Random Array Research Articles

Sulfur dioxide gas sensor based on vanadium oxide doped TiO2 nanopaper

Abstract The gas sensor based on TiO2 nanomaterials is promising for both industry and daily life because of its simple fabrication, low cost, high sensitivity, and easy application to micro-devices. However, its selectivity is relatively low and strongly influenced by the environment, thus limiting its practical application. In this work, we prepared TiO2 nanopaper by methods of hydrothermal synthesis and conventional paper preparation. To improve the selectivity, we added V2O5 by immersing the as-prepared nanopaper in an ammonium metavanadate (NH4VO3) solution evenly dispersed in ethanol. Nanopaper is a random array of long nanowires and nanofibers, which retains its shape and structure at high temperature, unlike pure nanowires, due to its high porosity and mechanical stability. V2O5 leads to a selective sensing performance with high catalytic activity for SO2 gas. The surface structure of the sensing material and its porosity were characterized by SEM, XRD, XPS. The improved sensing material exhibited a high response of about 22.6 for 100 ppm SO2 and a fast response and recovery of 9 s and 14 s, respectively. It also exhibited good reproducibility and selectivity in other interfering gases. Furthermore, the long-term sensing performance in the atmospheric environment was maintained for about 50 days.

Long-range enhancement for fluorescence and Raman spectroscopy using Ag nanoislands protected with column-structured silica overlayer

We demonstrate long-range enhancement of fluorescence and Raman scattering using a dense random array of Ag nanoislands (AgNIs) coated with column-structured silica (CSS) overlayer of over 100 nm thickness, namely, remote plasmonic-like enhancement (RPE). The CSS layer provides physical and chemical protection, reducing the impact between analyte molecules and metal nanostructures. RPE plates are fabricated with high productivity using sputtering and chemical immersion in gold(I)/halide solution. The RPE plate significantly enhances Raman scattering and fluorescence, even without proximity between analyte molecules and metal nanostructures. The maximum enhancement factors are 107-fold for Raman scattering and 102-fold for fluorescence. RPE is successfully applied to enhance fluorescence biosensing of intracellular signalling dynamics in HeLa cells and Raman histological imaging of oesophagus tissues. Our findings present an interesting deviation from the conventional near-field enhancement theory, as they cannot be readily explained within its framework. However, based on the phenomenological aspects we have demonstrated, the observed enhancement is likely associated with the remote resonant coupling between the localised surface plasmon of AgNIs and the molecular transition dipole of the analyte, facilitated through the CSS structure. Although further investigation is warranted to fully understand the underlying mechanisms, the RPE plate offers practical advantages, such as high productivity and biocompatibility, making it a valuable tool for biosensing and biomolecular analysis in chemistry, biology, and medicine. We anticipate that RPE will advance as a versatile analytical tool for enhanced biosensing using Raman and fluorescence analysis in various biological contexts.

Hyperspectral Analysis of Silicon Nanowires Manufactured Through Metal-Assisted Chemical Etching

Abstract This study used hyperspectral imaging to analyze localized near-field interactions between incident electromagnetic waves and silicon nanowire (SiNW) arrays manufactured through catalytic etching of Si wafers for different durations. The results revealed that the unetched upper surface area on Si wafers and reflection of incident light decreased with increasing etching time. A light reflection band peaking at approximately 880 nm was generated from arrays etched for more than 1 h. We used six separate hyperspectral images to analyze the wavelength-dependent spatial optical responses of the fabricated SiNW arrays. The images revealed hot spots of light reflection from unetched Si surfaces in the wavelength range of 470–750 nm and a resonant peak at 880 nm for a photonic crystal derived from a random SiNW array. Accordingly, hyperspectral imaging enables the assessment of localized optical responses of SiNW arrays, which can then be optimized to cater to various applications.

