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To create 3D arrangements of multiple materials in complex geometries, recent work within our lab has pursued the efficient and accurate modeling of nanoparticles and the assembly of micro- and nanostructures using optical tweezers. 

Lensfree holographic microscopy is a compact and cost-effective modality for imaging large fields of view with high resolution. When combined with automated image processing, it can be used for biomolecular sensing where biochemically functionalized micro- and nano-beads are used to label biomolecules of interest. Neural networks for image feature classification provide faster and more robust sensing results than traditional image processing approaches. While neural networks have been widely applied to other types of image classification problems, and even image reconstruction in lensfree holographic microscopy, it is unclear what type of network architecture performs best for the types of small object image classification problems involved in holographic-based sensors. Here, we apply a shallow convolutional neural network to this task, and thoroughly investigate how different layers and hyperparameters affect network performance. Layers include dropout, convolutional, normalization, pooling, and activation. Hyperparameters include dropout fraction, filter number and size, stride, and padding. We ultimately achieve a network accuracy of ∼83%, and find that the choice of activation layer is most important for maximizing accuracy. We hope that these results can be helpful for researchers developing neural networks for similar classification tasks. 

The recent comment on our previously published article questioned the novelty and computational efficiency of our work. Here we respond by restating the novelty and scientific value of our work as well as showing why the specific alternative methods stated in the comment are unlikely to outperform the methods we compare for metasurface applications involving high refractive index particles near high refractive index substrates. 

Many clinical procedures and biomedical research workflows rely on microscopy, including diagnosis of cancer, genetic disorders, autoimmune diseases, infections, and quantification of cell culture. Despite its widespread use, traditional image acquisition and review by trained microscopists is often lengthy and expensive, limited to large hospitals or laboratories, precluding use in point‐of‐care settings. In contrast, lensless or lensfree holographic microscopy (LHM) is inexpensive and widely deployable because it can achieve performance comparable to expensive and bulky objective‐based benchtop microscopes while relying on components that cost only a few hundred dollars or less. Lab‐on‐a‐chip integration is practical and enables LHM to be combined with single‐cell isolation, sample mixing, and in‐incubator imaging. Additionally, many manual tasks in conventional microscopy are instead computational in LHM, including image focusing, stitching, and classification. Furthermore, LHM offers a field of view hundreds of times greater than that of conventional microscopy without sacrificing resolution. Here, the basic LHM principles are summarized, as well as recent advances in artificial intelligence integration and enhanced resolution. How LHM is applied to the above clinical and biomedical applications is discussed in detail. Finally, emerging clinical applications, high‐impact areas for future research, and some current challenges facing widespread adoption are identified. 

Metasurface design tends to be tedious and time-consuming based on sweeping geometric parameters. Common numerical simulation techniques are slow for large areas, ultra-fine grids, and/or three-dimensional simulations. Simulation time can be reduced by combining the principle of the discrete dipole approximation (DDA) with analytical solutions for light scattered by a dipole near a flat surface. The DDA has rarely been used in metasurface design, and comprehensive benchmarking comparisons are lacking. Here, we compare the accuracy and speed of three DDA methods—substrate discretization, two-dimensional Cartesian Green’s functions, and one-dimensional (1D) cylindrical Green’s functions—against the finite difference time domain (FDTD) method. We find that the 1D cylindrical approach performs best. For example, the s-polarized field scattered from a silica-substrate-supported 600 × 180 × 60 nm gold elliptic nanocylinder discretized into 642 dipoles is computed with 0.78 % pattern error and 6.54 % net power error within 294 s, which is 6 times faster than FDTD. Our 1D cylindrical approach takes advantage of parallel processing and also gives transmitted field solutions, which, to the best of our knowledge, is not found in existing tools. We also examine the differences among four polarizability models: Clausius–Mossotti, radiation reaction, lattice dispersion relation, and digitized Green’s function, finding that the radiation reaction dipole model performs best in terms of pattern error, while the digitized Green’s function has the lowest power error. 

Fluorescence and, more generally, photoluminescence enable high contrast imaging of targeted regions of interest through the use of photoluminescent probes with high specificity for different targets. Fluorescence can be used for rare cell imaging; however, this often requires a high space-bandwidth product: simultaneous high resolution and large field of view. With bulky traditional microscopes, high space-bandwidth product images require time-consuming mechanical scanning and stitching. Lensfree imaging can compactly and cost-effectively achieve a high space-bandwidth product in a single image through computational reconstruction of images from diffraction patterns recorded over the full field of view of standard image sensors. Many methods of lensfree photoluminescent imaging exist, where the excitation light is filtered before the image sensor, often by placing spectral filters between the sample and sensor. However, the sample-to-sensor distance is one of the limiting factors on resolution in lensfree systems and so more competitive performance can be obtained if this distance is reduced. Here, we show a time-gated lensfree photoluminescent imaging system that can achieve a resolution of 8.77 µm. We use europium chelate fluorophores because of their long lifetime (642 µs) and trigger camera exposure ∼50 µs after excitation. Because the excitation light is filtered temporally, there is no need for physical filters, enabling reduced sample-to-sensor distances and higher resolutions. 

