Optical Microscopy Research Articles

Optical widefield nuclear magnetic resonance microscopy

Microscopy enables detailed visualization and understanding of minute structures or processes. While cameras have significantly advanced optical, infrared, and electron microscopy, imaging nuclear magnetic resonance (NMR) signals on a camera has remained elusive. Here, we employ nitrogen-vacancy centers in diamond as a quantum sensor, which converts NMR signals into optical signals that are subsequently captured by a high-speed camera. Unlike traditional magnetic resonance imaging, our method records the NMR signal over a wide field of view in real space. We demonstrate that our optical widefield NMR microscopy can image NMR signals in microfluidic structures with a ~10 μm resolution across a ~235 × 150 μm2 area. Crucially, each camera pixel records an NMR spectrum providing multicomponent information about the signal’s amplitude, phase, local magnetic field strengths, and gradients. The fusion of optical microscopy and NMR techniques enables multifaceted imaging applications in the physical and life sciences.

Enabling Hours-Long Drift Correction with Nanometer Resolution in Optical Microscopy through Reflection Interferometry of Fiducial Beads.

Optical microscopy excels at revealing the dynamics of biological systems in real time. However, focal drift limits most microscopy approaches to only several minutes in the absence of drift-mitigation strategies. Here we introduce focus readjustment for enhanced vertical resolution (FREVR), a method which uses micrometer-sized fiducial beads alongside the specimen. By tracking the interference fringes generated by these beads, FREVR can detect changes in focus position with nanometer precision. We showcase this approach on a microscope equipped with a high-speed CMOS camera used for bead tracking and a highly sensitive EMCCD camera tasked to quantify fluorescence with spectral resolution. Using this approach, we measured samples in focus for several hours on several single-molecule systems, as well as on mammalian cells having their actin filaments or membrane proteins fluorescently labeled. FREVR has the potential to dramatically increase the temporal and spatial resolution and the long-term stability of microscopy techniques.

Unequal {110} Facets: The Potential Role of Intraparticle Heterogeneity and Facet Termination in Photoelectrochemical Activity of Single BiVO4 Particles.

BiVO4 photoanodes are promising for solar water splitting, with photogenerated electrons and holes preferentially reacting at top {010} and lateral {110} facets, respectively. However, the mechanisms driving this facet-dependent reactivity remain unclear. Here, we investigate facet-dependent photocurrent and material heterogeneity using correlative scanning photoelectrochemical microscopy (SPCM), electron beam induced current (EBIC) mapping, and mid-IR scattering scanning near-field optical microscopy (s-SNOM). SPCM measurements of 62 BiVO4 particles confirmed higher photocurrents at lateral {110} facets compared to top {010} facets, but unexpectedly revealed variations in photocurrent among lateral facets within the same particle. Variations in lateral facet surface termination could explain the intraparticle-level reactivity heterogeneity, consistent with theoretical predictions. Nano-FTIR spectroscopy and Raman microspectroscopy indicated significant materials chemistry heterogeneity within individual particles and facets that could be attributed to variations in lattice vibration distortions that enhance the overlap between Bi 6s and O 2p orbitals. The increased orbital overlap is significant as it potentially increases hole mobility in the valence band and potentially explains the lateral facet-dependent charge separation efficiency observed in photocurrent maps. Facet-dependent electrical and EBIC measurements showed no space charge regions at interfacet junctions or metal-BiVO4 contacts under vacuum, suggesting that photogenerated holes beneath top {010} facets are unlikely to transport to lateral {110} facets to drive water/sulfite oxidation. These findings indicate the potential influence of distinct bulk properties and surface termination chemistries across different particles and facets, highlighting the importance of carefully controlling defects and surface chemistry during sample growth to optimize photocatalytic performance.

Data-driven signal-to-noise enhancement in scattering near-field infrared microscopy.

