Line Scan Research Articles

Synthesis of Bimetallic Pd/Pt Truncated Nanocubes and Their Catalytic Performance in Selective Hydrogenation of Acetophenone

A series of bimetallic Pd/Pt truncated nanocube catalysts with similar morphologies and particle sizes but different platinum contents were successfully synthesized using a colloidal method without using any capping agents. Their hydrogenation properties were systematically studied and compared with their monometallic Pd or Pt nanocrystal counterparts. The results of EDX-mapping and line scanning show that platinum was relatively uniformly distributed on the surface of the Pd/Pt bimetallic nanocrystals and was not selectively deposited at the corners of the nanocrystals. The results of the selective hydrogenation of acetophenone demonstrate that the hydrogenation rate and the carbonyl selectivity of bimetallic Pd/Pt truncated nanocube catalysts are generally much higher than those of their monometallic Pd or Pt nanocrystal counterparts. It was found that the electronic interaction between palladium and platinum in the bimetallic Pd/Pt truncated nanocube catalysts and the corresponding hydrogenation activity in the selective hydrogenation of acetophenone are closely related to the molar ratio between platinum and palladium and the thickness of the platinum layer in the bimetallic Pd/Pt truncated nanocube catalyst. With an increase in the Pt/Pd molar ratio in the bimetallic Pd/Pt truncated nanocube catalysts, the activity and carbonyl selectivity in the acetophenone hydrogenation reaction increase first, reach a maximum when the molar ratio of Pt/Pd is 0.02 and the theoretical thickness of Pt is 1.3 atomic layers, and then decrease with a further increase in the Pt/Pd ratio. The hydrogenation rate of acetophenone on the Pd/Pt0.02 catalyst reaches 1.07 × 103 mmol·h−1·gcat.−1, which is 79 and 75 times larger than that of the monometallic Pd and Pt nanocrystal catalysts, respectively. The maximum yield of the target product 1-phenylethanol on the Pd/Pt0.02 truncated nanocube catalyst reaches 97.2%, which is 6.6% and 16.7% higher than that of the monometallic Pd and Pt nanocrystal catalysts, respectively.

Development of an Indoor Line Scanning System for Photovoltaic Modules Performance Characterization

Uganda’s interest in using solar energy for various applications began 20 years ago. However, there have been numerous reports by the public over the poor performance of the photovoltaic (PV) modules during their operations. This study determined the performance of selected PV modules openly sold in the Ugandan markets. The low-cost indoor line scanning system developed using locally available materials was used to assess the performance of each of the solar cells in the PV module by determining their photogenerated currents. In addition, the electrical characteristics of the PV modules were determined using the outdoor characterization method, which assumed the actual operation of the PV modules under direct sunlight. From the indoor line scans, the percentage of performing solar cells in the three PV modules of single-crystalline silicon (c-Si), multi-crystalline silicon (mc-Si), and amorphous silicon (a-Si) technologies were determined as 79%, 61%, and 95% respectively. The outdoor characterization results revealed that the c-Si, mc-Si, and a-Si PV modules had measured maximum power outputs of 18.5 W, 19.0 W, and 18.9 W, respectively and these were lower than the rated power values of 20 W. The line scan results had a direct relationship with the measured power output of the PV module, which implied that solar cells mismatch, affected the performance of the PV modules by lowering their performance. This developed testing system is affordable for developing countries with limited testing systems and would in the long run contribute to significant increase in the deployment of PV technology in Uganda since only performing PV modules would be allowed into the open market.

The generation method of orthophoto expansion map of arched dome mural based on three-dimensional fine color model

