Precise Alignment Research Articles

Retrieval of Three-Dimensional Wave Surfaces from X-Band Marine Radar Images Utilizing Enhanced Pix2Pix Model

In this study, we propose a novel method for retrieving the three-dimensional (3D) wave surface from sea clutter using both simulated and measured data. First, the linear wave superposition model and modulation principle are employed to generate simulated datasets comprising 3D wave surfaces and corresponding sea clutter. Subsequently, we develop a Pix2Pix model enhanced with a self-attention mechanism and a multiscale discriminator to effectively capture the nonlinear relationship between the simulated 3D wave surfaces and sea clutter. The model’s performance is evaluated through error analysis, comparisons of wave number spectra, and differences in wave surface reconstructions using a dedicated test set. Finally, the trained model is applied to reconstruct wave surfaces from sea clutter data collected aboard a ship, with results benchmarked against those derived from the Schrödinger equation. The findings demonstrate that the proposed model excels in preserving high-frequency image details while ensuring precise alignment between reconstructed images. Furthermore, it achieves superior retrieval accuracy compared to traditional approaches, highlighting its potential for advancing wave surface retrieval techniques.

High-temperature-driven degradation analysis and modelling of an industrial gas turbine applicable γ/β NiCoCrAlYRe coating– Part II: Diffusion modelling and experimental validation under isothermal conditions

In a two-part research, the degradation process of a commercially utilised NiCoCrAlYRe coating on an IN738LC substrate was both simulated and experimentally validated. The first part of the study delved into the microstructural characteristics of the overlay coating system and analysed the oxidation and interdiffusion mechanisms. This second part extends the analysis to simulations of the coating degradation at temperatures of 900 °C and 1000 °C.The interdiffusion between the substrate and coating was modelled utilising thermodynamic and kinetic mobility data, while the oxidation aspect was addressed using two different established models from Whittle and Meier et al., along with an adapted approach specifically tailored for MCrAlY overlay coatings devised in this research. The simulation results were validated experimentally for up to 7000 h, with the degradation state evaluated based on the depletion of β-NiAl in the coating.Good agreement was found between simulated and experimental data. The initial coating and substrate microstructures were sufficiently predicted based on thermodynamic data, and the simulations closely matched the observed degradation pattern. The most precise alignment between simulated and experimental β-NiAl depletion was achieved with the newly proposed oxidation modelling approach for MCrAlY systems. While more optimisation is required to accurately predict the precipitation of chromium phases and carbides and to account for interdiffusion-obstructing effects, particularly at 1000 °C, the results demonstrate the potential of thermodynamic-kinetic modelling for estimating the lifespan of MCrAlY systems under diffusion-driven degradation conditions.

The safety, accuracy, and feasibility of robotic assistance in neuro-oncological surgery.

Over the past 3 decades, robotic technology has advanced significantly across surgical fields, driven by improvements in versatility, stability, skill, and tactile properties. Neurosurgery has led the way in integrating robotics to improve the accuracy and safety of procedures that require high precision. This study aimed to present one of the largest series in the literature and investigate the feasibility and effectiveness of robotic assistance in neuro-oncological surgery. The authors performed a retrospective review of the medical records of patients who underwent stereotactic biopsy only and biopsy and laser interstitial thermal therapy (LITT) using the Robotic Surgical Assistant (ROSA) system. The ROSA system was used for trajectory planning as well as the precise alignment and insertion of the needle and/or laser catheter. All procedures were performed by a single neurosurgeon. Electronic medical records were reviewed for patient demographics, preoperative clinical deficits, diagnosis, preoperative and postoperative MR images, lesion characteristics including location and volume, postoperative complications, and deficits. A total of 348 patients were identified. The mean age at surgery was 61.4 years, with 171 (49.1%) females. The most common presentations were GBM (40.5%, n = 141), metastasis (16.4%, n = 57), and radiation necrosis (9.8%, n = 34). A total of 189 (54%) patients underwent stereotactic biopsy only, and 159 (46%) underwent biopsy and LITT. The diagnostic yield of the cases was 98.6%, with only 5 cases having inconclusive pathology. Two (0.6%) patients experienced postoperative complications that resolved during follow-up. Ten (2.9%) patients developed asymptomatic subcentimeter hematomas following the procedure that did not require further intervention. No long-term consequences or permanent deficits occurred in any case within a median follow-up duration of 4.4 months (IQR 1-11.2 months). These results indicate that a robot-assisted approach for stereotactic biopsies and LITT is associated with a comparable diagnostic yield and safety profile for frameless and frame-based techniques. Its precision, user-friendly interface, and adaptability contribute to its suitability for such procedures. Future research, especially in long-term results and cost-effectiveness, will be crucial in fully understanding the utility and potential of this technology for broader applications in the field.