This study introduces a machine-learning approach to enhance signal-to-noise ratios in scattering-type scanning near-field optical microscopy (s-SNOM). While s-SNOM offers a high spatial resolution, its effectiveness is often hindered by low signal levels, particularly in weakly absorbing samples. To address these challenges, we utilize a data-driven "patch-based" machine learning reconstruction method, incorporating modern generative adversarial neural networks (CycleGANs) for denoising s-SNOM images. This method allows for flexible reconstruction of images of arbitrary sizes, a critical capability given the variable nature of scanned sample areas in point-scanning probe-based microscopies. The CycleGAN model is trained on unpaired sets of images captured at both rapid and extended acquisition times, thereby modeling instrument noise while preserving essential topographical and molecular information. The results show significant improvements in image quality, as indicated by higher structural similarity index and peak signal-to-noise ratio values, comparable to those obtained from images captured with four times the integration time. This method not only enhances image quality but also has the potential to reduce the overall data acquisition time, making high-resolution s-SNOM imaging more feasible for a wide range of biological and materials science applications.

Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning

The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigated label-free multimodal nonlinear optical microscopy for non-perturbative profiling of biopharmaceutical cell lines based on their intrinsic molecular contrast. Employing simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy with fluorescence lifetime imaging microscopy (FLIM), we characterized Chinese hamster ovary (CHO) cell lines at early passages (0–2). A machine learning (ML)-assisted analysis pipeline leveraged high-dimensional information to classify single cells into their respective lines. Remarkably, the monoclonal cell line classifiers achieved balanced accuracies exceeding 96.8% as early as passage 2. Correlation features and FLIM modality played pivotal roles in early classification. This integrated optical bioimaging and machine learning approach presents a promising solution to expedite cell line selection process while ensuring identification of high-performing biopharmaceutical cell lines. The techniques have potential for broader single-cell characterization applications in stem cell research, immunology, cancer biology and beyond.

Nanoscopic structural and optical investigations on blue InGaN single quantum wells serving as layers beneath efficient red active layers

In state-of-the-art red InGaN light-emitting diodes (LEDs), an InGaN-based blue single quantum well (SQW) is used as an underlying layer to improve the red emission efficiency. However, the role of blue SQW is not fully understood. This study investigates the structural and optical properties of blue InGaN SQW by atomic force microscopy (AFM) and photoluminescence (PL) spectroscopy under scanning near-field optical microscopy (SNOM). The AFM images reveal deep and shallow V-pits, corresponding to screw and mixed threading dislocations (TDs). The SNOM-PL intensity image illustrates that all the V-pits correspond to dark spots. We also observe dark spots not associated with the V-pits; given the correspondence between the dark spot density and the edge TD density estimated via x-ray diffraction measurement, these dark spots likely originate from edge TDs. Unlike previous studies, we find that TDs act as nonradiative recombination centers (NRCs) in recent blue SQW, most likely because of the reduction of point defects by InGaN/GaN superlattices. Edge, screw, and mixed TDs have nearly the same impact on the integrated PL intensity of the blue InGaN SQW. Given the correlation of dark emission positions between the blue and red emissions in hybrid red InGaN LEDs in our prior study, all the TDs should function as NRCs in the red emission. The comparable dark spot density between the blue and red SQW suggests that blue SQW suppresses the generation of NRCs in red SQW.

Wearable Body Temperature Sensing with Autonomous Self‐regulated Joule Heating and Passive Cooling for Healthcare Applications

AbstractThe positive temperature coefficient (PTC) effect observed in conductive polymer composites (CPCs) holds significant promise due to its wide materials selection and ability to offer enhanced sensitivity. However, traditional CPCs have relatively high PTC switching temperatures (typically above 100 °C) and are often unsuitable for bodily healthcare devices. This study introduces a novel approach leveraging the synergistic effect of an eco‐friendly fatty acid, namely lauric acid (LA), with flexible styrene‐ethylene‐butylene‐styrene (SEBS) thermoplastic elastomer (TPE) as a matrix and graphene nanoplatelets (GNPs) as a conductive filler. The composite film demonstrates exceptional temperature responsiveness at body‐relevant temperatures (35–40 °C) with a PTC intensity reaching an unprecedented 4 orders of magnitude, set apart by its fine‐tuning ability across a remarkable detecting temperature interval (Maximum temperature coefficient of resistance (TCR): 471.4% °C−1). This advancement is facilitated through a carefully engineered morphology, wherein the distribution of LA significantly influences the conductive network's reformation within the composite, with the in‐situ optical microscope used to reveal the reformation of the conductive network structure. The flexible composite demonstrates significant potential for body temperature sensing, self‐regulating heating, and passive cooling, paving the way for future developments in eco‐friendly, highly sensitive, and flexible sensors in wearable health monitoring and thermotherapy.

