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With the development of robotics and vision measurement technology, the use of robots with line laser scanners for 3D scanning and measurement of parts has become a mainstream trend in the field of industrial inspection. Traditional scanning and measuring robots mainly use the teach-in scanning method, which has unstable scanning quality and low scanning efficiency. In this paper, the adaptive sampling method for a free-form surface, which can realize the adaptive distribution of surface measurement points according to the curvature features of free-form surfaces, is proposed first. Then, integrated with the proposed adaptive sampling method, the automatic path planning method is proposed. This method consists of adaptive sampling, scanning attitude calculation based on a quaternion, scanning viewpoint planning based on viewable cones, and scan path generation based on bi-directional scanning. Based on the proposed automatic path planning method, the scanning and measuring robot can obtain complete 3D information of the surface to be measured with high measurement accuracy and efficiency. The performance index of the laser scanner can be fully reached. [ABSTRACT FROM AUTHOR]

Bearings are crucial components of machinery and equipment, and it is essential to inspect them thoroughly to ensure a high pass rate. Currently, bearing scratch detection is primarily carried out manually, which cannot meet industrial demands. This study presents research on the detection of bearing surface scratches. An improved YOLOV5 network, named YOLOV5-CDG, is proposed for detecting bearing surface defects using scratch images as targets. The YOLOV5-CDG model is based on the YOLOV5 network model with the addition of a Coordinate Attention (CA) mechanism module, fusion of Deformable Convolutional Networks (DCNs), and a combination with the GhostNet lightweight network. To achieve bearing surface scratch detection, a machine vision-based bearing surface scratch sensor system is established, and a self-made bearing surface scratch dataset is produced as the basis. The scratch detection final Average Precision (AP) value is 97%, which is 3.4% higher than that of YOLOV5. Additionally, the model has an accuracy of 99.46% for detecting defective and qualified products. The average detection time per image is 263.4 ms on the CPU device and 12.2 ms on the GPU device, demonstrating excellent performance in terms of both speed and accuracy. Furthermore, this study analyzes and compares the detection results of various models, demonstrating that the proposed method satisfies the requirements for detecting scratches on bearing surfaces in industrial settings. [ABSTRACT FROM AUTHOR]

[Objective] Intelligent Sensing Engineering is an emerging discipline that responds to the ongoing technological revolution and industrial transformation. Established with a focus on meeting the national demands in emerging industries, this interdisciplinary field is a significant research topic for the development of intelligent sensing education. The design of sensor courses holds a key position in the curriculum, serving as a vital link between foundational and advanced concepts. The traditional sensor course at our institution primarily addresses the design and development of analog sensor interface circuits for Pt100 resistance temperature detectors. However, the fixed experimental procedures and a singular approach have impacted student enthusiasm in this regard. In contrast, flexible sensors, as a novel technology with broad innovation potential, have become a driving force in the era of intelligent sensing. Hence, incorporating the design of sensors in the curriculum has become imperative to familiarize students with the structures and system design concepts of new types of flexible sensors, such as graphene-based sensors. To cultivate students' abilities for independent learning, practical application, innovation, and solving complex engineering problems, a comprehensive course was designed. [Methods] This course integrates the production of flexible sensors, the construction of measurement circuits, data processing methods, and system calibration. Through this course, students can explore new application areas, design innovative intelligent sensing devices, and develop creative thinking and problem-solving skills. The goal is to enhance students' comprehensive skills and knowledge across multiple disciplines, covering the design of sensitive elements, conversion components, and conversion circuitry. The course also explores the manufacturing of flexible sensors using advanced materials, providing an in-depth understanding of the working principles and current production philosophies of advanced flexible sensors. By integrating knowledge from electronic engineering, materials science, and mechanical engineering, the course aims to foster students' holistic competence. Practical exercises allow students to foster hands-on skills, independent critical thinking, and problem-solving abilities, thus cultivating talents with enhanced innovation, engineering practice, and research capabilities. [Results] Compared to graphene, graphite, owing to its electrical properties being less influenced by various factors, is less common in the production of standard sensors. However, its layered structure and ease of cutting and bending make it convenient for students to personalize their creations within the sensor system. In contrast to mature standardized sensors, the performance of graphite flexible sensors is influenced by additional factors, such as environmental temperature, graphite purity, cross-sectional area, and length. This complexity provides students with opportunities to develop problem-solving skills, exercise variable control, and enhance data processing capabilities, merging theoretical sensor knowledge with practical applications. The designed course on graphite flexible pressure sensors effectively improves the teaching methods in the new field of Intelligent Sensing Engineering. [Conclusion] This innovative teaching approach not only increases the student's interest in the course but also develops their ability to think innovatively using acquired knowledge. Moreover, the design of this course effectively hones students' practical innovation skills, making them more competitive in the job market. Furthermore, it helps meet the growing demand for talent in the field of Intelligent Sensing Engineering. In addition, being abreast with the cutting-edge technologies in the field, students gain detailed insights into the latest research findings, thus laying a solid foundation for subsequent research work for both senior undergraduate and graduate students. [ABSTRACT FROM AUTHOR]