Promoting camouflaged object detection through novel edge-target interaction and frequency-spatial fusion

The realm of camouflage object detection endeavors to precisely discern concealed targets that blend seamlessly into their surroundings. Recently, numerous research endeavors have converged on unraveling the disguise tactics employed by such objects, striving for efficient and accurate detection results. While these methods have garnered notable advancements in unveiling camouflaged targets, they still grapple with challenges posed by the high similarity between objects and their backgrounds, resulting in misidentifications, detection oversights, and the loss of intricate details. In light of these challenges, this paper introduces an innovative EFNet, aimed at elevating the integrity of target recognition and the finesse of detail capture through deepened interactions between Edge-target features and Frequency-spatial information. Notably, recognizing that the edges of camouflaged objects harbor a wealth of intrinsic details and serve as crucial delimiters distinguishing them from their surroundings, we have devised a dual-path architecture, where an auxiliary Edge Detection (ED) branch collaborates seamlessly with the primary Object Segmentation (OS) branch. Within the OS branch, we meticulously crafted the Edge-induced Segmentation Refinement module (ESR), which ingeniously harnesses the refined edge information provided by the ED branch to bolster target segmentation accuracy and augment detail fidelity. Moreover, in the supporting ED branch, we propose the Structure-induced Edge Enhancement module (SEE), designed to accentuate edge information while suppressing irrelevant noise with the assistance of the OS branch, thereby ensuring the precise extraction of edge information. To further enrich the information on dual-path, we innovatively introduce a Frequency Decomposition Module (FDM), which decomposes images into low- and high-frequency components, tailored to enhance target segmentation and edge detection, respectively. Additionally, to ensure seamless integration of frequency-domain and spatial-domain information, we devised the versatile Frequency-Spatial Domain Mixer (FSDM), achieving precise information alignment and fusion of these two domains. Quantitative and qualitative experimental results demonstrate that our proposed EFNet significantly outperforms existing state-of-the-art methods on four widely used benchmark datasets.

Developing and validating an adaptive multi-layer vehicle trajectory reconstruction method for outlier removal

Trajectory data is vital for traffic flow studies, and aerial photography-based methods are increasingly used to collect such data. However, these datasets often contain errors from various sources, which can be exacerbated by numerical derivative processes. Previous efforts have not fully addressed some of these issues such as consistency, varying length of outlier sequences, and unknown ground truth trends. Moreover, existing validation methods are often indirect and problematic. To address these limitations, we propose an adaptive multi-layer vehicle trajectory reconstruction method, which consists of three modules: the Initial Window Arrangement module to ensure the precise alignment between the reconstruction window and detected outlier fragments, maintaining internal consistency at boundary points; the Window Size Feasibility Test module to adaptively determine the window size according to varying length of outlier sequences, and XGBoost-based Ground Truth Estimation module to be combined with a least-square-based objective function to significantly improve reconstruction accuracy and more closely replicate the underlying trend. Additionally, we introduce the jerk-based reconstruction, which outperforms the acceleration-based reconstruction. To reliably assess and select the best ground truth estimation scheme and objective function, a novel synthetic dataset containing both the ground truth and realistic outlier fragments is proposed. Subsequently, a comparative evaluation of six different outlier removal methods was conducted using Zen Traffic Data. The validation results, utilizing both the synthetic dataset and Zen Traffic Data, demonstrate the exceptional performance of the proposed method across various evaluation perspectives. The good performance of the proposed outlier removal method is further demonstrated by comparing the IDM calibration results using the trajectories with and without outliers being removed. With just one parameter (jerk anomaly threshold), the parameter settings of our method are more objective and generalizable.

