Efficient Sorting Research Articles

Research on the dynamics of transitional regimes in an inertial screen with two motor vibrators

Purpose. Analysis of mathematical models of vibrating screens, using the theory of oscillations and mathematical modeling to study the impact of initial start-up parameters and the dynamics of transient processes on the operation of the screen. Methodology. This research is dedicated to the analysis of mathematical models that describe the startup process and subsequent operation of a vibratory screen. Methods of oscillation theory, system dynamics, and mathematical modeling are applied to analyze the vibratory processes of screens. Special attention is given to studying the impact of initial startup parameters on the operation of the screen, including amplitude, frequency, and phase of oscillations. Within the framework of this work, detailed graphs are constructed that reflect the displacement of different points of the structure, as well as the trajectories of their movement during operation, using the Mathcad software. Results. Transitional modes of startup and steady operation are analyzed, with motion dynamics equations for both phases detailed. Displacement graphs for key screen points over time and changes in screen movement across operational modes are highlighted. Scientific novelty. The study defines a mathematical model for the motion of a screen with two motor-vibrators during startup and steady operation. It introduces a graph showing the screen points' displacement over time, from startup to steady mode, and illustrates the trajectories of these points under various operational modes. This research advances material classification techniques, enhancing process optimization and equipment efficiency. Practical significance. This research enhances vibratory material sorting by analyzing screen dynamics across operating modes, leading to optimized workflows, lower energy use, and improved sorting efficiency. It also bolsters equipment reliability and reduces the risk of failures, cutting down on maintenance and repair expenses.

Magnetic sorting of circulating tumor cells based on different-level antibody expression using a divergent serpentine microchannel

The privilege of early cancer detection is entangled with many people's lives. Thus, providing an efficient tool to analyze circulating tumor cells (CTCs) would be an immense asset. In this study, a CTC sorting mechanism is introduced by classifying CTCs based on their EpCAM (epithelial cell adhesion molecule) level, a cell surface marker applicable to cancer staging. For this purpose, an integrated microfluidic chip was designed to focus and separate CTCs from blood cells in a divergent serpentine microchannel, and simultaneously, sort CTCs into three categories [low-EpCAM (N1 = 406), medium-EpCAM (N2 = 693), and high-EpCAM (N3 = 1693)] using a magnetic field. The velocity optimizations, using finite element method (FEM), provided a focusing efficiency above ∼95% (for different cell lines) and a CTC separation efficiency above 92%, validated with our experimental results in the passive section. Furthermore, by optimizing the characteristics of the magnet (through several simulations), three optimum designs were achieved, featuring microchannel lengths of 5, 15, and 30 mm in the active section. The innovative approach introduced in this study integrates microfluidic chips, enabling a one-time, high-throughput process with a recovery rate exceeding 99%. This not only reduces costs and runtime but also enhances sorting efficiency for monitoring the stage of cancer progression.

Viscoelastic-Sorting Integrated Deformability Cytometer for High-Throughput Sorting and High-Precision Mechanical Phenotyping of Tumor Cells.

The counts and phenotypes of circulating tumor cells (CTCs) in whole blood are useful for disease monitoring and prognostic assessment of cancer. However, phenotyping CTCs in the blood is difficult due to the presence of a large number of background blood cells, especially some blood cells with features similar to those of tumor cells. Herein, we presented a viscoelastic-sorting integrated deformability cytometer (VSDC) for high-throughput label-free sorting and high-precision mechanical phenotyping of tumor cells. A sorting chip for removing large background blood cells and a detection chip for detecting multiple cellular mechanical properties were integrated into our VSDC. Our VSDC has a sorting efficiency and a purity of over 95% and over 81% for tumor cells, respectively. Furthermore, multiple mechanical parameters were used to distinguish tumor cells from white blood cells using machine learning. An accuracy of over 97% for identifying tumor cells was successfully achieved with the highest identification accuracy of 99.4% for MCF-7 cells. It is envisioned that our VSDC will open up new avenues for high-throughput and label-free single-cell analysis in various biomedical applications.

