Microscope System Research Articles

Variational calculus approach to Zernike polynomials with application to FCS

Zernike polynomials are a sequence of orthogonal polynomials that play a crucial role in optics and, in particular, modeling microscopy systems. Introduced by Frits Zernike in 1934, they are particularly useful in expressing wavefront aberrations and, thus, imperfections of imaging systems. However, their origin and properties are rarely discussed and proven. Here, we present a novel approach to Zernike polynomials using variational calculus and apply them to describe aberrations in fluorescence microscopy. In particular, we model the impact of various optical aberrations on the performance of one-photon and two-photon excitation fluorescence microscopy.

Fiber-integrated quantum microscopy system for cells.

Quantum entanglement serves as an essential resource across various fields, including quantum communication, quantum computing, and quantum precision measurement. Quantum microscope, as one of the significant applications in quantum precision measurement, could bring revolutionary advancements in both signal-to-noise ratio (SNR) and spatial resolution of imaging. Here, we present a quantum microscopy system that relies on a fully fiber-integrated high-performance energy-time entangled light source operating within the near-infrared II (NIR-II) window. Complemented by tailored real-time data acquisition and processing software, we successfully demonstrate the quantum imaging of a standard target, achieving a SNR of 131.51 ± 6.74 and a spatial resolution of 4.75 ± 0.27 µm. Furthermore, we showcase quantum imaging of cancer cells, unveiling the potential of quantum entanglement in biomedical applications. Our fiber-integrated quantum microscope, characterized by high imaging SNR, instantaneous image capture, and analysis capabilities, marks an important step toward the practical application in life sciences.

A portable photoacoustic microscopy and ultrasound system for rectal cancer imaging

Photoacoustic microscopy offers functional information regarding tissue vasculature while ultrasound characterizes tissue structure. Combining these two modalities provides novel clinical applications including response assessment among rectal cancer patients undergoing therapy. We have previously demonstrated the capabilities of a co-registered photoacoustic and ultrasound device in vivo, but multiple challenges limited broad adoption. In this paper, we report significant improvements in an acoustic resolution photoacoustic microscopy and ultrasound (ARPAM/US) system characterized by simulation and phantom study, focusing on resolution, optical coupling, and signal characteristics. In turn, higher in-probe optical coupling efficiency, higher signal-to-noise ratio, higher data throughput, and better stability with minimal maintenance requirements were all accomplished. We applied the system to 19 ex vivo resected colorectal cancer samples and found significantly different signals between normal, cancer, and post-treatment tumor tissues. Finally, we report initial results of the first in vivo imaging study.

High energy density picoliter-scale zinc-air microbatteries for colloidal robotics.

The recent interest in microscopic autonomous systems, including microrobots, colloidal state machines, and smart dust, has created a need for microscale energy storage and harvesting. However, macroscopic materials for energy storage have noted incompatibilities with microfabrication techniques, creating substantial challenges to realizing microscale energy systems. Here, we photolithographically patterned a microscale zinc/platinum/SU-8 system to generate the highest energy density microbattery at the picoliter (10-12 liter) scale. The device scavenges ambient or solution-dissolved oxygen for a zinc oxidation reaction, achieving an energy density ranging from 760 to 1070 watt-hours per liter at scales below 100 micrometers lateral and 2 micrometers thickness in size. The parallel nature of photolithography processes allows 10,000 devices per wafer to be released into solution as colloids with energy stored on board. Within a volume of only 2 picoliters each, these primary microbatteries can deliver open circuit voltages of 1.05 ± 0.12 volts, with total energies ranging from 5.5 ± 0.3 to 7.7 ± 1.0 microjoules and a maximum power near 2.7 nanowatts. We demonstrated that such systems can reliably power a micrometer-sized memristor circuit, providing access to nonvolatile memory. We also cycled power to drive the reversible bending of microscale bimorph actuators at 0.05 hertz for mechanical functions of colloidal robots. Additional capabilities, such as powering two distinct nanosensor types and a clock circuit, were also demonstrated. The high energy density, low volume, and simple configuration promise the mass fabrication and adoption of such picoliter zinc-air batteries for micrometer-scale, colloidal robotics with autonomous functions.