Endoscope-assisted manipulator for precooling and deposition in low-temperature scanning tunneling microscope systems

We present the design and implementation of an advanced manipulator system for use in low-temperature scanning tunneling microscope (STM) setups, specifically in an ultrahigh vacuum (UHV) chamber equipped with a bottom-loading cryostat and a superconducting magnet. This system integrates UHV-compatible endoscope assemblies, providing visual assistance for the precise alignment and transfer of the STM scanner shuttle to its receptacle. Additionally, the manipulator features an 85 K precooling stage, enabling molecule deposition on metallic substrates and STM imaging to verify surface conditions before transferring the shuttle to its receptacle at 4.2 K. This new approach enhances the operational efficiency of STM systems, particularly for low-temperature measurements in high magnetic fields, and addresses the challenges of sample contamination and thermal shock during the transfer process.

SPW-TransUNet: three-dimensional computed tomography-cone beam computed tomography image registration with spatial perpendicular window Transformer.

Current medical image registration methods based on Transformer still encounter challenges, including significant local intensity differences and limited computational efficiency when dealing with three-dimensional (3D) computed tomography (CT) and cone beam CT (CBCT) images. These limitations hinder the precise alignment necessary for effective diagnosis and treatment planning. Therefore, the aim of this study is to develop a novel method that overcomes these challenges by enhancing feature interaction and computational efficiency in 3D medical image registration. This paper introduces a novel method that enhances feature interaction within Transformer by computing attention within resizable spatial perpendicular window (SPW). Additionally, it introduces a self-learning mapping control (SLMC) mechanism, which uses a mini convolutional neural network (CNN) to adaptively transform feature vectors into probability vectors. This approach is integrated into the UNet framework, resulting in the SPW-TransUNet. The effectiveness of the SPW-TransUNet is demonstrated through evaluations on two critical 3D medical imaging tasks: CT-CBCT registration and inter-CT registration. We utilized a range of evaluation metrics including Dice similarity coefficient (DICE), structural similarity index measure (SSIM), target registration error (TRE), and negative Jacobian percentage. The validation process involved comparative analysis against established baseline methods using statistical tests to ensure the robustness and reliability of our results. The proposed method demonstrated outstanding performance in the registration of 124 pairs of CT-CBCT lung images from 20 patients, achieving the lowest TRE of 2.16 mm and a minimal negative Jacobian of 0.126. It also recorded the highest SSIM and Dice coefficient of 86.87% and 88.28%, respectively. For the liver CT task involving 150 patients, the method achieved peak SSIM and DICE scores of 76.92% and 85.77%, respectively. Furthermore, ablation studies confirmed the effectiveness of the designed structural components. The SPW-TransUNet offers significant improvements in feature interaction and computational efficiency for medical image registration, providing an effective reference solution for patient and target localization in image-guided radiation therapy.