Knowledge Distillation for Enhancing a Lightweight Magnet Tile Target Detection Model: Leveraging Spatial Attention and Multi-Scale Output Features

Accurate and efficient sorting of diverse magnetic tiles during manufacturing is vital. However, challenges arise due to visual similarities among types, necessitating complex computer vision algorithms with large sizes and high computational needs. This impedes cost-effective deployment in the industry, resulting in the continued use of inefficient manual sorting. To address this issue, we propose an innovative lightweight magnetic tile detection approach that improves knowledge distillation for a compressed YOLOv5s model. Incorporating spatial attention modules into different feature extraction stages of YOLOv5s during the knowledge distillation process can enhance the ability of the compressed model to learn the knowledge of intermediate feature extraction layers from the original large model at different stages. Combining different outputs to form a multi-scale output, the multi-scale output feature in the knowledge refinement process enhances the capacity of the compressed model to grasp comprehensive target knowledge in outputs. Experimental results on our self-built magnetic tile dataset demonstrate significant achievements: 0.988 mean average precision, 0.5% discrepancy compared to the teacher’s network, and an 85% model size reduction. Moreover, a 36.70% boost in inference speed is observed for single image analysis. Our method’s effectiveness is also validated by the Pascal VOC dataset results, showing potential for broader target detection scenarios. This approach offers a solution to magnetic tile target detection challenges while being expected to expand to other applications.

Predicting TFe content and sorting iron ores from hyperspectral image by variational mode decomposition-based spectral feature

During iron ore processing, accurately predicting the total iron (TFe) content in the ore is important for highly efficient and accurate sorting, which can reduce dust pollution, energy consumption, and economic losses, and promote waste utilization. Traditional techniques have limitations in accurately predicting the TFe content of individual iron ores, whereas hyperspectral imaging (HSI) has the potential to fulfill this task. Therefore, this paper explored it. For the experimental materials, 150 high-grade and 150 low-grade ore samples with particle sizes ranging from 20 to 40 mm were prepared. Subsequently, hyperspectral images of these samples were acquired within the range of 953–2517 nm, and the TFe content of each ore sample was measured. After preprocessing, the spectral features were extracted from the mean spectra of the iron ore, based on the analysis of the ore samples using variational mode decomposition (VMD). Subsequently, a random forest (RF) model based on these features was proposed to predict TFe content of the ores, and further sorted each individual piece of iron ore. Based on the evaluations, the prediction coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and classification accuracy of the proposed model, VMD Feature-random forest (VMD-RF), were 0.94, 0.07, 0.03, and 0.91, respectively. The results proved the feasibility of accurately sorting each individual iron ore based on its predicted TFe content using HSI. This methodology offers technical support for a cleaner iron ore production.

Recent advances in recycling technologies for waste textile fabrics: a review

The fast fashion trends in the textile industry have resulted in high consumption of fiber with concomitant generation of waste. Awareness of environmental pollution resulting from textile production and disposal has increased significantly. This increase has pushed research activities toward more sustainable recycling alternatives to properly handle the end-of-life of textiles. This review provides an overview of existing technologies, the latest developments, and research studies on the recycling technologies employed in the textile industry. Different types of recycling—mechanical, chemical, and biochemical recycling of standard fabrics used in garments, cotton, wool, polyester, polyamide 6 6, and acrylic—are explored. Recent advances in recycling technologies such as advanced sorting techniques, innovative chemical processing, and emerging biochemical processes are revealed. The review also highlights efforts being made by various agencies and companies in facilitating and employing the technologies on a commercial scale. Various methods for efficient textile waste sorting and identification are also discussed. The reviewed studies revealed that most recycling technologies were conducted on post-industrial textile waste, which tends to be homogenous in the types of dyes and fibers present in the waste. It also suggested that post-consumer textiles could be recycled using chemical and biological options that have the potential to valorize the waste into high-value products.