Novel Water Probe for High-Frequency Focused Transducer Applied to Scanning Acoustic Microscopy System: Simulation and Experimental Investigation.

A scanning acoustic microscopy (SAM) system is a common non-destructive instrument which is used to evaluate the material quality in scientific and industrial applications. Technically, the tested sample is immersed in water during the scanning process. Therefore, a robot arm is incorporated into the SAM system to transfer the sample for in-line inspection, which makes the system complex and increases time consumption. The main aim of this study is to develop a novel water probe for the SAM system, that is, a waterstream. During the scanning process, water was supplied using a waterstream instead of immersing the sample in the water, which leads to a simple design of an automotive SAM system and a reduction in time consumption. In addition, using a waterstream in the SAM system can avoid contamination of the sample due to immersion in water for long-time scanning. Waterstream was designed based on the measured focal length calculation of the transducer and simulated to investigate the internal flow characteristics. To validate the simulation results, the waterstream was prototyped and applied to the TSAM-400 and W-FSAM traditional and fast SAM systems to successfully image some samples such as carbon fiber-reinforced polymers, a printed circuit board, and a 6-inch wafer. These results demonstrate the design method of the water probe applied to the SAM system.

Standardization of a Novel Semi-Automatic Software for Neurite Outgrowth Measurement.

Effective live-imaging techniques are crucial to assess neuronal morphology in order to measure neurite outgrowth in real time. The proper measurement of neurite outgrowth has been a long-standing challenge over the years in the neuroscience research field. This parameter serves as a cornerstone in numerous in vitro experimental setups, ranging from dissociated cultures and organotypic cultures to cell lines. By quantifying the neurite length, it is possible to determine if a specific treatment worked or if axonal regeneration is enhanced in different experimental groups. In this study, the aim is to demonstrate the robustness and accuracy of the Incucyte Neurotrack neurite outgrowth analysis software. This semi-automatic software is available in a time-lapse microscopy system which offers several advantages over commonly used methodologies in the quantification of the neurite length in phase contrast images. The algorithm masks and quantifies several parameters in each image and returns neuronal cell metrics, including neurite length, branch points, cell-body clusters, and cell-body cluster areas. Firstly, we validated the robustness and accuracy of the software by correlating its values with those of the manual NeuronJ, a Fiji plug-in. Secondly, we used the algorithm which is able to work both on phase contrast images as well as on immunocytochemistry images. Using specific neuronal markers, we validated the feasibility of the fluorescence-based neurite outgrowth analysis on sensory neurons in vitro cultures. Additionally, this software can measure neurite length across various seeding conditions, ranging from individual cells to complex neuronal nets. In conclusion, the software provides an innovative and time-effective platform for neurite outgrowth assays, paving the way for faster and more reliable quantifications.

Hydrodynamics, anomaly inflow and bosonic effective field theory

Euler hydrodynamics of perfect fluids can be viewed as an effective bosonic field theory. In cases when the underlying microscopic system involves Dirac fermions, the quantum anomalies should be properly described. In 1+1 dimensions the action formulation of hydrodynamics at zero temperature is reconsidered and shown to be equal to standard field-theory bosonization. Furthermore, it can be derived from a topological gauge theory in one extra dimension, which identifies the fluid variables through the anomaly inflow relations. Extending this framework to 3+1 dimensions yields an effective field theory/hydrodynamics model, capable of elucidating the mixed axial-vector and axial-gravitational anomalies of Dirac fermions. This formulation provides a platform for bosonization in higher dimensions. Moreover, the connection with 4+1 dimensional topological theories suggests some generalizations of fluid dynamics involving additional degrees of freedom.