Murals carry cultural significance and historical information, and are an important channel for understanding ancient social norms, artistic styles, and religious beliefs. At present, the digitization of murals is an important technical means for the protection of cultural heritage. Orthogonal images of murals play a vital role in high-precision recording, preservation, academic research, educational expansion, mural protection, digital exhibition and dissemination. At present, orthogonal images of murals are mostly realized by plane projection, but this method is not suitable for making orthogonal images of arched and dome-shaped murals. To address this problem, this paper proposes a method for generating orthogonal expansion images of arched and dome-shaped murals. This method combines a three-dimensional virtual space simulation model with an RTT virtual camera and adopts a spatial reference orthogonal ray scanning model. First, the detailed three-dimensional color model is fitted to the geometric reference of cylindrical and spherical objects to determine its parameters. Next, for the cylindrical murals on the arch, the orientation of the model is initialized using quaternions, and the viewport matrix is adjusted to obtain the required resolution. Then, the RTT camera is used to perform line orthogonal projection in the viewport, and the fringe projection image is generated by rotating around the cylinder axis according to the inversely calculated rotation angle. For the murals on the dome ceiling, this method is used to segment them according to a certain longitude, and the circumscribed cylinder of the fitted sphere is rotated to perform cylindrical orthogonal line scanning in the segmented area. These individual orthogonal line scan images are carefully spliced together to form a complete orthogonal unfolded image. Finally, a fringe projection image is generated with the central meridian of the unfolded part as the center line, and the fringe projection images are spliced together to obtain the final orthogonal unfolded image. Experiments show that compared with existing methods, this method can generate two-dimensional orthogonal unfolded images with high texture fidelity, minimal texture deformation, and uniform deformation distribution. This study provides a novel perspective on the orthogonal unfolding of quasi-cylindrical and quasi-spherical painted objects, and provides an accurate and diverse data basis for the digitization of murals.

Characteristics of [formula omitted]-gallium oxide sputtering etching by focused gallium ion beam in the micro-nano scale fabrication

β−Gallium oxide (β−Ga2O3) is an ultra-wide bandgap (UWBG) semiconductor that is widely recognized as an ideal material for the fabrication of optoelectronic and high-voltage devices. As an advanced sputtering etching technology, focused ion beam (FIB) is increasingly used in the micron and nanoscale processing of semiconductor materials. In this paper, the influence of Ga FIB on β−Ga2O3 sputtering etching is systematically investigated from three parts, namely, the calibration of sputtering yield, the influence of process parameters on etched profiles and the effect of redeposition. Firstly, sputtering etching of β−Ga2O3 substrate was carried out under typical processing conditions with different incident angles, and the key parameter of sputtering yield was precisely calibrated. Further, Yamamura’s model is applied to fit the curve of sputtering yield as a function of incident angle. In addition, the sputtering yield of β−Ga2O3 is compared with those of GaNand Si under the same conditions, revealing the differences between them. Next, through line scan etching and trench structure etching experiments, this study analyzed the maximum etched depth, width, and etched volume variation with ion dose. The evolution of β−Ga2O3 trench structure is described and compared with GaN and GaAs, and from the perspective of redeposition effects, the reasons for the differences between the three in the sputtering etching process are analyzed. Lastly, the study investigated the impact of FIB process parameters on the redeposition effects of β−Ga2O3. Experimental results indicated that significant redeposition effect occurs when etching β−Ga2O3 with large single dwell time. Further analysis identified a specific range of single dwell times under different beam currents that reduce redeposition and prevents the formation of sidewall angles. These findings provide important data support for the high-resolution etching process of β−Ga2O3-based micro-nano devices using FIB technology, and also establish a reliable data foundation for related simulation studies.

Metaheuristic Algorithm and Laser Projection for Adjusting the Model of the Last Lower Surface to a Footprint.

Nowadays, metaheuristic algorithms have been applied to optimize last lower-surface models. Also, the last lower-surface model has been adjusted through the computational algorithms to perform custom shoe lasts. Therefore, it is necessary to implement nature-inspired metaheuristic algorithms to perform the adjustment of last lower-surface model to the footprint topography. In this study, a metaheuristic genetic algorithm is implemented to adjust the last lower surface model to the footprint topography. The genetic algorithm is constructed through an objective function, which is defined through the last lower Bezier model and footprint topography, where a mean error function moves the last lower surface toward the footprint topography through the initial population. Also, the search space is deduced from the last lower surface and footprint topography. In this way, the genetic algorithm performs explorations and exploitations to optimize a Bezier surface model, which generates the adjusted last lower surface, where the surface is recovered via laser line scanning. Thus, the metaheuristic algorithm enhances the last lower-surface adjustment to improve the custom last manufacture. This contribution is elucidated by a discussion based on the proposed metaheuristic algorithm for surface model adjustment and the optimization methods implemented in recent years.