Augmented reality-enabled human-robot collaboration to balance construction waste sorting efficiency and occupational safety and health

Construction waste sorting (CWS) is highly recommended as a key step for construction waste management. However, current CWS involves humans’ manual hand-picking, which poses significant threats to their occupational safety and health (OSH). Robotic sorting promises to change the situation by adopting modern artificial intelligence and automation technologies. However, in practice, it is usually challenging for robots to do an efficient job (e.g., measured by quickness and accuracy) owing to the difficulties in precisely recognizing compositions of the mixed and heterogeneous waste stream. Leveraging augmented reality (AR) as a communication interface, this research aims to develop a human-robot collaboration (HRC) approach to address the dilemmatic balance between CWS efficiency and OSH. Firstly, a model for human-robot collaborative sorting using AR is established. Then, a prototype for the AR-enable collaborative sorting system is developed and evaluated. The experimental results demonstrate that the proposed AR-enabled HRC method can improve the accuracy rate of CWS by 10% and 15% for sorting isolated waste and obscured waste, respectively, when compared to the method without human involvement. Interview results indicate a significant improvement in OSH, especially the reduction of contamination risks and machinery risks. The research lays out a human-robot collaborative paradigm for productive and safe CWS via an immersive and interactive interface like AR.

Two-component polymer sorting to obtain high-purity s-SWCNTs for all-carbon photodetectors.

The advancement of carbon-based electronics is reliant on the development of semiconducting carbon nanotubes with high purity and yield. We developed a new extraction strategy to efficiently sort SWCNTs with superior yields and purity. The approach uses two polymers, poly[N-(1-octylnonyl)-9H-carbazol-2,7-diyl](PCz) and poly(9,9-n-dihexyl-2,7-fluorene-alt-9-phenyl-3,6-carbazole)(PDFP), and two sonication processes to eliminate surface polymer contamination. PCz selectively wraps large-diameter s-SWCNTs, with PDFP added as an enhancing molecule to increase sorting efficiency at 4-fold compared to the efficiency of only PCz alone sorting. The purity of the sorted s-SWCNTs was confirmed to be above 99 % using absorption and Raman spectra. Field-effect transistors and photodetectors made from the sorted s-SWCNTs exhibited excellent semiconductor properties and broad-spectrum detection, with good long-term stability. Furthermore, a photodetector using large-tube diameter s-SWCNTs achieved broad-spectrum detection, which the photoresponsivity is 0.35 mA/W and the detectivity is 4.7×106 Jones. The s-SWCNTs/graphene heterojunction photodetector achieved a photoresponsivity of 3 mA/W and a detectivity of 6.3×106 Jones. This new strategy provides a promising approach to obtain high-purity and high-yield s-SWCNTs for carbon-based photodetectors.

Vector vortex beams sorting of 120 modes in visible spectrum.

Polarization (P), angular index (l), and radius index (p) are three independent degrees of freedom (DoFs) of vector vortex beams, which have found extensive applications in various domains. While efficient sorting of a single DoF has been achieved successfully, simultaneous sorting of all these DoFs in a compact and efficient manner remains a challenge. In this study, we propose a beam sorter that simultaneously handles all the three DoFs using a diffractive deep neural network (D2NN), and demonstrate the robust sorting of 120 Laguerre-Gaussian (LG) modes experimentally in the visible spectrum. Our proposed beam sorter underscores the considerable potential of D2NN in optical field manipulation and promises to enhance the diverse applications of vector vortex beams.

Mine waste rock reprocessing using sensor-based sorting (SBS): Novel approach toward circular economy in phosphate mining

Phosphate extraction and processing generates large volumes of phosphate mine waste rock (PMWR) that still contains non-negligible amounts of residual phosphate. Due to the increasing demand for phosphorous (P), in particular by the fertilizer industry, PMWR can become a potential resource for P beneficiation if cost-effective methods are applied. The main objectives of this study are to:i)characterise PMWR from the Ben Guerir mine site (Morocco);ii)evaluate the efficiency of hand- and sensor-based sorting (SBS) to recover the lost phosphate; and.iii)upgrade the P2O5 content in the concentrate.Samples were collected from different locations in the PMWR piles and divided into two main fractions: >30 mm and < 30 mm. The coarse fraction (>30 mm) was sorted using three main SBS technologies to separate phosphate from gangue materials: dual energy X-ray transmission (DE-XRT), near-infrared combined with color (NIR/color), and color technologies. The fine fraction was processed with the conventional flowsheet used by the mine.Chemical and mineralogical characterisation showed that the composite sample contained up to 17.5 wt% P2O5, with an abundance of fluorapatite, calcite, quartz, and dolomite. The hand sorting demonstrated that >50 wt% of the PMWR within the coarse fraction (>30 mm) was in the form of indured phosphate (IndP). The remaining lithologies were represented by phosphated flint, flint, silexite, limestone, and marl. In terms of sorting efficiency, the tests highlighted the performance of DE-XRT for materials characterised with a difference in atomic density. The use of DE-XRT allowed a P2O5 recovery of 70 wt%, with an increase of the P2O5 content from 13.5 wt% to 18.5 wt%. The remaining lithologies were successfully separated using SBS technologies. Materials sorting is an interesting technology that can reduce the environmental footprint of PMWR by recovering the lost phosphate and valorising the remaining lithologies for civil engineering applications.

