arXiv subjects

Xudong Fan

Publications and source records attributed to Xudong Fan.

16 recordsLinked to original sources

arXiv2025

Multi-Channel Amplitude-Phase Asymmetric-Encrypted Janus Acoustic Meta-Holograms

Encrypted optical and acoustic meta-holograms only focus on the encrypted hologram in a single channel, viz. modulating spatial amplitude to project a holographic image. In this research, the unique concept of multi-channel amplitude-phase asymmetric-encrypted Janus acoustic meta-holograms is proposed, demonstrating remarkable capabilities of generating, encrypting, and decrypting both amplitude and phase holographic images on both sides of a metascreen. The flexible and decoupled manipulation mechanism for the amplitude-phase of the bidirectional acoustic waves used in our concept offers multiple possibilities to apply various encryption methods. In this work, our system enables single-input, two-faced four-channel asymmetric encryption, which substantially increase the communication capacity of conventional acoustic holograms, and establish a security framework based on mathematical problem, proving its security. Our work can lead to concrete applications including, but not limited to, multi-channel acoustic field communications and acoustic illusion and cloaking in non-transparent media.

physics.app-ph↗

arXiv2025

Multi-Wavelength Machine Learning for High-Precision Colorimetric Sensing

Conventional colorimetric sensing methods typically rely on signal intensity at a single wavelength, often selected heuristically based on peak visual modulation. This approach overlooks the structured information embedded in full-spectrum transmission profiles, particularly in intensity-based systems where linear models may be highly effective. In this study, we experimentally demonstrate that applying a forward feature selection strategy to normalized transmission spectra, combined with linear regression and ten-fold cross-validation, yields significant improvements in predictive accuracy. Using food dye dilutions as a model system, the mean squared error was reduced from over 22,000 with a single wavelength to 3.87 using twelve selected features, corresponding to a more than 5,700-fold enhancement. These results validate that full-spectrum modeling enables precise concentration prediction without requiring changes to the sensing hardware. The approach is broadly applicable to colorimetric assays used in medical diagnostics, environmental monitoring, and industrial analysis, offering a scalable pathway to improve sensitivity and reliability in existing platforms.

physics.med-ph↗

arXiv2025

Accuracy Enhancement in Refractive Index Sensing via Full-Spectrum Machine Learning Modeling

We present a full-spectrum machine learning framework for refractive index sensing using simulated absorption spectra from meta-grating structures composed of titanium or silicon nanorods under TE and TM polarizations. Linear regression was applied to 80 principal components extracted from each spectrum, and model performance was assessed using five-fold cross-validation, simulating real-world biosensing scenarios where unknown patient samples are predicted based on standard calibration data. Titanium-based structures, dominated by broadband intensity changes, yielded the lowest mean squared errors and the highest accuracy improvements: up to a 6065-fold reduction compared to the best single-feature model. In contrast, silicon-based structures, governed by narrow resonances, showed more modest gains due to spectral nonlinearity that limits the effectiveness of global linear models. We also show that even the best single-wavelength predictor is identified through data-driven analysis, not visual selection, highlighting the value of automated feature preselection. These findings demonstrate that spectral shape plays a key role in modeling performance and that full-spectrum linear approaches are especially effective for intensity-modulated index sensors.

physics.optics↗

arXiv2025

Development of a Miniaturized, Automated, and Cost-Effective Device for Enzyme-Linked Immunosorbent Assay

In this work, a miniaturized, automated, and cost-effective ELISA device is designed and implemented, without the utilization of conventional techniques such as pipetting or microfluidic valve technologies. The device has dimensions of 24 cm x 19 cm x 14 cm and weighs <3 Kg. The total hardware cost of the device is estimated to be approximately $1,200, which can be further reduced through optimization during scale-up production. 3D printed disposable parts, including the reagent reservoir disk and the microfluidic connector, have also been developed. IL-6 is used as a model system to demonstrate how the device provides an ELISA measurement. The cost per test is estimated to be less than ten dollars. The compactness, automated operation, along with the cost-effectiveness of this ELISA device, makes it suitable for point-of-care applications in resource-limited regions.

physics.med-ph↗

arXiv2025

Generic Multimodal Spatially Graph Network for Spatially Embedded Network Representation Learning