Segmenting hydrogen‐induced cracking defects in steel through scanning acoustic microscopy and deep neural networks

AbstractHydrogen‐induced cracking (HIC) presents a significant concern in industries, such as oil and gas, petrochemicals, and aerospace, where high‐strength steel is prevalently used. This phenomenon compromises the structural integrity of steel pipelines and equipment. Accurate detection and monitoring of HIC are crucial for the safety and reliability of these assets. While traditional defect detection methods are usually costly and easily affected by human physiological state, deep learning approaches offer both time and financial benefits along with high accuracy. This study focuses on employing deep learning to segment HIC in steel, utilizing the robust and state‐of‐the‐art YOLOv8‐seg architecture for defect identification and segmentation. The process involves acquiring two‐dimensional B‐scan images through a scanning acoustic microscopy (SAM) system, followed by employing the YOLOv8‐seg architecture to address the segmentation task within these images. The experimental results demonstrate the effectiveness of the YOLOv8‐seg model, achieving a mean average precision (mAP) score greater than ˜0.95. Notably, this research pioneers the development of a framework for reconstructing HIC within steel based on B‐scan image segmentation results, offering researchers and professionals a comprehensive understanding of internal defects in steel blocks. This work underscores the potential of YOLOv8‐seg architecture for accurate detection and segmentation of HIC in steel, providing a valuable tool for inspection and maintenance activities.

Columnar excitation fluorescence microscope for accurate evaluation of quantum properties of color centers in bulk materials

Color centers in wide band-gap semiconductors, which have superior quantum properties even at room temperature and atmospheric pressure, have been actively applied to quantum sensing devices. Characterizing the quantum properties of the color centers in the semiconductor materials and ensuring that these properties are uniform over a wide area are key issues for developing quantum sensing devices based on color centers. In this article, we have developed an optics design protocol optimized for evaluating the quantum properties of color centers and have used this design approach to develop a new microscopy system called columnar excitation fluorescence microscope (CEFM). The essence of this system is to maximize the amount of fluorescence detection of polarized color centers, which is achieved by large-volume and uniform laser excitation along the sample thickness with sufficient laser power density. This laser excitation technique prevents undesirable transitions to undesirable charge states and undesirable light, such as unpolarized color center fluorescence, while significantly increasing the color center fluorescence. This feature enables fast measurements with a high signal-to-noise ratio, making it possible to evaluate the spatial distribution of quantum properties across an entire mm-size sample without using a darkroom, which is difficult with typical confocal microscope systems.

The morphology of liquid CO2 hydrate films at different temperatures under saturation pressure

CO2 hydrate film formed on the surface of water droplets in liquid CO2 was studied via digital microscope system and in situ Raman spectrometer. Temperature significantly affects the initial morphology and lateral growth rate of hydrate film. In the temperature range of 273.15–279.15 K under corresponding saturation pressure of liquid CO2, the growth and development process is primarily separated into three stages, that is, rapid lateral growth, rapid thickening, and slow development. However, in the temperature range of 280.15–282.15 K, the growth and evolution of hydrated film is dominated by rapid lateral growth and slow development processes, while the rapid thickening disappears on our experimental scale, caused by the decrease in the driving force. Each of these two temperature ranges has unique characteristics and interesting discoveries during the hydrate film formation and development. For hydrate film formed at 283.15 K, the formation of hydrate is very difficult. It needs to nucleate inside the droplet with the assistance of the hydrate crystal seeds remaining in the water droplet after decomposition, so that the small hydrate crystals can grow in the water phase, and as the crystals get larger, they will eventually emerge on the droplet’s surface and produce a complete hydrate film. Combining the experimental phenomena during the growth and development of hydrate films at various temperatures, the further growth of the hydrate phase during the generation process mainly depends on the mass transfer process of water molecules through the hydrate film. This work provides valuable information on the mechanism of nucleation, growth and decomposition of liquid CO2 hydrate crystals, which is of great significance for applications in seafloor CO2 gas sequestration.