Research on coal gangue recognition method based on XBS-YOLOv5s

Aiming at the problems of misdetection, omission and low recognition accuracy of coal gangue recognition due to the harsh environmental factors such as low illumination, motion blur and large quantities of coal gangue mixing in coal mines, a coal gangue recognition method based on XBS-YOLOv5s is proposed. Simulate the actual underground production environment to build a machine vision platform, construct a coal gangue image data set, and provide a test environment for various target detection algorithms. In this paper, we construct a real-time detection model of coal gangue in the complex environment of coal mine by fusing SimAM parameter-free attention mechanism, BiFPN feature fusion network and XIoU loss function in YOLOv5s, so as to improve the model’s ability of extracting, fusing and localizing key features of the target. The experimental results show that the recognition accuracy of XBS-YOLOv5s algorithm for coal gangue in the complex environment of low illumination, motion blur and large quantities of coal gangue mixed are effectively improved. Its mean average precision reaches 96%, which is 4.3% higher than the original YOLOv5s algorithm, meanwhile, compared with other YOLO series algorithms, it has the best comprehensive detection performance, which can provide technical support for intelligent and efficient sorting of coal gangue.

IOT COLOR BASED PRODUCT SORTING MACHINE

The IoT Color-Based Product Sorting Machine is a cutting-edge automated system designed to revolutionize the manufacturing and packaging industries. This innovative solution employs Internet of Things (IoT) technology to enable efficient and precise sorting of products based on their color attributes. The system integrates advanced image recognition algorithms, sensor networks, and cloud computing to streamline the sorting process. Color Based Object Sorting has a wide usage in fruit sorting as well as candy sorting industries. This system puts forward a mechanism to detect color and sort items through image processing. Once identified a mechanism is used to sort the candies into particular bins baskets. We here demonstrate this mechanism using a camera with electronic circuitry along with sorting mechanism using 3 bins. The system uses raspberry pi connected to a controller circuit to achieve this task. The controller circuit consists of a camera attached to it that detects color of a small object in front of it. A motor is used to feed an object to the camera chamber. As soon is the color is detected a signal is sent to the sorter mechanism which uses a motor to position the sorting tube towards respective section. A feeder is then used to push the object towards the tubs so that it gets sorted and next object is pulled in by the feeder. The action details are sent to the IOT server using iotgecko platform to keep track of the number of objects sorted in each section. Thus we achieve a completely automated IOT based sorting system.Color based product sorting has a wide usagein fruits sorting as well as candy sorting industries. Now a day’s industrial area requires demand for automation. Due to automation human efforts are goes on decreasing day-byday. Since last decade .This system puts forward mechanism to detect color and sort item through image processing. This mechanism is used to sort the candies into particular bins or baskets Key Words: IoT, colour sensor, servo motor, node MCU,,

Flow cytometry analysis of protein expression using antibody-derived tags followed by CITE-Seq.

Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) is a single-cell phenotyping method that uses antibody-derived tags (ADTs) to quantitatively detect cell surface protein expression and generate transcriptomic data at the single-cell level. Despite the increased popularity of this technique to study cellular heterogeneity and dynamics, detailed methods on how to choose ADT markers and ensuring reagent performance in biological relevant systems prior to sequencing is not available. Here we describe a novel and easy-to-use multiplex flow proxy assay in which multiple protein markers can be measured simultaneously using a combination of ADT reagents and dye-oligo conjugates by flow cytometry. Using dye-oligo conjugates with sequences complementary to the ADT reagents, we can achieve specific binding and evaluate protein marker expression in a multiplex way. This quality control assay is useful for guiding ADT marker choice and confirming protein expression prior to sequencing. Importantly, the labeled cells can be directly isolated based on the specific fluorescence from dye-oligo conjugates using a flow cytometry cell sorter and processed for downstream single-cell multiomics. Using this streamlined workflow, we sorted natural killer cells and T cells efficiently using only ADT and dye-oligo reagents, avoiding the possibility of decreased marker resolution from co-staining cells with ADT and fluorescent antibodies. This novel workflow provides a viable option for improving ADT marker choice and cell sorting efficiency, allowing subsequent CITE-Seq.