Spatially embedded networks (SENs) represent a special type of complex graph, whose topologies are constrained by the networks' embedded spatial environments. The graph representation of such networks is thereby influenced by the embedded spatial features of both nodes and edges. Accurate network representation of the graph structure and graph features is a fundamental task for various graph-related tasks. In this study, a Generic Multimodal Spatially Graph Convolutional Network (GMu-SGCN) is developed for efficient representation of spatially embedded networks. The developed GMu-SGCN model has the ability to learn the node connection pattern via multimodal node and edge features. In order to evaluate the developed model, a river network dataset and a power network dataset have been used as test beds. The river network represents the naturally developed SENs, whereas the power network represents a man-made network. Both types of networks are heavily constrained by the spatial environments and uncertainties from nature. Comprehensive evaluation analysis shows the developed GMu-SGCN can improve accuracy of the edge existence prediction task by 37.1\% compared to a GraphSAGE model which only considers the node's position feature in a power network test bed. Our model demonstrates the importance of considering the multidimensional spatial feature for spatially embedded network representation.

cs.LG↗

arXiv2025

Sensitive bioassay with an ultra-large dynamic range via microlaser ensemble quenching

We present a bioassay platform that leverages the lasing threshold distribution in a microlaser ensemble (ME), consisting of hundreds of individual microlasers, to measure analyte concentrations in solution. An ME is formed by placing dye-doped microbeads in a micro Fabry-Perot cavity. Microbeads are surface modified with biorecognition molecules to capture analytes, while the quenchers resulting from the presence of the analytes on the microbeads' surfaces increase the lasing thresholds of microlasers. Since the number of analytes varies from one microbead (or microlaser) to another due to the randomness in binding processes, a distribution of the analytes (and hence the quenchers) in the ME is created, which in turn leads to a lasing threshold distribution in the ME. Experimentally, multiple pumping energy densities are used to probe the lasing threshold distribution. A theoretical model is developed to map the lasing threshold distribution to analyte distribution in the ME, and then to recover the analyte concentration in solution. Using streptavidin and interleukin-6 as a model system, our platform achieves a detection limit of 0.1 pg/mL and a dynamic range exceeding five orders of magnitude, showing that the ME quenching method can provide a high sensitivity with a superior dynamic range.

physics.optics↗

arXiv2024

Machine Learning-Enabled Multidimensional Data Utilization Through Multi-Resonance Architecture: A Pathway to Enhanced Accuracy in Biosensing

A novel framework is proposed that combines multi-resonance biosensors with machine learning (ML) to significantly enhance the accuracy of parameter prediction in biosensing. Unlike traditional single-resonance systems, which are limited to one-dimensional datasets, this approach leverages multi-dimensional data generated by a custom-designed nanostructure, a periodic array of silicon nanorods with a triangular cross-section over an aluminum reflector. High bulk sensitivity values are achieved for this multi-resonant structure, with certain resonant peaks reaching up to 1706 nm/RIU. The field analysis reveals Mie resonances as the physical reason behind the peaks. The predictive power of multiple resonant peaks from transverse magnetic (TM) and transverse electric (TE) polarizations is evaluated using Ridge Regression modeling. Systematic analysis reveals that incorporating multiple resonances yields up to three orders of magnitude improvement in refractive index detection precision compared to single-peak analyses. This precision enhancement is achieved without modifications to the biosensor hardware, highlighting the potential of data-centric strategies in biosensing. The findings establish a new paradigm in biosensing, demonstrating that the synergy between multi-resonance data acquisition and ML-based analysis can significantly enhance detection accuracy. This study provides a scalable pathway for advancing high-precision biosensing technologies.

q-bio.QM↗

arXiv2024

Entangled dual-comb spectroscopy

Optical frequency combs have emerged as a cornerstone for a wide range of areas, including spectroscopy, ranging, optical clocks, time and frequency transfer, waveform synthesis, and communications. However, quantum mechanical fluctuations of the optical carrier impose fundamental performance limits on the precision of traditional classical laser frequency combs, particularly in their use for interferometry and spectroscopy. Entanglement, as a quintessential quantum resource, allows for surpassing the fundamental limits of classical systems. Here, we introduce and experimentally demonstrate entangled dual-comb spectroscopy (EDCS) that surmounts the fundamental limits of classical DCS. EDCS builds on tailored entangled spectral structures of the frequency combs, enabling simultaneous detection of all comb lines below the standard quantum limit of classical DCS. Applying EDCS in gas detection, we achieve a 2.6 dB enhancement in signal-to-noise ratio and a 1.7-fold reduction in integration time over classical DCS, rendering EDCS particularly suited for dynamic chemical and biological sensing, where fast, precise measurements subject to power constraints are required. EDCS represents a new paradigm for quantum frequency combs, underscoring their prospects in a plethora of applications in precision metrology, spectroscopy, and timekeeping.