Tracking the extensive three-dimensional motion of single ions by an engineered point-spread function

Three-dimensional (3D) imaging of individual atoms is a critical tool for discovering new physical phenomena and developing new technologies in microscopic systems. However, the current single-atom-resolved 3D imaging methods are limited to static circumstances or a shallow detection range. Here, we demonstrate a generic dynamic 3D imaging method to track the extensive motion of single ions by exploiting the engineered point-spread function (PSF). We show that the image of a single ion can be engineered into a helical PSF, thus enabling single-snapshot acquisition of the position information of the ion in the trap. A preliminary application of this technique is demonstrated by recording the 3D motion trajectory of a single trapped ion and reconstructing the 3D dynamical configuration transition between the zig and zag structures of a 5-ion crystal. This work opens the path for studies on single-atom-resolved dynamics in both trapped-ion and neutral-atom systems.

Deep-learning assisted detection and quantification of (oo)cysts of Giardia and Cryptosporidium on smartphone microscopy images

The consumption of microbial-contaminated food and water is responsible for the deaths of millions of people annually. Smartphone-based microscopy systems are portable, low-cost, and more accessible alternatives for the detection of Giardia and Cryptosporidium than traditional brightfield microscopes. However, the images from smartphone microscopes are noisier and require manual cyst identification by trained technicians, usually unavailable in resource-limited settings. Automatic detection of (oo)cysts using deep-learning-based object detection could offer a solution for this limitation. We evaluate the performance of four state-of-the-art object detectors to detect (oo)cysts of Giardia and Cryptosporidium on a custom dataset that includes both smartphone and brightfield microscopic images from vegetable samples. Faster RCNN, RetinaNet, You Only Look Once (YOLOv8s), and Deformable Detection Transformer (Deformable DETR) deep-learning models were employed to explore their efficacy and limitations. Our results show that while the deep-learning models perform better with the brightfield microscopy image dataset than the smartphone microscopy image dataset, the smartphone microscopy predictions are still comparable to the prediction performance of non-experts. Also, we publicly release brightfield and smartphone microscopy datasets with the benchmark results for the detection of Giardia and Cryptosporidium, independently captured on reference (or standard lab setting) and vegetable samples. Our code and dataset are available at <a href='https://github.com/naamiinepal/smartphone_microscopy'>https://github.com/naamiinepal/smartphone_microscopy</a> and <a href='https://doi.org/10.5281/zenodo.7813183'>https://doi.org/10.5281/zenodo.7813183</a>, respectively

Dynamic quantitative phase microscopy: a single-shot approach using geometric phase interferometry

There is a significant gap in cost-effective quantitative phase microscopy (QPM) systems for studying dynamic cellular processes while maintaining accuracy for long-term cellular monitoring. Current QPM systems often rely on complex and expensive voltage-controllable components like Spatial Light Modulators or two-beam interferometry. To address this, we introduce a QPM system optimized for time-varying phase samples using azobenzene liquid crystal as a Zernike filter with a polarization-sensing camera. This system operates without input voltage or moving components, reducing complexity and cost. Optimized for gentle illumination to minimize phototoxicity, it achieves a 1 Hz frame rate for prolonged monitoring. The system demonstrated accuracy with a maximum standard deviation of ±42 nm and low noise fluctuations of ±2.5 nm. Designed for simplicity and single-shot operations, our QPM system is efficient, robust, and precisely calibrated for reliable measurements. Using inexpensive optical components, it offers an economical solution for long-term, noninvasive biological monitoring and research applications.

Development of a single photon emission microscope system with ∼200 μm spatial resolution

With a low-energy pinhole gamma camera, spatial resolution is primarily determined by the intrinsic spatial resolution of the imaging detector, the pinhole diameter, and the magnification ratio of the collimator. However, the pinhole diameter has traditionally been limited to around 1 mm due to manufacturing difficulties with tungsten. To overcome this limitation and enhance the spatial resolution of the pinhole gamma camera, we fabricated a pinhole with a diameter of 0.1 mm. This pinhole was then combined with a YAP(Ce) imaging detector to create an ultrahigh-resolution pinhole gamma camera. The imaging detector consisted of a 38 mm × 38 mm × 1 mm thick YAP(Ce) plate, optically coupled to a 2-inch square flat panel photomultiplier tube (FP-PMT) with a 1 mm thick light guide. The intrinsic spatial resolution of the YAP(Ce) imaging detector was approximately 1.4 mm FWHM for 60 keV gamma photons. The YAP(Ce) imaging detector was encased in a tungsten shield, and the 0.1 mm diameter pinhole collimator was positioned 100 mm from the imaging detector surface. With a magnification ratio of 10 at 10 mm from the pinhole center, the spatial resolution improved to approximately 0.2 mm (200 μm) at 10 mm from the pinhole center using the developed pinhole gamma camera. The system sensitivity was approximately 0.001%. High-resolution transmission images of various subjects were obtained. The developed ultrahigh-resolution pinhole gamma camera was named the “single-photon emission microscope.” This device will be valuable for applications requiring ultrahigh resolution in single-photon gamma or X-ray imaging.

