Design of high-performance plasmonic nanosensors by particle swarm optimization algorithm combined with machine learning

Morphological Reconstruction for Variable Wing Leading Edge Based on the Node Curvature Vectors

Precise morphology acquisition for the variable wing leading edge is essential for its bio-inspired adaptive control. Therefore, this study proposes a morphological reconstruction method for the variable wing leading edge, utilizing the node curvature vectors-based curvature propagation method (NCV-CPM). By establishing a strain-arc curvature function, the method fundamentally mitigates the impact of surface curvature angle on curvature computation accuracy at sensing points. We introduce a technique that uses high-order curvature fitting functions to determine the curvature vectors of arc segment nodes. This method reduces cumulative errors in curvature computation linked to the linear interpolation-based curvature propagation method (LI-CPM) at unattached sensor positions. Integrating curvature-strain functions aids in wing leading-edge strain field reconstruction, supporting structural health monitoring. Additionally, a particle swarm algorithm optimizes the sensing point distribution, reducing network complexity. This study demonstrates significantly enhanced morphological reconstruction accuracy compared to those obtained with conventional LI-CPM.

Convergence and Performance Analysis of a Particle Swarm Optimization Algorithm for Optical Tuning of Gold Nanohole Arrays

Gold nanohole arrays, hybrid metal/dielectric metasurfaces composed of periodically arranged air holes in a thick gold film, exhibit versatile support for both localized and propagating surface plasmons. Leveraging their capabilities, particularly in surface plasmon resonance-oriented applications, demands precise optical tuning. In this study, a customized particle swarm optimization algorithm, implemented in Ansys Lumerical FDTD, was employed to optically tune gold nanohole arrays treated as bidimensional gratings following the Bragg condition. Both square and triangular array dispositions were considered. Convergence and evolution of the particle swarm optimization algorithm were studied, and a mathematical model was developed to interpret its outcomes.

Polarization- and angle-insensitive broadband long wavelength infrared absorber based on coplanar four-sized resonators

In many potential applications, there is a high demand for long wavelength infrared (LWIR) absorbers characterized by a compact configuration, broad operational bandwidth, high absorption efficiency, and polarization- and angle-insensitive characteristics. In this study, we design and demonstrate a high-performance broadband LWIR absorber based on coplanar four-sized resonators, consisting of arrays of titanium (Ti) disks with different diameters supported by a continuous zinc selenide (ZnSe) layer and by a Ti film acting as a back-reflector. Particle swarm optimization (PSO) is employed to optimize the complicated geometry parameters, and the final optimized device exhibits near-unity absorption (∼96.7%) across the entire operational bandwidth (8 µm∼14 µm) under unpolarized normal incidence, benefiting from the impedance-matching condition and the multiple surface plasmon resonances of this configuration. Furthermore, the proposed absorber is insensitive to the angle of incidence due to the localized surface plasmon resonances supported by these four-sized resonators, and is insensitive to the state of polarization thanks to the highly symmetric feature of the circular pattern. The measured absorption of the fabricated sample exhibits a relatively high coincidence with the simulation, with an average absorption of 88.9% ranging from 8 µm to 14 µm. The proposed absorber, which can be easily integrated into a standardized micro/nano manufacture process for cost-effective large-scale production, provides a feasible solution for improving optical performance in thermal emitter, infrared detection, and imaging applications. Furthermore, the generalized design principle employing the optimized method opens up new avenues for realizing target absorption, reflection, and transmission based on more complicated structure configurations.

Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.

Intelligent design of the chiral metasurfaces for flexible targets: combining a deep neural network with a policy proximal optimization algorithm

Recently, deep reinforcement learning (DRL) for metasurface design has received increased attention for its excellent decision-making ability in complex problems. However, time-consuming numerical simulation has hindered the adoption of DRL-based design method. Here we apply the Deep learning-based virtual Environment Proximal Policy Optimization (DE-PPO) method to design the 3D chiral plasmonic metasurfaces for flexible targets and model the metasurface design process as a Markov decision process to help the training. A well trained DRL agent designs chiral metasurfaces that exhibit the optimal absolute circular dichroism value (typically, ∼ 0.4) at various target wavelengths such as 930 nm, 1000 nm, 1035 nm, and 1100 nm with great time efficiency. Besides, the training process of the PPO agent is exceptionally fast with the help of the deep neural network (DNN) auxiliary virtual environment. Also, this method changes all variable parameters of nanostructures simultaneously, reducing the size of the action vector and thus the output size of the DNN. Our proposed approach could find applications in efficient and intelligent design of nanophotonic devices.

Engineered extracellular vesicles as intelligent nanosystems for next-generation nanomedicine

Extracellular vesicles (EVs), as natural carriers of bioactive cargo, have a unique micro/nanostructure, bioactive composition, and characteristic morphology, as well as fascinating physical, chemical and biochemical features, which have shown promising application in the treatment of a wide range of diseases. However, native EVs have limitations such as lack of or inefficient cell targeting, on-demand delivery, and therapeutic feedback. Recently, EVs have been engineered to contain an intelligent core, enabling them to (i) actively target sites of disease, (ii) respond to endogenous and/or exogenous signals, and (iii) provide treatment feedback for optimal function in the host. These advances pave the way for next-generation nanomedicine and offer promise for a revolution in drug delivery. Here, we summarise recent research on intelligent EVs and discuss the use of "intelligent core" based EV systems for the treatment of disease. We provide a critique about the construction and properties of intelligent EVs, and challenges in their commercialization. We compare the therapeutic potential of intelligent EVs to traditional nanomedicine and highlight key advantages for their clinical application. Collectively, this review aims to provide a new insight into the design of next-generation EV-based theranostic platforms for disease treatment.