The persistence of the global COVID-19 pandemic caused by the SARS-CoV-2 virus has continued to emphasize the need for point-of-care (POC) diagnostic tests for viral diagnosis. The most widely used tests, lateral flow assays used in rapid antigen tests, and reverse-transcriptase real-time polymerase chain reaction (RT-PCR), have been instrumental in mitigating the impact of new waves of the pandemic, but fail to provide both sensitive and rapid readout to patients. Here, we present a portable lens-free imaging system coupled with a particle agglutination assay as a novel biosensor for SARS-CoV-2. This sensor images and quantifies individual microbeads undergoing agglutination through a combination of computational imaging and deep learning as a way to detect levels of SARS-CoV-2 in a complex sample. SARS-CoV-2 pseudovirus in solution is incubated with acetyl cholinesterase 2 (ACE2)-functionalized microbeads then loaded into an inexpensive imaging chip. The sample is imaged in a portable in-line lens-free holographic microscope and an image is reconstructed from a pixel superresolved hologram. Images are analyzed by a deep-learning algorithm that distinguishes microbead agglutination from cell debris and viral particle aggregates, and agglutination is quantified based on the network output. We propose an assay procedure using two images which results in the accurate determination of viral concentrations greater than the limit of detection (LOD) of 1.27 × 10 3 copies per mL, with a tested dynamic range of 3 orders of magnitude, without yet reaching the upper limit. This biosensor can be used for fast SARS-CoV-2 diagnosis in low-resource POC settings and has the potential to mitigate the spread of future waves of the pandemic. 

Lens-free microscopes can utilize holographic reconstruction techniques to recover the image of an object from the digitally recorded superposition of an unperturbed plane wave and a wave scattered by the object. Image reconstruction most commonly relies on the scalar angular spectrum method (ASM). While fast, the scalar ASM can be inaccurate for nanoscale objects, either because of the scalar approximation, or more generally, because it only models field propagation and not light-matter interaction, including inter-particle coupling. Here we evaluate the accuracy of the scalar ASM when combined with three different light-matter interaction models for computing the far-field light scattered by random arrays of gold and polystyrene nanoparticles. Among the three models—a dipole-matched transmission model, an optical path length model, and a binary amplitude model—we find that which model is most accurate depends on the nanoparticle material and packing density. For polystyrene particles at any packing density, there is always at least one model with error below 20%, while for gold nanoparticles with 40% or 50% surface coverage, there are no models that can provide errors better than 30%. The ASM error is determined in comparison to a discrete dipole approximation model, which is more computationally efficient than other full-wave modeling techniques. The knowledge of when and how the ASM fails can serve as a first step toward improved resolution in lens-free reconstruction and can also be applied to other random nanoparticle array applications such as lens-based super-resolution imaging, sub-diffraction beam focusing, and biomolecular sensing. 

Abstract The fabrication of three-dimensional (3D) microscale structures is critical for many applications, including strong and lightweight material development, medical device fabrication, microrobotics, and photonic applications. While 3D microfabrication has seen progress over the past decades, complex multicomponent integration with small or hierarchical feature sizes is still a challenge. In this study, an optical positioning and linking (OPAL) platform based on optical tweezers is used to precisely fabricate 3D microstructures from two types of micron-scale building blocks linked by biochemical interactions. A computer-controlled interface with rapid on-the-fly automated recalibration routines maintains accuracy even after placing many building blocks. OPAL achieves a 60-nm positional accuracy by optimizing the molecular functionalization and laser power. A two-component structure consisting of 448 1-µm building blocks is assembled, representing the largest number of building blocks used to date in 3D optical tweezer microassembly. Although optical tweezers have previously been used for microfabrication, those results were generally restricted to single-material structures composed of a relatively small number of larger-sized building blocks, with little discussion of critical process parameters. It is anticipated that OPAL will enable the assembly, augmentation, and repair of microstructures composed of specialty micro/nanomaterial building blocks to be used in new photonic, microfluidic, and biomedical devices. 

Abstract Three-dimensional structure fabrication using discrete building blocks provides a versatile pathway for the creation of complex nanophotonic devices. The processing of individual components can generally support high-resolution, multiple-material, and variegated structures that are not achievable in a single step using top-down or hybrid methods. In addition, these methods are additive in nature, using minimal reagent quantities and producing little to no material waste. In this article, we review the most promising technologies that build structures using the placement of discrete components, focusing on laser-induced transfer, light-directed assembly, and inkjet printing. We discuss the underlying principles and most recent advances for each technique, as well as existing and future applications. These methods serve as adaptable platforms for the next generation of functional three-dimensional nanophotonic structures.