Quantifying the impact of construction defects on square RC columns

Reinforced concrete (RC) columns are integral to structural integrity, and their performance is highly dependent on precise construction practices. This study investigates the influence of construction defects on the behavior of RC columns, focusing on key parameters such as misplaced longitudinal (vertical) reinforcing bars, honeycombed concrete, variations in stirrup spacing, casting eccentricity, and the effectiveness of square welded wire mesh for confinement. An experimental program was conducted with thirteen square RC column specimens designed to simulate potential construction defects. The findings demonstrate a clear relationship between defect types and column performance. Misplacement of vertical reinforcing bars by up to 50 % resulted in a 17 % reduction in ultimate load capacity. Honeycombed concrete, due to inadequate compaction, was identified as the most harmful defect, leading to a 46 % decrease in ultimate load and a 34 % reduction in toughness. Conversely, reducing stirrup spacing, while maintaining reinforcement quantity, enhanced column performance; a 33 % reduction in spacing resulted in comparable ultimate load but a 36 % increase in toughness. Increased casting eccentricity negatively impacted performance, with a 30 mm eccentricity causing a 46 % reduction in ultimate load capacity. The addition of square welded wire mesh over stirrups significantly improved performance, yielding a 13 % increase in ultimate load, while the use of welded wire mesh alone was ineffective, resulting in nearly a 50 % decrease in load capacity. These results underscore the importance of proper construction practices for the safety and durability of concrete structures. Accurate reinforcement placement, thorough concrete compaction, and precise column alignment are crucial. The research also suggests that using square welded wire mesh alongside stirrups can mitigate some construction defects’ adverse effects. Future studies should explore a broader range of column designs and loading conditions, as well as the effectiveness of various repair and strengthening techniques for columns with existing defects.

Resolution-dependent MRI-to-CT translation for orthotopic breast cancer models using deep learning

Objective.This study aims to investigate the feasibility of utilizing generative adversarial networks (GANs) to synthesize high-fidelity computed tomography (CT) images from lower-resolution MR images. The goal is to reduce patient exposure to ionizing radiation while maintaining treatment accuracy and accelerating MR image acquisition. The primary focus is to determine the extent to which low-resolution MR images can be utilized to generate high-quality CT images through a systematic study of spatial resolution-dependent magnetic resonance imaging (MRI)-to-CT image conversion.Approach.Paired MRI-CT images were acquired from healthy control and tumor models, generated by injecting MDA-MB-231 and 4T1 tumor cells into the mammary fat pad of nude and BALB/c mice to ensure model diversification. To explore various MRI resolutions, we downscaled the highest-resolution MR image into three lower resolutions. Using a customized U-Net model, we automated region of interest masking for both MRI and CT modalities with precise alignment, achieved through three-dimensional affine paired MRI-CT registrations. Then our customized models, Nested U-Net GAN and Attention U-Net GAN, were employed to translate low-resolution MR images into high-resolution CT images, followed by evaluation with separate testing datasets.Main Results.Our approach successfully generated high-quality CT images (0.142mm2) from both lower-resolution (0.282mm2) and higher-resolution (0.142mm2) MR images, with no statistically significant differences between them, effectively doubling the speed of MR image acquisition. Our customized GANs successfully preserved anatomical details, addressing the typical loss issue seen in other MRI-CT translation techniques across all resolutions of MR image inputs.Significance.This study demonstrates the potential of using low-resolution MR images to generate high-quality CT images, thereby reducing radiation exposure and expediting MRI acquisition while maintaining accuracy for radiotherapy.

Precise wavelength alignment of second-harmonic generation in thin-film lithium niobate resonators.

Second-harmonic generation (SHG) plays a significant role in modern photonic technology. Integrated photonic resonators fabricated with thin-film lithium niobate can achieve ultrahigh efficiencies by combining small mode volumes with high material nonlinearity. Cavity-enhanced SHG requires accurate phase and frequency matching conditions, where fundamental and second-harmonic wavelengths are both on resonance. However, this double-resonance condition can typically be realized only at a fixed random wavelength due to the high sensitivity of photonic resonances to the device geometry and fabrication variations. Here, we propose a novel method that can achieve the double-resonance condition over a large wavelength range. We combine thermal-optic and electro-optic (EO) effects to realize the separate tuning of fundamental and second-harmonic resonances. We demonstrated that the optimum SHG efficiency can be maintained over a wavelength range that exceeds the limit achievable with only thermal tuning. With this flexible tuning capability, we further show the precise alignment of SHG wavelengths of two separate thin-film lithium niobate resonators without sacrificing efficiencies.