Tunable structural colors generated by hybrid Si3N4 and Al metasurfaces

Metasurfaces with the capability of spectrum manipulation at subwavelength can generate structural colors. However, their practical applications in dynamic displays are limited because their optical performance is immutable after the fabrication of the metasurfaces. In this study, we demonstrate a color-tunable metasurface using numerical analysis. Moreover, we select a low-refractive-index dielectric material, Si₃N₄, which leaks the electric field to its surroundings. We investigate the potencial of these metasurfaces by simulations to achieve color-tuneable devices with encrypted watermarks. This modulation of colors can be applied to encrypted watermarks, anti-counterfeiting, and dynamic displays.

Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions

Advances in plasmonic materials and devices have given rise to a variety of applications in photocatalysis, microscopy, nanophotonics, and metastructures. With the advent of computing power and artificial neural networks, the characterization and design process of plasmonic nanostructures can be significantly accelerated using machine learning as opposed to conventional FDTD simulations. The machine learning (ML) based methods can not only perform with high accuracy and return optical spectra and optimal design parameters, but also maintain a stable high computing efficiency without being affected by the structural complexity. This work reviews the prominent ML methods involved in forward simulation and inverse design of plasmonic nanomaterials, such as Convolutional Neural Networks, Generative Adversarial Networks, Genetic Algorithms and Encoder-Decoder Networks. Moreover, we acknowledge the current limitations of ML methods in the context of plasmonics and provide perspectives on future research directions.

Graphene, Carbon Nanotube and Plasmonic Nanosensors for Detection of Viral Pathogens: Opportunities for Rapid Testing in Pandemics like COVID-19

With the emergence of global pandemics such as the Black Death (Plague), 1918 influenza, smallpox, tuberculosis, HIV/AIDS, and currently the COVID-19 outbreak caused by the SARS-CoV-2 virus, there is an urgent, pressing medical need to devise methods of rapid testing and diagnostics to screen a large population of the planet. The important considerations for any such diagnostic test include: 1) high sensitivity (to maximize true positive rate of detection); 2) high specificity (to minimize false positives); 3) low cost of testing (to enable widespread adoption, even in resource-constrained settings); 4) rapid turnaround time from sample collection to test result; and 5) test assay without the need for specialized equipment. While existing testing methods for COVID-19 such as RT-PCR (real-time reverse transcriptase polymerase chain reaction) offer high sensitivity and specificity, they are quite expensive – in terms of the reagents and equipment required, the laboratory expertise needed to run and interpret the test data, and the turnaround time. In this review, we summarize the recent advances made using carbon nanotubes for sensors; as a nanotechnology-based approach for diagnostic testing of viral pathogens; to improve the performance of the detection assays with respect to sensitivity, specificity and cost. Carbon nanomaterials are an attractive platform for designing biosensors due to their scalability, tunable functionality, photostability, and unique opto-electronic properties. Two possible approaches for pathogen detection using carbon nanomaterials are discussed here: 1) optical sensing, and 2) electrochemical sensing. We explore the chemical modifications performed to add functionality to the carbon nanotubes, and the physical, optical and/or electronic considerations used for testing devices or sensors fabricated using these carbon nanomaterials. Given this progress, it is reason to be cautiously optimistic that nanosensors based on carbon nanotubes, graphene technology and plasmonic resonance effects can play an important role towards the development of accurate, cost-effective, widespread testing capacity for the world’s population, to help detect, monitor and mitigate the spread of disease outbreaks.

Application of Particle Swarm Optimization in the Design of an ICT High-Voltage Power Supply with Dummy Primary Winding

The distribution of disk output voltage is a key factor for the design of an insulated core transformer (ICT) high-voltage power supply. The development of an ICT involves the design and optimization of many parameters, which greatly affect the uniformity of disk output voltage. A new ICT structure with dummy primary windings can compensate for the disk output voltage, which aims to improve uniformity. In this work, an optimization method based on a particle swarm optimization (PSO) algorithm was used to optimize the design parameters of an ICT with dummy primary windings. It achieved an optimal uniformity of disk output voltage and load regulation. The design parameters, including the number of secondary winding turns and the compensation capacitance, were optimized based on the finite-element method (FEM) and Simulink circuit simulation. The results show that the maximum non-uniformity of the disk output voltage is reduced from 11.1% to 4.4% from no-load to a full load for a 200 kV/20 mA HUST-ICT prototype. Moreover, the load regulation is greatly reduced from 14.3% to 9.6%. The method improves the stability and reliability of the ICT high voltage power supply and greatly reduces the design time.

Efficient inverse design and spectrum prediction for nanophotonic devices based on deep recurrent neural networks

The development of nanophotonic devices has presented a revolutionary means to manipulate light at nanoscale. How to efficiently design these devices is an active area of research. Recently, artificial neural networks (ANNs) have displayed powerful ability in the inverse design of nanophotonic devices. However, there is limited research on the inverse design for modeling and learning the sequence characteristics of a spectrum. In this work, we propose a deep learning method based on an improved recurrent neural network to extract the sequence characteristics of a spectrum and achieve inverse design and spectrum prediction. A key feature of the network is that the memory or feedback loops it comprises allow it to effectively recognize time series data. In the context of nanorods hyperbolic metamaterials, we demonstrated the high consistency between the target spectrum and the predicted spectrum, and the network learned the deep physical relationship concerning the structural parameter changes reflected on the spectrum. The effectiveness of our approach is also tested by user-drawn spectra. Moreover, the proposed model is capable of predicting an unknown spectrum based on a known spectrum with only 0.32% mean relative error. The prediction model may be helpful to predict data beyond the detection limit. We propose this versatile method as an effective and accurate alternative to the application of ANNs in nanophotonics, paving way for fast and accurate design of desired devices.