Spatter detection and tracking in high-speed video observations of laser powder bed fusion

Purpose In laser powder bed fusion (L-PBF) additive manufacturing, spatter particles are ejected from the melt pool and can be detrimental to material performance and powder recycling. Quantifying spatter generation with respect to processing conditions is a step toward mitigating spatter and better understanding the phenomenon. This paper reveals process insights of spatter phenomena by automatically annotating spatter particles in high-speed video observations using machine learning. Design/methodology/approach A high-speed camera was used to observe the L-PBF process while varying laser power, laser scan speed and scan strategy on a constant geometry on an EOSM290 using Ti-6Al-4V powder. Two separate convolutional neural networks were trained to segment and track spatter particles in captured high-speed videos for spatter characterization. Findings Spatter generation and ejection angle significantly differ between keyhole and conduction mode melting. High laser powers lead to large ejections at the beginning of scan lines. Slow and fast build rates produce more spatter than moderate build rates at constant energy density. Scan strategies with more scan vectors lead to more spatter. The presence of powder significantly increases the amount of spatter generated during the process. Originality/value With the ability to automatically annotate a large volume of high-speed video data sets with high accuracy, an experimental design of observed parameter changes reveals quantitively stark changes in spatter morphology that can aid process development to mitigate spatter occurrence and impacts on material performance.

Effect of Composite Scanning Strategy on Forming Quality, Microstructure, and Tensile Properties of Laser Powder Bed Fusion Titanium Alloy

Laser powder bed fusion (LPBF) technology offers significant advantages in manufacturing complex‐shaped titanium alloy components. Traditional scanning strategies, such as zigzag and island scanning, however, often fall short in fabricating parts with variable cross sections. To enhance the forming quality of components featuring combined thin‐walled and bulk structures, a composite scanning strategy is proposed that adapts to the local characteristics of parts. This novel approach is designed to employ both island and zigzag scanning within the same deposition layer, aiming to optimize the balance between porosity and stress distribution. Notably, with a feature transition distance of 4 mm and a scan line offset of 0.67 mm, the specimens achieve a tensile strength of 1311.0 MPa, a yield strength of 1103.0 MPa, and an elongation of 8.8%. This strategy leads to the optimization of defects and a transition in microstructure for combined structural features. These promising outcomes lay the foundation for the intelligent allocation of scanning strategies and the high‐quality formation of complex‐shaped, high‐strength titanium alloy parts.

The determination of the line of interest in a line scan camera-based measurement system

The determination of the line of interest in a line scan camera-based measurement system

Machine Vision AI in Railroad Safety: Advanced Inspection Techniques

This article explores the transformative impact of machine vision AI in railroad safety, focusing on advanced inspection techniques. It examines the integration of technologies such as line scan cameras, LiDAR, and IoT sensors in enabling comprehensive 360-degree inspections of railway infrastructure and rolling stock. The article discusses key innovations in continuous monitoring, data processing, and AI-driven analysis, highlighting their potential to significantly enhance defect detection accuracy, reduce maintenance costs, and prevent accidents. Real-world applications in structural integrity assessment and overhead line inspection are presented, along with an analysis of current challenges and future developments in the field. The article underscores the paradigm shift from reactive to proactive maintenance strategies, promising a safer and more efficient future for rail transport.

Relative Radiometric Correction Method Based on Temperature Normalization for Jilin1-KF02

The optical remote sensors carried by the Jilin-1 KF02 series satellites have an imaging resolution better than 0.5 m and a width of 150 km. There are radiometric problems, such as stripe noise, vignetting, and inter-slice chromatic aberration, in their raw images. In this paper, a relative radiometric correction method based on temperature normalization is proposed for the response characteristics of sensors and the structural characteristics of optical splicing of Jilin-1 KF02 series satellites cameras. Firstly, a model of temperature effect on sensor output is established to correct the variation of sensor response output digital number (DN) caused by temperature variation during imaging process, and the image is normalized to a uniform temperature reference. Then, the horizontal stripe noise of the image is eliminated by using the sensor scan line and dark pixel information, and the vertical stripe noise of the image is eliminated by using the method of on-orbit histogram statistics. Finally, the method of superposition compensation is used to correct the vignetting area at the edge of the image due to the lack of energy information received by the sensor so as to ensure the consistency of the image in color and image quality. The proposed method is verified by Jilin-1 KF02A on-orbit images. Experimental results show that the image response is uniform, the color is consistent, the average Streak Metrics (SM) is better than 0.1%, Root-Mean-Square Deviation of the Mean Line (RA) and Generalized Noise (GN) are better than 2%, Relative Average Spectral Error (RASE) and Relative Average Spectral Error (ERGAS) are greatly improved, which are better than 5% and 13, respectively, and the relative radiation quality is obviously improved after relative radiation correction.