A polyamide-facilitated soldering approach for Mini LED precise alignment leveraging 3D interfacial networks

Driven by the need for improved quality, energy efficiency, and visual innovation, display technology has evolved from CRT to Mini LED. However, the transfer process in Mini LED assembly poses challenges in precision. This study addressed the displacement issue during the transfer process by investigating the synergistic effects of solder and functional organic chemicals. Through the Mini LED assembly process, with the Mini LED size measuring 150 μm (length) ∗ 100 μm (width) ∗ 70 μm (thickness), polyamide was identified as a facilitator for precise alignment, which enhanced self-alignment capabilities by 68.8 % and improved the accuracy on self-aligned distance from 12.5 μm to 21.1 μm in Mini LED packaging. Through the powder coalescence approach, further extensive analysis using XPS, SEM, FTIR, and DSC reveals the synergistic effects. It supports the proposed three-dimensional polyamide-tin ion coordination interfacial network construction mechanism that facilitates solder-to-solder self-alignment and coalescence. This study provides insight into such a polymer-metal ion 3D coordination network for Mini LED precise alignment, which is promising for mass production.

Evaluation of the Efficacy of 3D-Printed Customized Orthodontic Brackets in Reducing Treatment Time and Improving Outcomes

ABSTRACT Background: The advent of 3D printing technology has revolutionized orthodontic treatment, enabling the production of customized orthodontic brackets tailored to individual patients. These customized brackets are hypothesized to reduce treatment time and improve clinical outcomes by providing better alignment precision and minimizing the need for adjustments. Materials and Methods: A randomized controlled trial was conducted with 60 patients aged 12–25 years requiring orthodontic treatment. The participants were randomly assigned to two groups: the experimental group (n = 30) received 3D-printed customized orthodontic brackets, while the control group (n = 30) received conventional brackets. Treatment duration, the number of adjustment visits, and the quality of alignment were recorded over an 18-month period. Quality of alignment was assessed using the American Board of Orthodontics (ABO) grading system. Statistical analysis was performed using a paired t-test to compare the outcomes between the two groups. Results: The average treatment time for the experimental group was significantly shorter, with a mean duration of 14.2 months (SD ± 1.8) compared to 18.6 months (SD ± 2.3) in the control group (P < 0.01). The number of adjustment visits was reduced by 35% in the experimental group (mean = 8 visits) compared to the control group (mean = 12 visits). Furthermore, the quality of alignment, as measured by the ABO grading system, was significantly higher in the experimental group (mean score = 90.5) compared to the control group (mean score = 78.2) (P < 0.05). Conclusion: The use of 3D-printed customized orthodontic brackets significantly reduces treatment time and improves the quality of orthodontic outcomes compared to conventional brackets. These findings suggest that customized brackets offer a promising approach for enhancing the efficiency and effectiveness of orthodontic treatment, potentially leading to better patient satisfaction and reduced clinical workload.

Research on Support Structure of Rectangular Cryogenic Infrared Lens with Large Aperture

This paper presents the design and optimization of a composite flexible support structure aimed at addressing the challenges associated with maintaining the positional accuracy and surface integrity of large-aperture cryogenic infrared lenses with long focal lengths. The primary objective of the structure is to maintain precise lens alignment while preserving the surface shape under operational conditions. The design complexities and underlying principles of the flexible support structure are systematically explored. A mechanical model of the flexible support structure was derived based on its structural characteristics, and the equilibrium equation was established to ensure the lens meets thermal deformation requirements in various directions. Optimization of key design parameters was conducted for a lens operating at 200 K, measuring 304 mm × 230 mm. The gravitational deformation of the optimized lens exhibited a root mean square (RMS) surface accuracy of 7.72 nm in the X direction, 7.08 nm in the Y direction, and 9.60 nm in the Z direction for lens surface 1. For lens surface 2, RMS values were 8.62 nm in the X direction, 8.41 nm in the Y direction, and 9.64 nm in the Z direction. At 200 K and lower temperatures, the RMS values of lens surfaces 1 and 2 were 2.41 nm and 2.74 nm, respectively, with a first-order mode frequency of 143.37 Hz.

3D Crystal Construction by Single-Crystal 2D Material Supercell Multiplying.