A biconcave-shaped random laser array for unclonable coding

Photonic barcodes with random fingerprints possess significant potential applications in anti-counterfeiting and security protection due to their unclonable characteristics. Here, a scalable coding strategy is proposed to realize the physical unclonable function (PUF) codes based on a random-distributed biconcave-cavity random laser (BCRL) array in microchannel. Its unique configuration exhibits scalable randomness induced by microcavities and the scatterers, demonstrating a PUF characteristics in lasing intensity variations with pump positions. The random intensity information is firstly used as 1st-order coding to generate the PUF codes. The CIE tristimulus values of the tricolor lasing is then established as 2nd-order coding. The obtained codes strongly rely on the relative position of the arrays, indicating excellent unclonability and high generation efficiency. This work supplies a low-cost way to generate PUF codes for security system, promoting random lasing applications in security protection.

Comprehensive analysis of charge carriers dynamics through the honeycomb structure of graphite thin films and polymer graphite with applications in cold field emission and scanning tunneling microscopy

Polymer graphite electron sources have performed satisfactorily as field emission emitters and scanning tunneling microscopy probes in the past few years. However, the emission process was characterized by limited total emission currents. This paper introduces the elemental, vibrational, electronic structure, and optical analysis of polymer graphite and glass-graphite composite field emission cathodes to study these limitations. Moreover, the field emission characteristics are studied including the changes in the potential energy barrier of the used materials and structures. Among the studied structures, the cathodes prepared from graphite thin films deposited on a micropointed glass substrate (film-GMF) showed superior performance as random field emission arrays. This includes obtaining much higher emission current values ≈ 20 times) and lower threshold voltages ≈ 1/2) compared to the results obtained from polymer graphite samples. The enhancement factor in such emitters is believed to be the three-dimensional honeycomb structure of graphite. Moreover, the study includes applying graphite coatings to tungsten nano-field emission cathodes and scanning tunneling microscopy probes, which improves the performance of such cathodes/probes in both microscopic techniques.

Metalens array for quantum random number

Quantum random number generation (QRNG) leveraging intrinsic quantum uncertainty has attracted significant interest in the field of integrated photonic architecture, with applications in quantum cryptography, tests of quantum nonlocality, and beyond. The demand for compact, low-energy consumption, robust, fast, and cost-effective QRNGs integrated into photonic chips is highlighted, whereas most previous works focused on bulk optics. Here, based on the metalens array entangled source, we experimentally realized a miniaturized, high-dimensional quantum random number generator via a meta-device without post-randomness extraction. Specifically, the device has a high-density output with 100 channels per square millimeter. This chip-scale quantum randomness source can obtain random number arrays without post-randomness extraction and enable compact integration for quantum applications needing secure keys or randomness. Our approach demonstrates potential in secure key generation and randomness for quantum applications.

Transcranial focused ultrasound modulates visual thalamus in a nonhuman primate model.

The thalamus plays a pivotal role as a neural hub, integrating and distributing visual information to cortical regions responsible for visual processing. Transcranial focused ultrasound (tFUS) has emerged as a promising non-invasive brain stimulation technology, enabling modulation of neural circuits with high spatial precision. This study investigates the tFUS neuromodulation at visual thalamus and characterizes the resultant effects on interconnected visual areas in a nonhuman primate model. Experiments were conducted on a rhesus macaque trained in a visual fixation task, combining tFUS stimulation with simultaneous scalp electroencephalography (EEG) and intracranial recordings from area V4, a region closely linked to the thalamus. Ultrasound was delivered through a 128-element random array ultrasound transducer operating at 700 kHz, with the focus steered onto the pulvinar of the thalamus based on neuroanatomical atlas and individual brain model. EEG source imaging revealed localized tFUS-induced activities in the thalamus, midbrain, and visual cortical regions. Critically, tFUS stimulation of the pulvinar can elicit robust neural responses in V4 without visual input, manifested as significant modulations in local field potentials, elevated alpha and gamma power, corroborating the functional thalamocortical connectivity. Furthermore, the tFUS neuromodulatory effects on visually-evoked V4 activities were region-specific within the thalamus and dependent on ultrasound pulse repetition frequency. This work provides direct electrophysiological evidence demonstrating the capability of tFUS in modulating the visual thalamus and its functional impact on interconnected cortical regions in a large mammalian model, paving the way for potential investigations for tFUS treating visual, sensory, and cognitive impairments.