quant-ph↗

arXiv2021

Retention time trajectory matching for target compound peak identification in chromatographic analysis

Retention time drift caused by fluctuations in physical factors such as temperature ramping rate and carrier gas flow rate is ubiquitous in chromatographic measurements. Proper peak identification and alignment across different chromatograms is critical prior to any subsequent analysis. This work introduces a peak identification method called retention time trajectory (RTT) matching, which uses chromatographic retention times as the only input and identifies peaks associated with any subset of a predefined set of target compounds. RTT matching is also capable of reporting interferents. An RTT is a 2-dimensional (2D) curve formed uniquely by the retention times of the chromatographic peaks. The RTTs obtained from the chromatogram of a test sample and of pre-characterized library are matched and statistically compared. The best matched pair implies identification. Unlike most existing peak alignment methods, no mathematical warping or transformations are involved. Based on the experimentally characterized RTT, an RTT hybridization method is developed to rapidly generate more RTTs without performing actual time-consuming chromatographic measurements. This enables successful identification even for chromatograms with serious retention time drift. Experimentally obtained gas chromatograms and publicly available fruit metabolomics liquid chromatograms are used to generate over two trillions of tests that validate the proposed method, demonstrating real-time peak/interferent identification.

eess.SP↗

arXiv2018

Chromatin Laser Imaging Reveals Abnormal Nuclear Changes for Early Cancer Detection

We developed and applied rapid scanning laser-emission microscopy to detect abnormal changes in cell nuclei for early diagnosis of cancer and cancer precursors. Regulation of chromatins is essential for genetic development and normal cell functions, while abnormal nuclear changes may lead to many diseases, in particular, cancer. The capability to detect abnormal changes in apparently normal tissues at a stage earlier than tumor development is critical for cancer prevention. Here we report using LEM to analyze colonic tissues from mice at-risk for colon cancer by detecting prepolyp nuclear abnormality. By imaging the lasing emissions from chromatins, we discovered that, despite the absence of observable lesions, polyps, or tumors under stereoscope, high-fat mice exhibited significantly lower lasing thresholds than low-fat mice. The low lasing threshold is, in fact, very similar to that of adenomas and is caused by abnormal cell proliferation and chromatin deregulation that can potentially lead to cancer. Our findings suggest that conventional methods, such as colonoscopy, may be insufficient to reveal hidden or early tumors under development. We envision that this work will provide new insights into LEM for early tumor detection in clinical diagnosis and fundamental biological and biomedical research of chromatin changes at the biomolecular level of cancer development.

q-bio.TO↗

arXiv2018

Experimental Realization of Acoustic Chern Insulator

Topological insulators are new states of matter in which the topological phase originates from symmetry breaking. Recently, time-reversal invariant topological insulators were demonstrated for classical wave systems, such as acoustic systems, but limited by inter-pseudo-spin or inter-valley backscattering. This challenge can be effectively overcome via breaking the time-reversal symmetry. Here, we report the first experimental realization of acoustic topological insulators with nonzero Chern numbers, viz., acoustic Chern insulator (ACI), by introducing an angular-momentum-biased resonator array with broken Lorentz reciprocity. High Q-factor resonance is leveraged to reduce the required speed of rotation. Experimental results show that the ACI featured with a stable and uniform metafluid flow bias supports one-way nonreciprocal transport of sound at the boundaries, which is topologically immune to the defect-induced scatterings. Our work opens up opportunities for exploring unique observable topological phases and developing practical nonreciprocal devices in acoustics.