Design of a catadioptric mid-wave infrared telecentric microscope with high numerical aperture and long working distance

Infrared microscopy is widely used in the field of thermal analysis. This paper proposes a design method for a catadioptric mid-wave infrared telecentric microscope system that solves the problem of traditional microscopy systems being unable to simultaneously meet high numerical aperture, long working distance and large magnification requirements. The system is composed of a microscope objective and a tubelens. The objective uses an improved Schwarzschild structure and is connected to the tubelens through an externally located pupil. The system works in the wavelength range of 3.7 μm–4.8 μm, achieves a 0.82 numerical aperture, a 15.5 mm working distance, an 8× magnification and a 2.75 μm object-side resolution. The image size of the detector is 17um, and the MTF of the microscope system at the detector cutoff frequency is greater than 0.2, which is close to the diffraction limit. Finally, tolerance analysis and athermal analysis are conducted on the system, and the results indicate that the system possesses good manufacturability and thermal stability.

Electronic correlations influence on the anomalous constitutive law between the structure and spectra of hydrogen bonds

Classical constitutive law between the structure and spectra has long been established in most physical processes and behaviors. Despite the emerging consensus, whether this law applies to microscopic systems adhering to quantum mechanics remains a fundamental and open question, due to limitations in even the most state-of-the-art experimental techniques. Here, we report an anomalous constitutive law of hydrogen bonds (X-H···Y) using a benchmark method. Notably, a donor X-H bond shortening occurred simultaneously with an unexpected redshift and enhanced intensity in IR spectra. The reason for donor X-H bond shortening is attributed to both the slowly increasing attractive force from the acceptor Y atom and the increasing short-range repulsive forces from other protons attached to the approaching acceptor Y atom. Furthermore, neither eliminating vibrational mixing nor introducing various physical effects make vibrational spectra respond to X-H bond shortening. Other structural-characterization NMR spectra provide robust evidence for this anomalous law. This anomalous law is ascribed to the interplay of electronic correlations. Our Letter thus inspires a reassessment of paradigms, which broadens the scope of microscopic behavior for finer control. Published by the American Physical Society 2024

Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks.

Hyperspectral imaging (HSI) is an emerging imaging modality for oncological applications and can improve cancer detection with digital pathology. The study aims to highlight the increased accuracy and sensitivity of detecting the margin of thyroid carcinoma in hematoxylin and eosin (H&E)-stained histological slides using HSI and data augmentation methods. Using an automated microscopic imaging system, we captured 2599 hyperspectral images from 65 H&E-stained human thyroid slides. Images were then preprocessed into 153,906 image patches of dimension . We modified the TimeSformer network architecture, which used alternating spectral attention and spatial attention layers. We implemented several data augmentation methods for HSI based on the RandAugment algorithm. We compared the performances of TimeSformer on HSI against the performances of pretrained ConvNext and pretrained vision transformers (ViT) networks on red, green, and blue (RGB) images. Finally, we applied attention unrolling techniques on the trained TimeSformer network to identify the biological features to which the network paid attention. In the testing dataset, TimeSformer achieved an accuracy of 90.87%, a weighted score of 89.79%, a sensitivity of 91.50%, and an area under the receiving operator characteristic curve (AU-ROC) score of 97.04%. Additionally, TimeSformer produced thyroid carcinoma tumor margins with an average Jaccard score of 0.76 mm. Without data augmentation, TimeSformer achieved an accuracy of 88.23%, a weighted score of 86.46%, a sensitivity of 85.53%, and an AU-ROC score of 94.94%. In comparison, the ViT network achieved an 89.98% accuracy, an 88.14% weighted score, an 84.77% sensitivity, and a 96.17% AU-ROC. Our visualization results showed that the network paid attention to biological features. The TimeSformer model trained with hyperspectral histological data consistently outperformed conventional RGB-based models, highlighting the superiority of HSI in this context. Our proposed augmentation methods improved the accuracy, the score, and the sensitivity score.