Ag-deposited nanostructured Boehmite substrates for the detection of explosives with surface enhanced Raman spectroscopy

We propose aluminum oxide (Boehmite) sputter-deposited with Ag substrates for Surface-Enhanced Raman Spectroscopy (SERS). These substrates are cost-effective and easily fabricated by heating aluminum in an aqueous environment to create Boehmite, followed by Ag sputtering. The metal deposition is optimized, resulting in random arrays of Ag nanostructures with a diameter of ∼100 nm and a spacing of <100 nm leading to significant enhancement of the Raman signal. The performance and sensitivity of the substrates are initially tested with the use of Crystal Violet analyte which results in limits of detection close to 10−10M. These substrates are used for the rapid detection of four different explosive compounds: Nitroglycerin (NG), Picric Acid (PA), Cyclotrimethylene trinitramine (RDX) and 2,4,6-Trinitrophenylmethylnitramine (Tetryl). A series of Raman spectra are collected for these four selected explosives on the fabricated substrates and principal component analysis (PCA) was used for proper evaluation and identification of the corresponding measured spectra.

A high-resolution DOA estimation via random forest virtual array extension

In sonar detection, underwater recognition, and similar fields, the challenge of using small-scale arrays is frequently encountered. With small-scale arrays, the degrees of freedom (DOFs) and accuracy of direction of arrival (DOA) estimation decrease significantly. Moreover, existing algorithms are less robust in complex environments and typically require substantial computing resources to ensure accurate estimation. To overcome these problems, this paper proposes a high-resolution DOA estimation via Random Forest virtual array extension (RFVAE). The algorithm first trains the random forest (RF) network using real array element data, and thus proposes a virtual covariance reconstruction technique. This technique allows for a substantial increase in the number of virtual array elements. Then, based on the virtual covariance reconstruction technique, a high resolution estimation network technique is proposed, which can increase the accuracy and DOFs of DOA estimation. Experimental results show that the proposed algorithm reduces error by 30% to 80% compared to recent technologies and can provide up to twice as many virtual elements. Additionally, the estimation speed can be increased by 3 to 1000 times, and it has significantly stronger robustness, functioning normally at signal-to-noise ratios 10 dB lower than the conditions under which other algorithms fail.

Design and Fabrication of Continuous Surface Optical Field Modulator for Angular Spectrum Discreteness Compensation.

The light homogenizing element is a crucial component of the illumination system of the lithography machine. Its primary purpose is to realize the uniform distribution of energy. However, it suffers from a common issue, which is angular spectrum discreteness, which significantly impacts light uniformity. To address this, we design and fabricate random micro-cylindrical lens arrays to obtain a small-angle Gaussian optical field, which can compensate for the angular spectrum discreteness. By adjusting the pitches and curvature radii of the micro-cylindrical lenses separately, we are able to manipulate the divergence angle of the emitted sub-beams, enabling precise angular spectrum modulation. By using mask-moving technology, the angular spectrum modulator is fabricated to generate a Gaussian illumination field. The surface profile is measured and determined with a structural roughness below 10 nm. Furthermore, optical test experiments on the modulator have been conducted, achieving an angle error of less than 0.02° and a balance better than 0.5%.

Solid-state transverse Anderson localized fiber laser.

For the first time, to our knowledge, an all-solid transverse Anderson localizing optical fiber laser is demonstrated. A combination of the molten core and stack-and-draw fiber fabrication techniques is used to produce a 112 µm core diameter fiber that is a random array of Yb-doped high index and passive low index regions. A localized channel first assists in the guidance of amplified spontaneous emission before stimulating laser action, which occurs in the same channel via mixed Anderson localization and step index wave-guiding. Threshold behavior and lasing are monitored with changing output power slopes, beam profiling, spectral content, fluorescence clamping, and temporal intensity. The average output power is stable, while the laser wavelength hops between 1066 and 1088 nm. Lasing is highly directional along the fiber axis.