Line Scanning Thermography for Rail Base Defect Detection

Rail base defects present significant maintenance challenges within the railway industry, as they are difficult to detect before they lead to failure. While various in-motion nondestructive evaluation (NDE) methods exist for inspecting rail, none of the existing NDE methods can inspect the rail base area reliably and efficiently. This paper presents finite element analysis (FEA) and experimental results applying a line scanning thermography (LST) approach developed for rail base area inspection. This work demonstrates the LST approach for dynamic inspection of thick steel components containing surface and subsurface anomalies. Initially, FEA was used to help design the experimental setup, then to compare trends in surface temperature variations with those from experimental trials. Through motorized testing, test specimens were moved through a region heated by three stationary line heaters, utilizing LST to capture temperature variations on the surface of each sample. The results have shown that subsurface features 6.3 mm in diameter and with aspect ratios greater than 1.5 were detectable in all specimens. This is a promising development that warrants further study.

Application of the thermal shock response spectrum (TSRS) methodology to various forms of heat sources by pulse thermography and comparison by using a rotating line scan contour search algorithm

Application of the thermal shock response spectrum (TSRS) methodology to various forms of heat sources by pulse thermography and comparison by using a rotating line scan contour search algorithm

A Ground Penetrating Radar Array Antenna Designed for Small Targets Detection

Abstract The detection of small targets, especially those made of non-metallic materials, presents significant challenges due to their small size and the difficulty of being detected by traditional metal detectors. Additionally, environmental factors and soil conditions further complicate their detection. Traditional Ground Penetrating Radar (GPR) operate with a single-line detection method, resulting in a low number of scan lines and inefficient detection. This approach makes it difficult to generate slice images and can lead to high missed detection. A miniaturized GPR array antenna suitable for handheld and unmanned platforms has been developed. Utilizing a planar dipole antenna with multiple resistive and capacitive loading, a horizontal oblique polarization array comprising five transmitters and six receivers has been constructed, achieving dual-reception from a single transmission across ten channels, significantly improving upon the limitations of single antenna systems in terms of data volume and sampling interval. Experimental tests demonstrate that the designed system can successfully detect the echo signals of metallic and non-metallic small targets like buried up to 20 cm deep in typical soil conditions, and generate clear slice images, proving its capability for high-resolution imaging detection of shallow buried small targets.

