arXiv subjects

Xiao Shang

Publications and source records attributed to Xiao Shang.

10 recordsLinked to original sources

arXiv2026

An optical cryostat for automated testing of silicon photomultipliers with vacuum-ultraviolet light

We present the design and performance of an optical cryostat for testing silicon photomultipliers (SiPMs) with vacuum-ultraviolet (VUV) light. The cryostat operates within a temperature range of 120 to 340 K, achieving a temperature stability of 1.2 mK, measured in a rolling 10-minute window once settled within a liquid nitrogen fill-cycle, and 13.8 mK over a complete fill-cycle including the refill transient. The cryostat includes a VUV-transparent CaF$_2$ viewport with a 142 mm diameter clear aperture, which limits the optically accessible sample area to 158 cm$^2$. The viewport is integrated into a sealed dark box and optical table to facilitate light delivery. VUV illumination is achieved using a custom xenon scintillation lamp or a xenon flash lamp, with a motorized two-axis linear rail system ensuring precise light positioning over the sample. This paper details the cryostat and optical system design, as well as commissioning current-voltage (IV) measurements using Hamamatsu VUV-sensitive SiPMs from 165 to 295 K and a measurement of the photodetection efficiency at 165 K.

physics.ins-det↗

arXiv2026

Conditional grain-graph diffusion for property-guided inverse design of polycrystalline microstructures

Graph representations compactly encode polycrystalline microstructures while retaining grain topology and grain boundary information. We present a conditional graph diffusion framework for property-guided inverse design of dual-phase Ti-6Al-4V microstructures. An enhanced grain graph neural network (GNN) with grain boundary edge features, learnable node and edge embeddings, and multi-statistic pooling serves as a forward surrogate for stress prediction and candidate evaluation. The conditional diffusion model generates candidates through reverse diffusion under prescribed {\alpha}-phase volume fraction, elastic modulus, and yield-stress proxy targets. Across four target regimes and independently seeded starting sets, generated candidates consistently approach the prescribed properties, including a target outside the property envelope of the existing microstructures. Local crystallographic consistency is evaluated post-generation from deviations from the Burgers orientation relationship (BOR). BOR-aware ranking increases mean BOR consistency by up to 44.9% and 56.4% for the in- and out-of-envelope targets, respectively, while maintaining property alignment. Finite element validation of the five best candidates in each primary design case yields a maximum absolute relative error of 1.0% in their mean properties. In a representative benchmark, diffusion requires 32 candidate evaluations per input graph, compared with approximately 40,000 for random search and evolutionary optimization, and reduces runtime by approximately two orders of magnitude in the tested implementations. These results establish conditional grain-graph diffusion as an efficient framework for property-guided polycrystalline microstructure design.

cond-mat.mtrl-sci↗

arXiv2025

Polymer-inspired mechanical metamaterials

Metamaterials benefit from unique architected patterns to achieve lightweight with exceptional mechanical properties inaccessible to conventional materials. Typical mechanical metamaterials are inspired by crystal-like lattice structures, whose closely packed frameworks often exhibit a rigid mechanical nature. Here, we present polymer-inspired metamaterials (PIMs) by programming deformation and strengthening mechanisms that mimic the mechanical roles of key constituent elements in polymer networks. By combining metamaterial programmability with polymer-inspired structures, we design crosslinking, proto-crystalline order, and entanglement in PIMs to enable macroscale strengthening mechanisms inspired by crosslink, molecular-density, and pre-stretch strengthening in polymers, expanding the metamaterial structure-property design space. This macroscale polymer-inspired programmability also suggests that PIMs could serve as a design platform incorporating the programmability strategies to achieve desired deformation and strengthening responses, holding a potential for applications in soft robotic joints and compliant connectors.

physics.app-ph↗

arXiv2024

Ion manipulation from liquid Xe to vacuum: Ba-tagging for a nEXO upgrade and future $0 \nu \beta \beta$ experiments