cond-mat.mes-hall↗

arXiv2017

Ultra-thin Acoustic Metasurface-Based Schroeder Diffuser

Schroeder diffuser is a classical design, proposed over 40 years ago, for artificially creating optimal and predictable sound diffuse reflection. It has been widely adopted in architectural acoustics and it has also shown substantial potential in noise control, ultrasound imaging, microparticle manipulation, among others. The conventional Schroeder diffuser, however, has a considerable thickness on the order of one wavelength, severely impeding its applications for low frequency sound. In this paper, a new class of ultra-thin and planar Schroeder diffusers are proposed based on the concept of acoustic metasurface. Both numerical and experimental results demonstrate satisfactory sound diffuse reflection produced from the metasurface-based Schroeder diffuser despite it being one order of magnitude thinner than the conventional one. The proposed design not only offer promising building blocks with great potential to profoundly impact architectural acoustics and related fields, but also constitutes a major step towards real-world applications of acoustic metasurfaces.

physics.class-ph↗

arXiv2014

Surface Sensitive Microfluidic Optomechanical Sensors

The microfluidic optomechanical resonator (uFOMR) based on a thin-walled glass capillary supports high Q-factor (>1000) mechanical modes in the presence of liquids. In this Letter, the sensitivity of the uFOMR to the surface change is studied by layer-by-layer removal of SiO2 molecules from the uFOMR inner surface using various concentrations of hydrofluoric acid solutions. A frequency downshift is observed with a sensitivity of 1.2 Hz/(pg/mm2), which translates to a surface density detection limit of 83 pg/mm2. This work opens a door to using the optomechanical mode for detection and characterization of molecules present near the resonator surface.

physics.optics↗

arXiv2014

Opto-Acoustic Biosensing with Optomechanofluidic Resonators

Opto-mechano-fluidic resonators (OMFRs) are a unique optofluidics platform that can measure the acoustic properties of fluids and bioanalytes in a fully-contained microfluidic system. By confining light in ultra-high-Q whispering gallery modes of OMFRs, optical forces such as radiation pressure and electrostriction can be used to actuate and sense structural mechanical vibrations spanning MHz to GHz frequencies. These vibrations are hybrid fluid-shell modes that entrain any bioanalyte present inside. As a result, bioanalytes can now reflect their acoustic properties on the optomechanical vibrational spectrum of the device, in addition to optical property measurements with existing optofluidics techniques. In this work, we investigate acoustic sensing capabilities of OMFRs using computational eigenfrequency analysis. We analyze the OMFR eigenfrequency sensitivity to bulk fluid-phase materials as well as nanoparticles, and propose methods to extract multiple acoustic parameters from multiple vibrational modes. The new informational degrees-of-freedom provided by such opto-acoustic measurements could lead to surprising new sensor applications in the near future.

physics.optics↗

arXiv2013

Brillouin cavity optomechanics with microfluidic devices

Cavity optomechanics allows the parametric coupling of phonon- and photon-modes in microresonators and is presently investigated in a broad variety of solid-state systems. Optomechanics with superfluids has been proposed as a path towards ultra-low optical- and mechanical-dissipation. However, there have been no optomechanics experiments reported with non-solid phases of matter. Direct liquid immersion of optomechanics experiments is challenging since the acoustic energy simply leaks out to the higher-impedance liquid surrounding the device. Conversely, here we confine liquids inside hollow resonators thereby enabling optical excitation of mechanical whispering-gallery modes at frequencies ranging from 2 MHz to 11,000 MHz (for example, with mechanical Q = 4700 at 99 MHz). Vibrations are sustained even when we increase the fluid viscosity to be higher than that of blood. Our device enables optomechanical investigation with liquids, while light is conventionally coupled from the outer dry side of the capillary, and liquids are provided by means of a standard microfluidic inlet.

physics.optics↗

arXiv2012

Cavity optomechanics on a microfluidic resonator with water and viscous liquids

Currently, optical- or mechanical-resonances are commonly used in microfluidic research. However, optomechanical oscillations by light pressure were not shown with liquids. This is because replacing the surrounding air with water inherently increases the acoustical impedance and hence the associated acoustical radiation-losses. Here, we bridge between microfluidics and optomechanics by fabricating hollow bubble resonators with liquid inside and optically exciting 100-MHz vibrations with only mW optical-input power. This constitutes the first time that any microfluidic system is optomechanically actuated. We further prove the feasibility of microfluidic optomechanics on liquids by demonstrating vibrations on organic fluids with viscous-dissipation higher than blood viscosity while measuring density changes in the liquid via the vibration frequency shift. Our device will enable using cavity optomechanics for studying non-solid phases of matter.

physics.optics↗