WxMo1‐xO2 Nanocrystals Synthesized by WSe2‐Assisted Chemical Vapor Deposition Method

Transition metal oxide alloys, with tunable stoichiometric ratio, have garnered significant attention due to their unique electronic structure and diverse chemical properties. However, synthesis of micron‐scale, single‐crystalline transition metal oxide alloys remains a challenge due to their unique alloy structure and stringent synthesis conditions. Herein, a WSe2‐assisted chemical vapor deposition method is reported for synthesizing WxMo1‐xO2 nanocrystals with various dimensions: nanorods, nanoplates, and nanodots. The microscopic (optical microscope, atomic force microscope, and scanning electron microscope) images reveal that the nanocrystals have well‐defined non‐layered polyhedral structures. The elemental composition is confirmed by Raman spectroscopy and energy dispersion spectroscopy. The change in phonon symmetry in nanorods and the blue shift of Raman peaks in nanocrystals demonstrate the significant role of the quantum confinement effect. The spatial distribution of the various dimensional nanocrystals provides insights into the possible growth mechanism and the key role of the WSe2 in growth. The WSe2‐assisted growth strategy for alloyed nanocrystals can expand the transition metal oxide alloy material library and offer a platform for exploring additional properties of transition metal oxide alloys.

MicroStabilize: In-plane microstructure stabilization in optical microscopy via normalized correlation coefficient matching method

microStabilize: In-plane microstructure stabilization in optical microscopy via normalized correlation coefficient matching method

Deep learning enabled rapid classification of yeast species in food by imaging of yeast microcolonies.

Diverse species of yeasts are commonly associated with food and food production environments. The contamination of food products by spoilage yeasts poses significant challenges, leading to quality degradation and food loss. Similarly, the introduction of undesirable strains during fermentation can cause considerable challenges with the quality and progress of the fermentation process. Conventional detection methods require the isolation of visible yeast colonies for genetic or biochemical characterization, which takes 5-7days and demands significant labor. This study presents a deep learning-based yeast classification approach that combines conventional cultivation methods, white light optical microscopy of microcolony, and deep learning techniques for rapidly detecting and classifying yeasts. Utilizing deep convolutional neural networks, the model accurately discriminates 7 different yeasts within 6h, achieving a mPrecision of 96.0% and a mRecall of 96.3%. Synthetic image dataset generated by generative adversarial networks (GAN) model further improved the model performance for Debaryomyces hansenii and Wickerhamomyces anomalus, yeast species with lower initial classification performance. With the addition of synthetic images in the training process, Precision for W. anomalus and Recall for D. hansenii increased by 7.7% and 5.6%, respectively. The yeast classification model was validated in the presence of microscopic food debris using tomato and tomato juice as representative examples of fresh produce and processed juice. The model maintained high classification accuracy in the presence of food debris (mPrecision and mRecall >93.9%). Overall, this methodology significantly accelerates the detection and classification of yeast species using conventional cultivation and simple white light microscopy in combination with deep learning. The simplicity, including low cost of the experimental approaches and the robustness of the deep learning model make it a highly applicable approach for routine yeast monitoring and yeast spoilage control in the food industry.

Liquid crystal sensor for Cr(III)-citrate detection via interfacial coagulation.