Reservoir computing with a random memristor crossbar array.

Physical implementations of reservoir computing (RC) based on the emerging memristors have become promising candidates of unconventional computing paradigms. Traditionally, sequential approaches by time-multiplexing volatile memristors have been prevalent because of their low hardware overhead. However, they suffer from the problem of speed degradation and fall short of capturing the spatial relationship between the time-domain inputs. Here, we explore a new avenue for RC using memristor crossbar arrays (MCAs) with device-to-device variations, which serve as physical random weight matrices of the reservoir layers, enabling faster computation thanks to the parallelism of matrix-vector multiplication as an intensive operation in RC. To achieve this new RC architecture, ultralow-current, self-selective memristors are fabricated and integrated without the need of transistors, showing greater potential of high scalability and three-dimensional integrability compared to the previous realizations. The information processing ability of our RC system is demonstrated in tasks of recognizing digit images and waveforms. This work indicates that the "nonidealities" of the emerging memristor devices and circuits are a useful source of inspiration for new computing paradigms.

Hardware Implementation of Next Generation Reservoir Computing with RRAM‐Based Hybrid Digital‐Analog System

Reservoir computing (RC) possesses a simple architecture and high energy efficiency for time‐series data analysis through machine learning algorithms. To date, RC has evolved into several innovative variants. The next generation reservoir computing (NGRC) variant, founded on nonlinear vector autoregression (NVAR) distinguishes itself due to its fewer hyperparameters and independence from physical random connection matrices, while yielding comparable results. However, NGRC networks struggle with massive Kronecker product calculations and matrix‐vector multiplications within the read out layer, leading to substantial efficiency challenges for traditional von Neumann architectures. In this work, a hybrid digital‐analog hardware system tailored for NGRC is developed. The digital part is a Kronecker product calculation unit with data filtering, which realizes transformation of nonlinear vector of the input linear vector. For matrix‐vector multiplication, a computing‐in‐memory architecture based on resistive random access memory array offers an energy‐efficient hardware solution, which markedly reduces data transfer and greatly improve computational parallelism and energy efficiency. The predictive capabilities of this hybrid NGRC system are validated through the Lorenz63 model, achieving a normalized root mean square error (NRMSE) of 0.00098 and an energy efficiency of 19.42TOPS W−1.

Controlled fabrication of upright and inverted pyramid arrays of silicon via the interaction of copper ions and oxidants in hydrofluoric acid

Recent advances in chemical etching of silicon have allowed its widespread use in the fabrication of microelectronic devices and energy converters, in which shaped silicon exhibits outstanding properties and functions. The texturing process used to fabricate light-trapping structures on silicon surfaces is an indispensable step in preparing solar cells. Here, this manuscript describes the use of a one-step wet chemical etching method with copper as a catalyst to manufacture different structures, including inverted pyramid structures and upright pyramid structures, on silicon surfaces, which can be easily controlled by varying the solution composition and etching time. Moreover, this manuscript demonstrates an exciting method for fabricating random inverted pyramid and random upright pyramid arrays of silicon. The findings provide important contributions that can help improve the fabrication methods for silicon-based solar cell devices and optimize the light trapping structure on the cell surface.

Flow past a random array of statistically homogeneously distributed stationary Platonic polyhedrons: Data analysis, Probability maps and Deep Learning models