Machine-learning-assisted classification of construction and demolition waste fragments using computer vision: Convolution versus extraction of selected features

Improper sorting of construction and demolition waste (CDW) leads to significant environmental and economic implications, including inefficient resource use and missed recycling opportunities. To address this, we developed a machine-learning-assisted procedure for recognizing CDW fragments using an RGB camera. Our approach uniquely leverages selected feature extraction, enhancing classification speed and accuracy. We employed three classifiers: convolutional neural network (CNN), gradient boosting (GB) decision trees, and multi-layer perception (MLP). Notably, our method’s extraction of selected features for GB and MLP outperformed the traditional CNN in terms of speed and accuracy, especially for challenging samples with similar textures. Specifically, while convolution resulted in an overall accuracy of 85.9%, our innovative feature extraction approach yielded accuracies up to 92.3%. This study’s findings have significant implications for the future of CDW management, offering a pathway for efficient and accurate waste sorting, fostering sustainable resource use, and reducing the environmental impact of CDW disposal. Supplementary materials, including datasets, codes, and models, are provided, promoting transparency and reproducibility.

The scaling of mental computation in a sorting task

Many cognitive models provide valuable insights into human behavior. Yet the algorithmic complexity of candidate models can fail to capture how human reaction times scale with increasing input complexity. In the current work, we investigate the algorithms underlying human cognitive processes. Computer science characterizes algorithms by their time and space complexity scaling with problem size. We propose to use participants’ reaction times to study how human computations scale with increasing input complexity. We tested this approach in a task where participants had to sort sequences of rectangles by their size. Our results showed that reaction times scaled close to linearly with sequence length and that participants learned and actively used latent structure whenever it was provided. This behavior was in line with a computational model that used the observed sequences to form hypotheses about the latent structures, searching through candidate hypotheses in a directed fashion. These results enrich our understanding of plausible cognitive models for efficient mental sorting and pave the way for future studies using reaction times to investigate the scaling of mental computations across psychological domains.

A High-Throughput Microfluidic Cell Sorter Using a Three-Dimensional Coupled Hydrodynamic-Dielectrophoretic Pre-Focusing Module.

Dielectrophoresis (DEP) is a powerful tool for label-free sorting of cells, even those with subtle differences in morphological and dielectric properties. Nevertheless, a major limitation is that most existing DEP techniques can efficiently sort cells only at low throughputs (<1 mL h-1). Here, we demonstrate that the integration of a three-dimensional (3D) coupled hydrodynamic-DEP cell pre-focusing module upstream of the main DEP sorting region enables cell sorting with a 10-fold increase in throughput compared to conventional DEP approaches. To better understand the key principles and requirements for high-throughput cell separation, we present a comprehensive theoretical model to study the scaling of hydrodynamic and electrostatic forces on cells at high flow rate regimes. Based on the model, we show that the critical cell-to-electrode distance needs to be ≤10 µm for efficient cell sorting in our proposed microfluidic platform, especially at flow rates ≥ 1 mL h-1. Based on those findings, a computational fluid dynamics model and particle tracking analysis were developed to find optimum operation parameters (e.g., flow rate ratios and electric fields) of the coupled hydrodynamic-DEP 3D focusing module. Using these optimum parameters, we experimentally demonstrate live/dead K562 cell sorting at rates as high as 10 mL h-1 (>150,000 cells min-1) with 90% separation purity, 85% cell recovery, and no negative impact on cell viability.