An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI

Abstract. The retrieval of cloud parameters from the atmospheric Sentinel missions requires Earth reflectance measurements from a set of spectral bands. The ground pixels of the involved spectral bands should be fully aligned, but when they are not, special treatment is required within the operational algorithms. This so-called inter-band spatial misregistration of passive spectrometers is present when the Earth reflectance measurements in different spectral bands are captured by different spectrometers. The cloud retrieval algorithm requires reflectance measurements in the UV(ultraviolet)–VIS (visible) band, where the first cloud parameter (i.e., radiometric cloud fraction) is retrieved from OCRA (Optical Cloud Recognition Algorithm). In addition, Earth reflectances in the NIR (near-infrared) band are needed for the retrieval of two additional cloud parameters (i.e., cloud height and cloud albedo or cloud-top height and optical thickness) from the ROCINN (Retrieval of Cloud Information using Neural Networks) algorithm. In the former TROPOMI (TROPOspheric Monitoring Instrument)/S5P (Sentinel-5 Precursor) retrieval, a coregistration scheme of the derived cloud parameters from the source band to the target band based on pre-calculated mapping weights from UV–VIS to NIR and vice versa is applied. In this paper we present a new scheme for the coregistration of the TROPOMI cloud parameters using collocated VIIRS (Visible Infrared Imaging Radiometer Suite)/SNPP (Suomi National Polar-orbiting Partnership) information. The new coregistration scheme based on the VIIRS data improves the TROPOMI cloud product quality and allows the addition of cloud information for the first (westernmost) TROPOMI UVIS ground pixel. In practice, the latter means that a significant number of valid data points are included in the TROPOMI cloud, total ozone, SO2 and HCHO product since 26 November 2023 (orbit 31705), when the new coregistration scheme became operational. From a comparison analysis between the two techniques, we found that the largest differences mainly appear for inhomogeneous scenes. From a validation exercise of TROPOMI against VIIRS in the across-track direction, we found that the old coregistration scheme tends to smooth out cloud structures along the scan line, whereas such structures can be maintained with the new scheme. The need to implement a similar inter-band spatial coregistration scheme is foreseen for the Sentinel-4/MTG-S (Meteosat Third Generation – Sounder) and Sentinel-5/MetOp-SG (Meteorological Operational Satellite – Second Generation) missions. In the case of the Sentinel-4 instrument, the external cloud information will originate from collocated data captured by the FCI (Flexible Combined Imager) on board the MTG-I (Meteosat Third Generation – Imager) satellite.

Linearity Assessment from the Plankton, Aerosol, Cloud, and Ocean Ecosystem Ocean Color Instrument Pre-Launch Testing and On-Orbit Operations

Satellite observations have become increasingly important in scientific studies of the Earth’s climate, especially for oceanographic science. A next generation sensor known as the Ocean Color Instrument (OCI) was launched in February 2024 onboard the Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) platform, and will extend the data set of on-orbit scientific observations used for ocean and atmospheric science research. Delivering high quality data from a space-borne sensor requires the instrument to be well calibrated; while much of the calibration can be performed on-orbit, some aspects of the calibration must be measured or the mechanisms verified pre-launch. One aspect of the OCI calibration that is novel to its design (for a space-based sensor) is its ability to perform linearity measurements on orbit. When viewing the Sun reflected off a dim diffuser with ∼2–3% reflectance, the sensor is capable of recording successive pixels with increasing integration times. The result is a series of light levels measured from the same source in a given scan line. These measurements are then used to assess the linearity. During the pre-launch test campaign, this mechanism was verified using a known source in the Earth view portion of the scan. Results from these tests form a baseline that was verified once on-orbit (and the calibration may be replaced if the linearity is shown to change). The linearity measured both prior to launch and post-launch will be assessed in this work.

Precise and accurate speed measurements in rapidly flowing dense suspensions

We introduce a method for precise and accurate measurements of particle speeds in dense suspensions flowing at high rates and demonstrate the utility of the approach for revealing complex flow fluctuations during shearing in a setup that combines imaging with a confocal microscope and shearing with a rheometer. We scan the focal point in one dimension, aligned with direction of flow, producing absolute measurements of speed that are independent of suspension structure and particle shape. We compare this flow-direction line scanning approach with a complementary method we introduced previously, measuring speed using line scanning in the vorticity direction. By comparing results in various flow conditions, including shear-thinning and thickening regimes, we demonstrate the efficacy of our new approach. We find that both approaches exhibit qualitatively similar flow profiles, but a comparative analysis reveals a 15%–25% overestimation in speed measurement using vorticity line scanning, with discrepancies generated by anisotropic suspension microstructure under flow. Moreover, in the thickening regime where complex flow fields are present, both approaches capture local speed fluctuations. However, line scanning in the flow direction reveals and precisely captures stagnation and backflows, a capability not achievable with vorticity line scanning. The approach introduced here not only provides a refined technique for speed measurement in fast-flowing suspensions but also emphasizes the significance of accurate measurement techniques in advancing our understanding of flow behavior in dense suspensions, particularly in contexts where strong non-affine flows are prevalent.