Acoustofluidic-based microscopic examination for automated and point-of-care urinalysis.

Urinalysis is a heavily used diagnostic test in clinical laboratories; however, it is chronically held back by urine sediment microscopic examination. Current instruments are bulky and expensive to be widely adopted, making microscopic examination a procedure that still relies on manual operations and requires large time and labor costs. To improve the efficacy and automation of urinalysis, this study develops an acoustofluidic-based microscopic examination system. The system utilizes the combination of acoustofluidic manipulation and a passive hydrodynamic mechanism, and thus achieves a high throughput (1000 μL min-1) and a high concentration factor (95.2 ± 2.1 fold) simultaneously, fulfilling the demands for urine examination. The concentrated urine sample is automatically dispensed into a hemocytometer chamber and the images are then analyzed using a machine learning algorithm. The whole process is completed within 3 minutes with detection accuracies of erythrocytes and leukocytes of 94.6 ± 3.5% and 95.1 ± 1.8%, respectively. The examination outcome of urine samples from 50 volunteers by this device shows a correlation coefficient of 0.96 compared to manual microscopic examination. Our system offers a promising tool for automated urine microscopic examination, thus it has potential to save a large amount of time and labor in clinical laboratories, as well as to promote point-of-care urine testing applications in and beyond hospitals.

Automatic Defect Detection Instrument for Spherical Surfaces of Optical Elements

In order to realize automatic surface defect detection for large aperture precision spherical optical elements, an automatic surface defect detection instrument has been designed. The instrument consists of a microscopic imaging system, illumination system, motion scanning system, and a software algorithm system. Firstly, a multi-angle channel illumination source and a coaxial illumination source were designed. Bright and dark field images of surface defects were captured by cooperating with an automatic zoom microscope. Then, algorithms for scanning trajectory planning, image stitching, and intelligent defect recognition were designed to achieve full-aperture surface image acquisition and defect quantification detection. The automated defect detection process of the instrument is summarized and introduced. Finally, the experimental platform was constructed, which can work well for the optical elements with a maximum diameter of 400 mm and a relative aperture R/D value of 1. It takes about 15 min to detect an optical element with a diameter of 200 mm in dark-field imaging mode. As a result, the minimum line width of scratch detectable is 2 μm and the minimum diameter of pitting detectable is 4 μm. Clearly, the instrument can realize the automatic detection of surface defects of spherical optical elements, and has the advantages of a high efficiency, stability, reliability, quantification, and data traceability.

Infinite Order Hydrodynamics: An Analytical Example.

We construct a kinetic model for matter-radiation interactions whose hydrodynamic gradient expansion can be computed analytically up to infinite order in derivatives, in the fully nonlinear regime, and for arbitrary flows. The frequency dependence of the opacity of matter is chosen to mimic the relaxation time of a self-interacting scalar field. In this way, the transient sector simulates that of a realistic quantum field theory. The gradient series is found to diverge for most flows, in agreement with previous findings. We identify, for the model at hand, the mechanism at the origin of the divergence, and we provide a successful regularization scheme. Additionally, we propose a universal qualitative framework for predicting the breakdown of the gradient expansion of an arbitrary microscopic system undergoing a given flow. This framework correctly recovers all previously known instances of gradient expansion divergence. As a new prediction, we show that the gradient expansion diverges when the energy-dependent mean free path is unbounded above.