Neutrinoless double beta decay {($0\nu\beta\beta$)} provides a way to probe physics beyond the Standard Model of particle physics. The upcoming nEXO experiment will search for $0\nu\beta\beta$ decay in $^{136}$Xe with a projected half-life sensitivity exceeding $10^{28}$ years at the 90\% confidence level using a liquid xenon (LXe) Time Projection Chamber (TPC) filled with 5 tonnes of Xe enriched to $\sim$90\% in the {$\beta \beta$}-decaying isotope $^{136}$Xe. In parallel, a potential future upgrade to nEXO is being investigated with the aim to further suppress radioactive backgrounds and to confirm $\beta \beta$-decay events. This technique, known as Ba-tagging, comprises extracting and identifying the $\beta \beta$-decay daughter $^{136}$Ba ion. One tagging approach being pursued involves extracting a small volume of LXe in the vicinity of a potential $\beta \beta$-decay using a capillary tube and facilitating a liquid-to-gas phase transition by heating the capillary exit. The Ba ion is then separated from the accompanying Xe gas using a radio-frequency (RF) carpet and RF funnel, conclusively identifying the ion as $^{136}$Ba via laser-fluorescence spectroscopy and mass spectrometry. Simultaneously, an accelerator-driven Ba ion source is being developed to validate and optimize this technique. The motivation for the project, the development of the different aspects, along with the current status and results, are discussed here.

physics.ins-det↗

arXiv2024

Accurate Inverse Process Optimization Framework in Laser Directed Energy Deposition

In additive manufacturing (AM), particularly for laser-based metal AM, process optimization is crucial to the quality of products and the efficiency of production. The identification of optimal process parameters out of a vast parameter space, however, is a daunting task. Despite advances in simulations, the process optimization for specific materials and geometries is developed through a time-consuming trial-and-error approach, which often lacks the versatility to address multiple optimization objectives. Machine learning (ML) provides a powerful tool to accelerate the optimization process, but most current studies focus on simple single-track prints, which hardly translate to manufacturing 3D components for engineering applications. In this study, we develop an Accurate Inverse process optimization framework in laser Directed Energy Deposition (AIDED), based on machine learning models and a genetic algorithm, to aid process optimization in laser DED processes. Using the AIDED, we demonstrate the following: (i) Accurately predict single-track (R2 score 0.995), multi-track (R2 score 0.969), and multi-layer (1.07% and 10.75% error in width and height, respectively) cross-sectional melt pool geometries directly from process parameters; (ii) Determine appropriate hatch spacing and layer thickness for fabricating fully dense (density > 99.9%) multi-track and multi-layer prints; (iii) Inversely identify optimal process parameters directly from customizable application objectives within 1-3 hours. We also validate the effectiveness of the AIDED experimentally by achieving two exemplary targets: fast print speed and fine print resolution. Furthermore, we show the high transferability of the framework from stainless steel to pure nickel. With AIDED, we pave a new way for ''aiding'' the process optimization in the laser-based AM processes that is applicable to a wide range of materials.

cond-mat.mtrl-sci↗

arXiv2024

Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations

The keyhole phenomenon has been widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced defects, primarily pores, dramatically affect the performance of final products, impeding the broad use of these laser-based technologies. The formation of these pores is typically associated with the dynamic behavior of the keyhole. So far, the accurate characterization and prediction of keyhole features, particularly keyhole depth, as a function of time, has been a challenging task. In situ characterization of keyhole dynamic behavior using the synchrotron X-ray technique is informative but complicated and expensive. Current simulations are generally hindered by their poor accuracy and generalization abilities in predicting keyhole depths due to the lack of accurate laser absorptance data. In this study, we develop a machine learning-aided simulation method that accurately predicts keyhole dynamics, especially in keyhole depth fluctuations, over a wide range of processing parameters. In two case studies involving titanium and aluminum alloys, we achieve keyhole depth prediction with a mean absolute percentage error of 10%, surpassing those simulated using the ray-tracing method with an error margin of 30%, while also reducing computational time. This exceptional fidelity and efficiency empower our model to serve as a cost-effective alternative to synchrotron experiments. Our machine learning-aided simulation method is affordable and readily deployable for a large variety of materials, opening new doors to eliminate or reduce defects for a wide range of laser materials processing techniques.