2D stacking presents a promising avenue for creating periodic superstructures that unveil novel physical phenomena. While extensive research has focused on lateral 2D material superstructures formed through composition modulation and twisted moiré structures, the exploration of vertical periodicity in 2D material superstructures remains limited. Although weak van der Waals interfaces enable layer-by-layer vertical stacking, traditional methods struggle to control in-plane crystal orientation over large areas, and the vertical dimension is constrained by unscalable, low-throughput processes, preventing the achievement of global order structures. In this study, a supercell multiplying approach is introduced that enables high-throughput construction of 3D superstructures on a macroscopic scale, utilizing artificially stacked single-crystalline 2D multilayers as foundational repeating units. By employing wafer-scale single-crystalline 2D materials and referencing the crystal orientation of substrates, the method ensures precise alignment of crystal orientation within and across each supercell, thereby achieving controllable periodicity along all three axes. A centimeter-scale 3R-MoS₂ crystal is successfully constructed, comprising over 200 monolayers of single-crystalline MoS₂, through a bottom-up stacking process. Additionally, the approach accommodates the integration of amorphous oxide, enabling the assembly of 3D non-linear optical crystals with quasi-phase matching. This method paves the way for the bottom-up construction of macroscopic artificial 3D crystals with atomic plane precision, enabling tailored optical, electrical, and thermal properties and advancing the development of novel artificial materials and high-performance applications.

Study on Path Planning in Cotton Fields Based on Prior Navigation Information

Aiming at the operation scenario of existing crop coverage and the need for precise row alignment, the sowing prior navigation information of cotton fields in Xinjiang was used as the basis for the study of path planning for subsequent operations to improve the planning quality and operation accuracy. Firstly, the characteristics of typical turnaround methods were analyzed, the turnaround strategy for dividing planning units was proposed, and the horizontal and vertical operation connection methods were put forward. Secondly, the obstacle avoidance strategies were determined according to the traits of obstacles. The circular arc–linear and cubic spline curve obstacle avoidance path generation methods were proposed. Considering the dual attributes of walking and the operation of agricultural machinery, four kinds of operation semantic points were embedded into the path. Finally, path generation software was designed. The simulation and field test results indicated that the operation coverage ratio CR ≥ 98.21% positively correlated with the plot area and the operation distance ratio DR ≥ 86.89% when non-essential reversing and obstacles were ignored. CR and DR were negatively correlated with the number of obstacles when considering obstacles. When considering non-essential reversing, the full coverage of operating rows could be achieved, but DR would be reduced correspondingly.

BIFROST: A method for registering diverse imaging datasets of the Drosophila brain

Imaging methods that span both functional measures in living tissue and anatomical measures in fixed tissue have played critical roles in advancing our understanding of the brain. However, making direct comparisons between different imaging modalities, particularly spanning living and fixed tissue, has remained challenging. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, reaching this goal is difficult because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas. We then develop a registration pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments.

Numerical simulation of individualized flow diversion cerebral aneurysms treatment

AbstractFlow diverter implantation has emerged as a highly effective treatment for cerebral aneurysms. However, the variability in patient‐specific vascular anatomy necessitates detailed pre‐operative planning to mitigate potential complications arising from standard, one‐size‐fits‐all devices. To address these challenges, robust predictive simulation tools are essential for optimizing flow diverter design and implantation strategies tailored to the unique characteristics of each patient's anatomy. This study focuses on developing an advanced numerical simulation tool for modeling the mechanical behavior of braided flow diverters during crimping and navigation through patient‐specific vasculature. Using isogeometric analysis (IGA) with NURBS‐based representations in LS‐Dyna, the intricate deformations of the flow diverter wires during catheter crimping are captured with high accuracy. The patient‐specific vessel geometries, derived from imaging data, are integrated into the model to account for variations in vascular structure, ensuring precise alignment and controlled navigation of the device. The navigation process relies on optimizing the central axis of the blood vessel to minimize torsional stress on the flow diverter, reducing the risk of device malfunction or failure during deployment. By incorporating patient‐specific information, such as vessel curvature and tortuosity, the simulation tool enables the prediction of potential issues, thus allowing for intervention planning that is tailored to individual anatomy. This patient‐specific approach enhances the safety and efficacy of flow diverter implantation and serves as a foundation for improved device design prior to prototype development.