The simultaneous analysis of trace amounts of dual biomarkers is crucial in the early diagnosis, treatment, and prognosis of hepatocellular carcinoma (HCC). In this study, we prepared SERS-active hydrogel microparticles (SAHMs) with 3D hierarchical gold nanoparticles (AuNPs) micro-nanostructures by microdroplet technology and in situ synthesis, which demonstrated high reproducibility and sensitivity. Compared with traditional 2D SERS substrates, this newly prepared 3D SERS substrate provided a high density of nano-wrinkled structures and numerous AuNPs. Furthermore, a newly designed SERS-active substrate was proposed for the simultaneous microfluidic detection of AFP and AFU. The Raman signals of sandwich immunocomplexes on the surface of the SAHMs were measured for the trace analysis of these biomarkers. The proposed microfluidic platform achieved AFP and AFU detection in the range of 0.1–100 ng mL−1 and 0.01–100 ng mL−1, respectively, which represents a good response. Indeed, this platform is easy to fabricate, of low cost and has short detection time and comparable detection limits to other methods. As far as we know, this is the first study to achieve the simultaneous detection of AFP and AFU on a microfluidic platform. Therefore, we proposed a new simultaneous detection platform for dual HCC biomarkers that shows strong potential for the early diagnosis of HCC. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a harmful malady that truly debilitates human health, and hence it is of significance to isolate and on-line profile the phenotype of HCC cells for further diagnosis and therapy. We developed a novel strategy for efficient capture and in situ heterogeneous phenotype analysis of circulating tumor cells (CTCs) at the single-cell level based on surface-enhanced Raman scattering (SERS) fingerprint characteristics. Herein, a new microfluidic chip with lantern-like bypass structure was designed to capture CTCs by their large size from whole blood. Furthermore, two types of SERS-aptamer nanotags were fabricated, realizing spectral recognition of single CTCs in accordance with the surface membrane protein expression. Up to 84% of CTCs with a purity of 95% were captured from whole blood samples using the present SERS-aptamer based microfluidic chip at 20 μL min−1. The results showed that the proposed strategy can successfully identify HCC cell subtypes by SERS measurements, which was related to the clinical surface biomarkers. This may open a new avenue for serving as a powerful tool of cancer diagnosis and prognosis evaluation. [ABSTRACT FROM AUTHOR]

The purpose of this study was to implement principal component analysis (PCA) on videocapsule endoscopy (VE) images to develop a new computerized tool for celiac disease recognition. Three PCA algorithms were implemented for feature extraction and sparse representation. A novel strip PCA (SPCA) with nongreedy L1-norm maximization is proposed for VE image analysis. The extracted principal components were interpreted by a non-parametric k-nearest neighbor (k-NN) method for automated celiac disease classification. A benchmark dataset of 460 images (240 from celiac disease patients with small intestinal villous atrophy versus 220 control patients lacking villous atrophy) was constructed from the clinical VE series. It was found that the newly developed SPCA with nongreedy L1-norm maximization was most efficient for computerized celiac disease recognition, having a robust performance with an average recognition accuracy of 93.9 percent. Furthermore, SPCA also has a reduced computation time as compared with other methods. Therefore, it is likely that SPCA will be a helpful adjunct for the diagnosis of celiac disease. [ABSTRACT FROM AUTHOR]