We perform particle-resolved direct numerical simulations (PR-DNS) of the flow past a random array of stationary Platonic polyhedrons seeded in a tri-periodic box at Reynolds number Re=1, 10, and 100 and solid volume fraction 0.05, 0.1 and 0.2. Using Platonic polyhedrons enables us to examine the effects of particle angularity on the hydrodynamic force and torque exerted on the stationary particles in a controlled manner. We report the average drag, lift, and torque coefficients as well as their respective distribution. We explore the relationship between the particle angularity, particle angular position, flow disturbances created by the neighboring particles, and the hydrodynamic force and torque exerted on each individual particle. We attempt to extend our probability map based model (MPP), our Physics Informed neural network (PINN) model previously developed for spheres to Platonic polyhedrons, and propose an additional NN architecture. We show that ignoring the angular position and actual shape of the reference and neighboring particles constitutes a reasonable first order approximation that significantly simplifies the construction of deterministic models of hydrodynamic forces and torques at low Re=1 to moderate Re=10. At high Re=100, both the MPP model and the PINN model without any additional knowledge of the particle shape and angular position fail to properly predict the force and torque fluctuations. We then design a Convolutional Neural Network (CNN) architecture that is able to maintain a satisfactory performance at Re=100 but requires additional input data in the form of a coarse-grained velocity field around each particle. Overall, predicting the torque fluctuation remains a significant challenge.

Localized and extended collective optical phonon modes in regular and random arrays of contacting nanoparticles: Escape from phonon confinement

Localized and extended collective optical phonon modes in regular and random arrays of contacting nanoparticles: Escape from phonon confinement

Equilibrium Dynamics of Infinite-Range Quantum Spin Glasses in a Field

We determine the low-energy spectrum and Parisi replica-symmetry-breaking function for the spin-glass phase of the quantum Ising model with infinite-range random exchange interactions and transverse and longitudinal (h) fields. We show that, for all h, the spin-glass state has full replica symmetry breaking and the local spin spectrum is gapless, with a spectral density that vanishes linearly with frequency. These results are obtained using an action functional—argued to yield exact results at low frequencies—that expands in powers of a spin-glass order parameter, which is bilocal in time, and a matrix in replica space. We also present the exact solution of the infinite-range spherical quantum p-rotor model at nonzero h: here, the spin-glass state has one-step replica symmetry breaking and gaplessness only appears after imposition of an additional marginal stability condition. Possible connections to experiments on random arrays of trapped Rydberg atoms are noted. Published by the American Physical Society 2024

Reversible, irreversible, and mixed regimes for periodically driven disks in random obstacle arrays.

We examine an assembly of repulsive disks interacting with a random obstacle array under a periodic drive and find a transition from reversible to irreversible dynamics as a function of drive amplitude or disk density. At low densities and drives, the system rapidly forms a reversible state where the disks return to their exact positions at the end of each cycle. In contrast, at high amplitudes or high densities, the system enters an irreversible state where the disks exhibit normal diffusion. Between these two regimes, there can be an intermediate irreversible state where most of the system is reversible, but localized irreversible regions are present that are prevented from spreading through the system due to a screening effect from the obstacles. We also find states that we term "combinatorial reversible states" in which the disks return to their original positions after multiple driving cycles. In these states, individual disks exchange positions but form the same configurations during the subcycles of the larger reversible cycle.

Matte surfaces with broadband transparency enabled by highly asymmetric diffusion of white light.

The long-standing paradox between matte appearance and transparency has deprived traditional matte materials of optical transparency. Here, we present a solution to this centuries-old optical conundrum by harnessing the potential of disordered optical metasurfaces. Through the construction of a random array of meta-atoms tailored in asymmetric backgrounds, we have created transparent matte surfaces that maintain clear transparency regardless of the strength of disordered light scattering or their matte appearances. This remarkable property originates in the achievement of highly asymmetric light diffusion, exhibiting substantial diffusion in reflection and negligible diffusion in transmission across the entire visible spectrum. By fabricating macroscopic samples of such metasurfaces through industrial lithography, we have experimentally demonstrated transparent windows camouflaged as traditional matte materials, as well as transparent displays with high clarity, full color, and one-way visibility. Our work introduces an unprecedented frontier of transparent matte materials in optics, offering unprecedented opportunities and applications.

Location Error Analysis for Collaborative Beamforming in UAVs Random Array

Location Error Analysis for Collaborative Beamforming in UAVs Random Array