si/Al = 3.5) exhibits improved proton reactivity (as well as specific activity, per unit mass of catalyst) in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al = 2.8)
CAR-neutrophil mediated delivery of tumor-microenvironment responsive nanodrugs for glioblastoma chemo-immunotherapy
No full textGlioblastoma (GBM) is one of the most aggressive and lethal solid tumors in human. While efficacious therapeutics, such as emerging chimeric antigen receptor (CAR)-T cells and chemotherapeutics, have been developed to treat various cancers, their effectiveness in GBM treatment has been hindered largely by the blood-brain barrier and blood-brain-tumor barriers. Human neutrophils effectively cross physiological barriers and display effector immunity against pathogens but the short lifespan and resistance to genome editing of primary neutrophils have limited their broad application in immunotherapy. Here we genetically engineer human pluripotent stem cells with CRISPR/Cas9-mediated gene knock-in to express various anti-GBM CAR constructs with T-specific CD3ζ or neutrophil-specific γ-signaling domains. CAR-neutrophils with the best anti-tumor activity are produced to specifically and noninvasively deliver and release tumor microenvironment-responsive nanodrugs to target GBM without the need to induce additional inflammation at the tumor sites. This combinatory chemo-immunotherapy exhibits superior and specific anti-GBM activities, reduces off-target drug delivery and prolongs lifespan in female tumor-bearing mice. Together, this biomimetic CAR-neutrophil drug delivery system is a safe, potent and versatile platform for treating GBM and possibly other devastating diseases
Predicted Three-Dimensional Structure of the GCR1 Putative GPCR in Arabidopsis thaliana and Its Binding to Abscisic Acid and Gibberellin A1
No full textGCR1 has been proposed as a plant analogue to animal G-protein-coupled receptors that can promote or regulate several physiological processes by binding different phytohormones. For instance, abscisic acid (ABA) and gibberellin A1 (GA1) have been shown to promote or regulate germination and flowering, root elongation, dormancy, and biotic and abiotic stresses, among others. They may act through binding to GCR1, which would put GCR1 at the heart of key signaling processes of agronomic importance. Unfortunately, this GPCR function has yet to be fully validated due to the lack of an X-ray or cryo-EM 3D atomistic structure for GCR1. Here, we used the primary sequence data from Arabidopsis thaliana and the GEnSeMBLE complete sampling method to examine 13 trillion possible packings of the 7 transmembrane helical domains corresponding to GCR1 to downselect an ensemble of 25 configurations likely to be accessible to the binding of ABA or GA1. We then predicted the best binding sites and energies for both phytohormones to the best GCR1 configurations. To provide the basis for the experimental validation of our predicted ligand-GCR1 structures, we identify several mutations that should improve or weaken the interactions. Such validations could help establish the physiological role of GCR1 in plants