A line scanning monitoring method for conveyor belt deviation using point cloud

Abstract A precise conveyor belt deviation monitoring method using line array point cloud data is proposed and demonstrated, which can ensure the healthy running of the conveyor system. The point cloud data characterizing the surface of the conveyor belt is collected in a line scanning way. Then, using a unique soft extraction method that weighted fusing three key features (cross-sectional variation, belt’s horizontal width, and previous frame) to process this data, the edge information of the conveyor belt can be accurately and robustly identified in real-time. Furthermore, the point cloud processing mode enables a belt-segmented deviation analysis method based on a standard sequence query. This can accurately determine the offset value and deviation trend of the conveyor belt, thereby achieving early warning of deviation faults. Experimental results show that the belt edge identification precision can reach 0.3 mm, and an early warning can be provided at least 57 m before the occurrence of a belt deviation fault. This belt deviation monitoring method can be widely applied in various working environments, especially in harsh conditions like mines and ports. It also has potential applications in automated production lines within Industry 4.0.

Hyperspectral Image Transects during Transient Events in Rivers (HITTER): Framework Development and Application to a Tracer Experiment on the Missouri River, USA

Rivers convey a broad range of materials, such as sediment, nutrients, and contaminants. Much of this transport can occur during or immediately after an episodic, pulsed event like a flood or an oil spill. Understanding the flow processes that influence the motion of these substances is important for managing water resources and conserving aquatic ecosystems. This study introduces a new remote sensing framework for characterizing dynamic phenomena at the scale of a channel cross-section: Hyperspectral Image Transects during Transient Events in Rivers (HITTER). We present a workflow that uses repeated hyperspectral scan lines acquired from a hovering uncrewed aircraft system (UAS) to quantify how a water attribute of interest varies laterally across the river and evolves over time. Data from a tracer experiment on the Missouri River are used to illustrate the components of the end-to-end processing chain we used to quantify the passage of a visible dye. The framework is intended to be flexible and could be applied in a number of different contexts. The results of this initial proof-of-concept investigation suggest that HITTER could potentially provide insight regarding the dispersion of a range of materials in rivers, which would facilitate ecological and geomorphic studies and help inform management.

Fabrication of silicon carbide color center nanoparticles by femtosecond laser ablation in liquid

Combined with the fabrication of low-dimensional materials, certain silicon carbide color centers can become excellent platforms for nano-optics, and be applied to quantum and biomedical fields. In this work, color center nanoparticles were prepared based on high purity semi-insulating silicon carbide substrate using liquid-assisted femtosecond laser machining. Effects of multi-pulse array ablation and line scanning fabrication were investigated, and parameters such as repetition frequency and pulse number were also optimized. We employed photoluminescence spectroscopy, field emission scanning electron microscopy and transient fluorescence spectroscopy to characterize optical properties and micromorphology of nanoparticles. The broadband photoluminescence within the range of 850–950 nm should be attributed to silicon vacancy color centers, and V1/V1’ zero-phonon lines were confirmed at low temperature. It is noted that the femtosecond laser annealing is critical for the luminescence enhancement of color center nanoparticles. The evolution of particles and cavitation bubbles during processing was elucidated. After optimization, silicon vacancy color center nanoparticles were prepared by multi-pulse (107) array ablation and 200 kHz pulse repetition frequency, with an average diameter of approximately 15 nm and an average density reaching 273.94 counts/μm2. The sample can be preserved stably in the form of nanoparticle solutions. Processing methods and corresponding conclusions of this study would be applicable to the femtosecond laser fabrication and micromorphology control of color center nanoparticles of various hard-brittle semiconductors.

Modeling sector route segment capacity under convective weather based on machine learning

Abstract Estimating airspace capacity under convective weather conditions is crucial for ensuring the efficiency and safety of air traffic operations. Sector route segments, which are essential components of flight routes, require timely capacity predictions during operationally critical periods. In this paper, initially, an enhanced Recursive Feature Elimination algorithm is used to select meteorological data and develop predictive features. Subsequently, the CWSRC model is established using the RF supervised learning algorithm. Finally, the paper takes ENH-YIH segment as an example to predict the capacity. Compared with other machine learning algorithms, the residual percentages for KNN, MLP and RF are 86.03%, 77.37% and 93.40%, respectively, within the range of [−0.2, 0.2]. In three separate day cases, results show that the CWSRC model’s MAE, MSE, RMSE and R2 significantly outperform traditional methods like Maxflow/Mincut and scanning line. The results confirm the CWSRC model’s superior predictive capabilities.