Highlights • We report a portable absorption photometry-based microfluidic system for detection of human triglyceride, albumin and total protein. • A new pump-free microfluidic chip is developed and contributed to detection sensitivity and miniaturization. • Quantitative analysis of three blood test items in the required concentration range is robust and reproducible. Abstract We reported a novel integrated portable human blood system based on absorption photometry and microfluidic technology. A capillary pump for fluid control was embedded in microfluidic chip instead of heavy syringe pump. The analytes could automatically flow, mix and react in chip. It achieves long-term stability of hydrophilic microfluidic channel surface for rapid and sensitive detection. Herein, we detected three analytes from human blood, including triglyceride (TGL), albumin (ALB) and total protein (TP) which are very important for health examination. All components of this system including light-emitting diode (LED) light source, microfluidic chip, photodiode detector (PD), electronic circuit, Organic LED screen and memory card were fully integrated into a portable instrument with small size (11.2 × 7.2 × 6.1 cm). The pair of LED and PD was used for measuring light absorbance of biochemical assay. The limit of detections (LODs) for TGL, ALB and TP were estimated to be below 0.1 mmol/L, 4 g/L and 10 g/L respectively, which is significantly below the clinical cut-off value. Passing − Boblok regression analyses were used to achieve the evaluation for this system. It showed good accuracy and feasibility of our system and was expected to be useful as a potential clinical tool for human blood serum test. [ABSTRACT FROM AUTHOR]

We report a novel wash-free magnetic immunoassay technique for prostate-specific antigen (PSA) that uses a surface-enhanced Raman scattering (SERS)-based microdroplet sensor. The magnetic bar embedded in a droplet-based microfluidic system segregates the free and bound SERS tags by splitting the droplets into two smaller parts. The presence of PSA targets leads more SERS tags to immunocomplex in one droplet so that fewer SERS tags remain in another supernatant solution droplet. Thus, SERS signal measurement enables the quantitative evaluation of PSA markers. This approach can provide a rapid and sensitive assay that is applicable for PSA cancer markers in serum without any washing. Specifically, SERS signals were measured at 174 droplets per minute and averaged for quantitative evaluation of PSA. The limit of detection (LOD) determined by our SERS-based microdroplet sensor was estimated to be below 0.1 ng mL−1, which is significantly below the clinical cut-off value for the diagnosis of prostate cancer. In addition, because the entire assay can be carried out automatically, only a minimal amount of sample is needed. Accordingly, the approach is expected to be useful as a potential clinical tool for the early diagnosis of prostate cancer. [ABSTRACT FROM AUTHOR]

We report the application of a fully automated surface-enhanced Raman scattering (SERS)-based solenoid-embedded microfluidic device to the quantitative and sensitive detection of anthrax biomarker poly-γ- d -glutamic acid (PGA) in solution. Analysis is based on the competitive reaction between PGA and PGA-conjugated gold nanoparticles with anti-PGA-immobilized magnetic beads within a microfluidic environment. Magnetic immunocomplexes are trapped by yoke-type solenoids embedded within the device, and their SERS signals were directly measured and analyzed. To improve the acccuracy of measurement process, external standard values for PGA-free serum were also measured through use of a control channel. This additional measurement greatly improves the reliability of the assay by minimizing the influence of extraneous experimental variables. The limit of detection (LOD) of PGA in serum, determined by our SERS-based microfluidic sensor, is estimated to be 100 pg/mL. We believe that the defined method represents a valuable analytical tool for the detection of anthrax-related aqueous samples. [ABSTRACT FROM AUTHOR]

Herein, nanorod structures and four-leaf clover structures of fluorescent zinc 5,10,15,20-tetra(4-pyridyl)-21 H,23 H-porphine (ZnTPyP)-containing coordination polymer particles (CPPs) were first synthesized by a bottom-up strategy assisted by surfactants and then employed as seed structures for further shape-transformation reactions. We have successfully designed the morphological transformation for different dimensions, achieving the controlled formation of octahedron structures at both the nanometer scale and micrometer scale from the seed structures. Our approach illustrates a new method to design and synthesize metalloporphyrin-containing CPPs in a systematic and controllable manner. [ABSTRACT FROM AUTHOR]

The referenced article [Opt. Express30, 28220 (2022)10.1364/OE.466148] has been retracted by the authors.