Generation of pseudo-nondiffracting symmetrical Layer beams using cylindrical lenses

The pseudo-nondiffracting behavior of optical caustic beams makes them potentially useful in precise component alignment at CERN. This paper introduces an innovative method for generating one and two-dimensional symmetrical Airy-like beams, known as Layer beams, utilizing a specific configuration of plano-convex cylindrical lenses. Simulations and experimental results have validated this method, demonstrating its potential for scalable and cost-effective beam production with tunable properties, making it practical for a wide range of scientific applications.

Addressable planar arrays of highly-luminescent 1,4-bis(5-phenyloxazol-2-yl)benzene nanowires via mask-confined graphoepitaxy for optoelectronic applications

This study introduces a facile method for the controlled growth of addressable planar arrays of highly luminescent, catalyst-free 1,4-bis(5-phenyloxazol-2-yl)benzene (POPOP) nanowires. By employing a hollow mask over a faceted sapphire substrate, simultaneous control over the position and orientation of the nanowires is achieved through a mask-confined graphoepitaxial growth, offering substantial advantages over traditional post-growth assembly techniques. High-temperature annealing creates parallel nanogrooves on the sapphire surface, inducing a graphoepitaxial effect that aligns the nanowires with a consistent [102] crystallographic axis. The hollow mask further aids in precisely localizing nanowire growth through its shadowing effect. Optoelectronic investigations reveal that these nanowires emit intense and stable blue photoluminescence at room temperature, with a broad spectrum spanning from 400 to 600 nm. This luminescence is achieved through excitation by continuous-wave ultraviolet light or two-photon absorption using femtosecond infrared light. Notably, the emission quantum efficiency of POPOP nanowires reaches 59 %, a remarkable improvement over the 12 % observed in powder counterparts when excited with 405 nm light. Transit absorption spectra indicate that ground state bleaching and excited state absorption display consistent kinetics within a 100 ps time window, suggesting the same origin from singlet excitons. The precise alignment and positioning of these nanowires make them viable for in-situ integration into photodetectors with rapid ultraviolet light responses. This study advances the controlled growth of catalyst-free nanowire arrays and enhances the understanding of the optoelectronic properties of POPOP nanowires.

Transport-of-intensity differential phase contrast imaging: defying weak object approximation and matched-illumination condition

Quantitative phase imaging (QPI) has emerged as a promising label-free imaging technique with growing importance in biomedical research, optical metrology, materials science, and other fields. Partially coherent illumination provides resolution twice that of the coherent diffraction limit, along with improved robustness and signal-to-noise ratio, making it an increasingly significant area of study in QPI. Partially coherent QPI, represented by differential phase contrast (DPC), linearizes the phase-to-intensity transfer process under the weak object approximation (WOA). However, the nonlinear errors caused by WOA in DPC can lead to phase underestimation. Additionally, DPC requires strict matching of the illumination numerical aperture (NA) to ensure the complete transmission of low-frequency information. This necessitates precise alignment of the optical system and limits the flexible use of objective and illumination. In this study, the applicability of the WOA under different coherence parameters is explored, and a method to defy WOA by reducing the illumination NA is proposed. The proposed method uses the transport-of-intensity equation through an additional defocused intensity image to recover the lost low-frequency information due to illumination mismatch, without requiring any iterative procedure. This method overcomes the limitations of DPC being unable to recover large phase objects and does not require the strict illumination matching conditions. The accurate quantitative morphological characterization of customized artifact and microlens arrays that do not satisfy WOA under non-matched-illumination conditions demonstrated the precise quantitative capability of the proposed method and its excellent performance in the field of measurement. Meanwhile, the phase retrieval of tongue slices and oral epithelial cells demonstrated its application potential in the biomedical field. The ability to accurately recover phase under a concise and implementable optical setup makes it a promising solution for widespread application in various label-free imaging domains.