cond-mat.mtrl-sci↗

arXiv2023

Tailoring the mechanical properties of 3D microstructures: a deep learning and genetic algorithm inverse optimization framework

Materials-by-design has been historically challenging due to complex process-microstructure-property relations. Conventional analytical or simulation-based approaches suffer from low accuracy or long computational time and poor transferability, further limiting their applications in solving the inverse material design problem. Here, we establish a deep learning and genetic algorithm framework that integrates forward prediction and inverse exploration. This framework provides an end-to-end solution to achieve application-specific mechanical properties by microstructure optimization. In this study, we select the widely used Ti-6Al-4V to demonstrate the effectiveness of this framework by tailoring its microstructure and achieving various yield strength and elastic modulus across a large design space, while minimizing the stress concentration factor. Compared with conventional methods, our framework is efficient, versatile, and readily transferrable to other materials and properties. Paired with additive manufacturing's potential in controlling local microstructural features, our method has far-reaching potential for accelerating the development of application-specific, high-performing materials.

cond-mat.mtrl-sci↗

arXiv2018

Enhancement of steady-state bosonic squeezing and entanglement in a dissipative optomechanical system

We systematically study the influence of amplitude modulation on the steady-state bosonic squeezing and entanglement in a dissipative three-mode optomechanical system, where a vibrational mode of the membrane is coupled to the left and right cavity modes via the radiation pressure. Numerical simulation results show that the steady-state bosonic squeezing and entanglement can be significantly enhanced by periodically modulated external laser driving either or both ends of the cavity. Remarkably, the fact that as long as one periodically modulated external laser driving either end of the cavities is sufficient to enhance the squeezing and entanglement is convenient for actual experiment, whose cost is that required modulation period number for achieving system stability is more. In addition, we numerically confirm the analytical prediction for optimal modulation frequency and discuss the corresponding physical mechanism.

quant-ph↗

arXiv2018

Optically induced phonon blockade in an optomechanical system with second-order nonlinearity

Quantum control of phonons has being become a focus of attention for developing quantum technologies. Here, we propose a proposal to realize phonon blockade in a quadratically coupled optomechanical system, where a strong nonlinear interaction between photons and phonons can be induced by an external field coherently driving the cavity, and the effective coupling strength is tunable by adjusting the amplitude of the driving field. This optically induced nonlinearity is different from standard methods for realization of phonon blockade, where the nonlinearity is achieved by coupling the mechanical system to superconducting qubits. We both analytically and numerically study the phonon statistical properties via the steady-state solution of the second-order correlation function, and find phonon blockade can be efficiently realized for a large cooperativity of the system, which is achievable based on the optically enhanced nonlinear coupling and high quality mechanical system.

quant-ph↗

arXiv2017

Durable Bistable Auxetics Made of Rigid Solids

Bistable Auxetic Metamaterials (BAMs) are a class of monolithic perforated periodic structures with negative Poisson's ratio. Under tension, a BAM can expand and reach a second state of equilibrium through a globally large shape transformation that is ensured by the flexibility of its elastomeric base material. However, if made from a rigid polymer, or metal, BAM ceases to function due to the inevitable rupture of its ligaments. The goal of this work is to extend the unique functionality of the original kirigami architecture of BAM to a rigid solid base material. We use experiments and numerical simulations to assess performance, bistability and durability of rigid BAMs at 10,000 cycles. Geometric maps are presented to elucidate the role of the main descriptors of BAM architecture. The proposed design enables the realization of BAM from a large palette of materials, including elastic-perfectly plastic materials and potentially brittle materials.

physics.app-ph↗