Trivalent chromium (Cr(III)) and its highly soluble carboxyl complexes, often discharged into the environment by industries such as electroplating, leather tanning, and textile manufacturing, present severe risks to human health and ecosystems due to their high toxicity. These compounds are notoriously difficult to detect and remove during wastewater treatment, as they can persist in aqueous environments. Consequently, there is a pressing need for the development of simple, cost-effective, and reliable methods for their detection, which can improve monitoring, facilitate timely interventions, and enhance environmental protection efforts. In this study, we developed a liquid crystal (LC)-based sensor for detecting Cr(III)-citrate in aqueous environments. The sensor utilizes the amphiphilic ligand tributylhexadecylphosphonium bromide (THPB), which is strategically doped into the LC matrix. When subjected to polarized optical microscopy, the THPB-doped LC displayed a transition from a dark to a bright optical state specifically in the presence of Cr(III)-citrate, demonstrating high selectivity over other metal ions, anions, chelating groups and metal-citrate complexes. Comprehensive analyses at both bulk and molecular levels demonstrated that this notable optical transition is facilitated by strong electrostatic interactions between THPB and Cr(III)-citrate, resulting in interfacial coagulation at the LC/aqueous interface. Impressively, the sensor achieved a detection limit as low as 5μM for Cr(III)-citrate in water, significantly below the regulatory limits set for industrial discharge. This work addresses the urgent need for simple, effective analytical techniques to detect and monitor Cr(III)-citrate in aqueous environments without relying on complex instrumentation, making it ideal for a promising tool for on-site environmental monitoring and assessment. It also highlights the broader potential of LC-based sensors for efficient environmental monitoring of other metal ion complexes.

Interaction of biomimetic lipid membranes with detergents with different physicochemical characteristics.

Membrane solubilization by detergents is routinely performed to separate membrane components, and to extract and purify membrane proteins. This process depends both on the characteristics of the detergent and properties of the membrane. Here we investigate the interaction of eight detergents with very distinct physicochemical features with model membranes in different biologically relevant phases. The detergents chosen were the non-ionic Triton X-100, Triton X-165, C10E5, octyl glucopyranoside (OG) and dodecyl maltoside (DDM) and the ionic sodium dodecyl sulfate (SDS), cetyl trimethyl ammonium bromide (CTAB) and Chaps. Three lipid compositions were explored: pure palmitoyl oleoyl phosphatidylcholine (POPC), in the liquid-disordered (Ld) phase, sphingomyelin (SM)/cholesterol 7:3 (chol) in the liquid-ordered (Lo) phase and the biomimetic POPC/SM/chol 2:1:2, which might exhibit Lo/Ld phase separation. Turbidity measurements of small liposomes were performed along the titration with the detergents to obtain the overall solubilization profiles and optical microscopy of giant unilamellar vesicles (GUVs) was used to reveal the mechanism of interaction of the detergents. The presence of cholesterol renders the membranes partly/fully insoluble in all detergents, and the charged detergents are the least effective to solubilize POPC. The non-ionic detergents, with exception of DDM, with the bulkiest headgroup, caused a substantial increase in surface area of POPC, which was quantified directly on single GUVs. The other detergents induced mainly vesicle burst. Detergents that caused some increase in area induced Lo/Ld phase separation in the ternary mixture, with preferential solubilization of the latter. The insoluble area fraction left intact was quantified. Overall, the non-ionic detergents were the most effective in solubilizing lipid membranes.

Sculpting isolated optical vortex knots on demand

The rapid development of optical technologies, including optical trapping, enhanced imaging, and microscopy, necessitates fundamentally new approaches to higher-dimensional optical beam shaping. We introduce a rigorous theoretical approach for sculpting three-dimensional, topological particle-like objects, such as optical knots or links, including precise control of their individual parts. Universally applicable to knots created using braided zero lines, our method is validated through theoretical analysis and experimental measurements. The proposed approach enables new degrees of freedom in multi-dimensional singularities shaping, including rotations, shifts, and rescaling of their parts for enhanced stability in complex media. These results may find applications in the fields of three-dimensional optical trapping, manipulation, and subwavelength microscopy, as well as probing and imaging through atmospheric or underwater turbulence.