One-shot projection structured light 3D measurement is a method to establish the stereo matching relationship and reconstruct 3D shape by projecting one pattern. However, the traditional stereo matching algorithm does not solve the problem of low matching accuracy and matching efficiency, which fundamentally limits the accuracy of 3D measurement. As the projector and imaging systems have daily higher resolution and imaging quality, RGB dots projection has more application prospects because of its ability to establish a stereo matching relationship through one projection. In this work, we proposed a single-shot 3D measurement method using line clustering stereo matching, and model correction methods. The projected RGB dots are extracted by slope differenced distribution and area constrained erosion method. Area constrained erosion can solve the problem of the segmented connected blobs caused by insufficient projection resolution. The clustering stereo matching method is utilized to coarse match the segmented center red points. A model correction method is utilized to restore and constrain the pattern that cannot be imaged. Experimental results demonstrated that our method achieves the best accuracy of about 0.089mm, better than the traditional disparity and RGB line method, which may shed light on the proposed method can accurately reconstruct the 3D surface.

Highly sensitive analysis of cancer biomarkers demonstrates an important impact in early diagnosis and therapies of cancer. A novel surface-enhanced Raman scattering (SERS) based immunoassay using microfluidic technique was reported for rapid analysis of prostate-specific antigen (PSA) biomarker. It is a useful screening test to discriminate prostate cancer and other diseases related to prostate. A "sandwich" immunoassay based on SERS nanotags, PSA biomarkers, and magnetic beads was applied on a pump-free microfluidic sensor. Magnetic immunocomplexes are isolated and trapped at the detection chamber by a permanent magnet integrated into the chip. The PBS buffer washed magnetic immunocomplexes and brought the free gold nanoparticles to the downsteam channel for waste. Our results show a good linear response in the range from 0.01 to 100 ng mL -1 . The limit of detection of the PSA level is estimated to be below 0.01 ng mL -1 using this chip. This detection level of PSA biomarker in human serum can be accomplished in 5 min without manual incubation and a heavy syringe pump. To the best of our knowledge, this is the first SERS-based immunoassay which applied a pump-free microfluidic chip as a detection platform. We believe that the proposed method reveals a valuable potential tool for the diagnosis of prostate cancer.

Herein, nanorod structures and four-leaf clover structures of fluorescent zinc 5,10,15,20-tetra(4-pyridyl)-21H,23H-porphine (ZnTPyP)-containing coordination polymer particles (CPPs) were first synthesized by a bottom-up strategy assisted by surfactants and then employed as seed structures for further shape-transformation reactions. We have successfully designed the morphological transformation for different dimensions, achieving the controlled formation of octahedron structures at both the nanometer scale and micrometer scale from the seed structures. Our approach illustrates a new method to design and synthesize metalloporphyrin-containing CPPs in a systematic and controllable manner., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We report the fast and sensitive trace analysis of diaquat dibromide monohydrate (DQ) in water using a surface-enhanced Raman scattering (SERS)-based microdroplet sensor. This sensor is composed of two compartments: the first one is for droplet generation for fresh silver nanoparticle (AgNP) synthesis and the second for droplet merging for SERS detection. Silver ions were nucleated and grown to large size AgNPs in droplets, and then each droplet was synchronously merged with another droplet containing DQ for SERS detection. This two-phase liquid-liquid segmented flow system prevented memory effects caused by the precipitation of nanoparticle aggregates on channel walls because the aqueous droplets were isolated by a continuous oil phase. The limit of detection (LOD) of DQ in water was determined to be below 5 nM, which satisfies the maximum contaminant level defined by the United States EPA. This method was also validated successfully in DQ-spiked tap water. The SERS-based integrated sensing system is expected to be useful as an in-the-field sensing platform for fast and reproducible trace analysis of environmental pollutants in water.

We report the rapid and highly sensitive trace analysis of mercury(ii) ions in water using a surface-enhanced Raman scattering (SERS)-based microdroplet sensor. Aptamer-modified Au/Ag core-shell nanoparticles have been fabricated and utilized as highly functional sensing probes. All detection processes for the reaction between mercury(II) ions and aptamer-modified nanoparticles were performed in a specially designed microdroplet channel. Small water droplets that included sample reagents were separated from each other by an oil phase that continuously flowed along the channel. This two-phase liquid-liquid segmented flow system prevented the adsorption of aggregated colloids to the channel walls due to localized reagents within encapsulated droplets. The result was reduced residence time distributions. The limit of detection (LOD) of mercury(II) ions in water was determined by the SERS-based microdroplet sensor to be below 10 pM, which is three orders below the EPA-defined maximum contaminant level. This combination of a SERS-based microfluidic sensor with aptamer-based functional nanoprobes can be used for in-the-field sensing platforms, due to its size and simplicity.