Material Characterization and Technological Properties of Biocompatible Ti-12Al-42Nb Spherical Powder Alloy for Additive Manufacturing of Personal Medical Implants

The paper focuses on material characterization and technology properties of a new Ti-12Al-42Nb spherical powder alloy for additive manufacturing of personal medical implants. The electrode induction melting inert gas atomization (EIGA) method was used to produce the powder alloy. The powder sphericity coefficient (PSC) was 1.02. Image J software was used to calculate the spherical degree by processing images sets from scanning electron microscopy (SEM) and optical microscopy (OM). SEM of particles cross-sections indicated internal thermal-induced porosity (TIP) with a 2.3 μm pore diameter. Particle size distribution was in the range from 15.72 μm (d10) to 64.48 μm (d100) as measured by laser particle analyzer. It was indicated that flowability and powder bulk density were 196 sec and 2.79 g/cm3, respectively. XRD analysis confirmed the beta phase of the powder alloy with no additional phases. X-ray fluorescence spectrometry confirmed the alloyed composition. Reducing and oxidative melting methods of analysis showed a slight amount of impurities: oxygen (0.0087 wt.%), nitrogen (0.03 wt.%), hydrogen (0.0012 wt.%), sulfur (0.0016 wt.%), and carbon (0.022 wt.%). Simultaneous thermal analysis (STA) was performed to indicate weight growth and losses and thermal effects in argon, nitrogen, and air as well as the oxidation of Al2O3, TiO2, and Nb2O5 on the surface layer of Ti-12Al-42Nb powder alloy particles. Different phase transformations of γAl2O3 θAl2O3 αAl2O3 and TiO2 rutile TiO2 anatase phase transformation were detected by STA in the oxidative layer.

Technological and Functional Potentialities of Mairá (Casimirella rupestris) and Ariá (Goeppertia allouia) Starches.

The Amazon region holds untapped potential with its starch-rich tubers, which are not yet industrialized and face a risk of extinction due to competition from widely cultivated crops. Beyond their traditional subsistence use, Amazonian tubers such as Mairá and Ariá can be utilized as starch sources, offering an opportunity to support regional agriculture, preserve indigenous heritage, and provide sustainable income streams. This study aimed to characterize starches extracted from Mairá (MPS) and Ariá (ARS for rhizome and APS for potato), focusing on their technological and functional potential. Following extraction (maceration, filtration, decantation, and drying), their structure and size were analyzed using SEM and optical microscopy. Amylose content was determined spectrophotometrically, while pasting and thermal properties were assessed using RVA and DSC techniques. They exhibited axial diameters of 18.9, 22.6, and 26.7μm, with MPS, ARS, and APS displaying spherical, elliptical, and polygonal shapes, respectively. According to RVA results, MPS showed lower viscosity and paste stability compared to others, making it more suitable for products requiring minimal thickening. Ariá starches demonstrated a rapid retrogradation and the formation of opaque pastes, indicating potential applications in products requiring these characteristics. Starches from these Amazonian tubers present significant potential as thickening and gelling agents for the food industry, standing out for their safety in consumption and their role in preserving endangered species.

Research on Authenticity Identification and Physicochemical Properties of Various Commercially Available Lotus Rhizome Starch

ABSTRACTLotus rhizome starch (LRS) is rich in nutrients and high price, yet the yield of LRS is relatively low compared with other source starch. Thus, unscrupulous merchants often adulterate LRS with other inexpensive starches, like potato starch. This study identified the authenticity of 10 commercially available LRS samples using optical microscopy and studied their physicochemical properties, including retrogradation and gelatinization properties. The results showed that there were three kinds of genuine LRS in samples, and the amylose content in adulterated LRS samples was higher than that in pure LRS, indicating that the amylose content of the starches used for adulteration was higher than that of LRS. Adulterated LRS were more prone to retrograde, exhibited a higher the proportion of sedimentation volume than genuine LRS. Besides, adulterated LRS had a higher gelatinization temperature and poorer freeze‐thaw stability compared to pure LRS. In summary, there were significant differences in quality between adulterated LRS products and pure LRS available on the market. These performance differences can explore the impact of the incorporation of other starches on the physicochemical properties and edibility of genuine LRS, and provide certain reference value for LRS adulteration detection technology and sustainable development of LRS industry.

Optical Microscopy and Deep Learning for Absolute Quantification of Nanoparticles on a Macroscopic Scale and Estimating Their Number Concentration.

We present a simplistic and absolute method for estimating the number concentration of nanoparticles. Macroscopic volumes of a nanoparticle dispersion (several μL) are dropped on a glass surface and the solvent is evaporated. The optical microscope scans the entire surface of the dried droplet (several mm2), micrographs are stitched together (several tens), and all nanoparticles are counted (several thousand per droplet) by using an artificial neural network. We call this method evaporated volume analysis (EVA) because nanoparticles are counted after droplet volume evaporation. As a model, the concentration of ∼60 nm Tm3+-doped photon-upconversion nanoparticles coated in carboxylated silica shells is estimated with a combined relative standard uncertainty of 2.7%. Two reference methods provided comparable concentration values. A wider applicability is tested by imaging ∼80 nm Nile red-doped polystyrene and ∼90 nm silver nanoparticles. Theoretical limits of EVA such as the limit of detection, limit of quantification, and optimal working range are discussed.

Liquid crystalline self-assembly of mixtures of rod- and wedge-shaped MIDA boronates.

Rod-like MIDA boronates form smectic mesophases, while wedge-shaped MIDA boronates self-assemble into columnar mesophases. However, the phase behavior of mixtures is less understood. In order to obtain further insight on the molecular self-assembly of MIDA boronate mixtures two series of binary mixtures of rod-like and wedge-shaped mesogens were prepared. The phase behavior was studied using differential scanning calorimetry (DSC), polarizing optical microscopy (POM) and X-ray diffraction (XRD). The study revealed a strong dependency of the mesophase structure on the mesogen composition. Usage of a less bulky columnar mesogen suppressed the formation of columnar mesophases in the mixture and led to a decrease in melting and clearing temperatures. The phase behavior is discussed in terms of the packing parameter model typically applied for lyotropic liquid crystals.

Low-cost prototype for real-time analysis using liquid crystal optical sensors in water quality assessment

In the food production sector, quickly identifying potential hazards is crucial due to the resilience of many pathogens, which could lead to wasted production results and, more severely, epidemic outbreaks. E. coli monitoring is essential; however, traditional quality control methods in fish farming are often slow and intrusive, thus promoting an increase in fish stress and mortality rates. This paper presents an alternative method by utilizing a prototype inspired by polarized optical microscopy (POM), constructed with a Raspberry Pi microprocessor to assess pixel patterns and calculate analyte levels. The sensors are based on the immune complexation reactions between E. coli specific antibodies and the disruption of liquid crystal (LC) alignment, which are measured with the POM technique. The prototype yielded a sensitivity of 1.01%±0.17%/log10 (CFU/mL) for E. coli. In this paper, tests using sunlight as the prototype’s light source were also performed, and a user-friendly graphical user interface was designed.

Characterization of Microstructure and Localized Corrosion Resistance of Heat-Treated 17-4 PH Stainless Steel Fabricated by Material Extrusion

The quality, reproducibility, and reliability of additive manufactured parts strongly depend on optimizing printing parameters and post-processing treatments. This study evaluates the effects on the microstructure and corrosion resistance properties of solution annealing and aging heat treatments performed on 17-4 PH stainless steel samples fabricated with different build-up orientations using a material extrusion technology: the Bound Metal DepositionTM. The chemical composition and microstructures were determined using X-ray diffraction, chemical etching, optical microscopy, and scanning electron microscopy. The corrosion resistance properties in neutral sodium chloride electrolytes were investigated through cyclic potentiodynamic polarization and open circuit potential monitoring and analysis. The findings demonstrated that the solution annealing heat treatment remarkably enhanced the overall corrosion resistance properties of the samples. The improvement was attributed to the growth of the ferritic phase along the grain boundaries of the martensitic matrix and a finer dispersion of copper precipitates. The aging heat treatment performed after solution annealing enhanced the ferritic phase development, resulting in a further improvement of the localized corrosion resistance properties.