An introduction to artificial intelligence in machine vision for postharvest detection of disorders in horticultural products

Horticultural products may suffer from surface, subsurface, and internal disorders. These lower the market value and may lead to consumer dissatisfaction. Today’s postharvest industry has a high interest in implementing technologies that allow efficient sorting of disordered products to avoid food loss, and to improve market value and consumer appreciation. Inspection by machine vision using RGB imaging and more advanced techniques such as spectral cameras, X-ray and magnetic resonance imaging (MRI), is increasingly implemented to non-destructively analyze disorders in horticultural products. However, for objective, robust and reliable detection of symptoms of disorders that are often even not obvious to detect by human vision, powerful data analysis methods are essential. In recent years, image analysis by artificial intelligence (AI) has evolved from traditional machine learning (ML) to advanced deep learning (DL), which is reaching impressive detection abilities. The aim of this article is to introduce recent developments in AI for extracting information on postharvest disorders from complex image data obtained by modern imaging systems to the non-AI expert. The main progress in AI for postharvest applications is the increasing adoption of available convolutional neural networks (CNNs), usually developed and tested with large open source image datasets elsewhere, to postharvest image data. Challenges in the design of DL models are identified, such as the need for large quantities of (labeled) data, the model explainability, and generalizability. Although proposed innovations often result in excellent detection abilities, their usability in an industrial context should be further considered, where uncontrollable parameters may negatively impact the performance.

Cell-specific secretory granule sorting mechanisms: the role of MAGEL2 and retromer in hypothalamic regulated secretion.

Intracellular protein trafficking and sorting are extremely arduous in endocrine and neuroendocrine cells, which synthesize and secrete on-demand substantial quantities of proteins. To ensure that neuroendocrine secretion operates correctly, each step in the secretion pathways is tightly regulated and coordinated both spatially and temporally. At the trans-Golgi network (TGN), intrinsic structural features of proteins and several sorting mechanisms and distinct signals direct newly synthesized proteins into proper membrane vesicles that enter either constitutive or regulated secretion pathways. Furthermore, this anterograde transport is counterbalanced by retrograde transport, which not only maintains membrane homeostasis but also recycles various proteins that function in the sorting of secretory cargo, formation of transport intermediates, or retrieval of resident proteins of secretory organelles. The retromer complex recycles proteins from the endocytic pathway back to the plasma membrane or TGN and was recently identified as a critical player in regulated secretion in the hypothalamus. Furthermore, melanoma antigen protein L2 (MAGEL2) was discovered to act as a tissue-specific regulator of the retromer-dependent endosomal protein recycling pathway and, by doing so, ensures proper secretory granule formation and maturation. MAGEL2 is a mammalian-specific and maternally imprinted gene implicated in Prader-Willi and Schaaf-Yang neurodevelopmental syndromes. In this review, we will briefly discuss the current understanding of the regulated secretion pathway, encompassing anterograde and retrograde traffic. Although our understanding of the retrograde trafficking and sorting in regulated secretion is not yet complete, we will review recent insights into the molecular role of MAGEL2 in hypothalamic neuroendocrine secretion and how its dysregulation contributes to the symptoms of Prader-Willi and Schaaf-Yang patients. Given that the activation of many secreted proteins occurs after they enter secretory granules, modulation of the sorting efficiency in a tissue-specific manner may represent an evolutionary adaptation to environmental cues.

Fast sorting method of retired batteries based on multi-feature extraction from partial charging segment

The second-life utilization of retired power batteries presents promising opportunities; however, the substantial upfront testing costs and subsequent maintenance expenses have hindered its widespread adoption. A key challenge is achieving a balance between accurate and comprehensive sorting outcomes and minimizing the costs associated with the detection process. Existing methods have primarily focused on exploring cost-effective hidden indicators with stronger representativeness or leveraging algorithms to extract low-cost latent features that correlate with the required performance parameters for sorting. Hence, we developed a rapid sorting method based on extracting multiple features from partial charge intervals. Through a comprehensive analysis of correlations and cost considerations, five features that can be extracted from the same partial charging segment are selected as classification criteria. Meanwhile, these features exhibit different tendencies toward the three main battery characteristics: capacity, internal resistance, and voltage. The proposed method utilizes a self-organizing map algorithm and subtractive clustering to achieve efficient sorting. In comparison to traditional sorting methods, the detection time is reduced to 19.96%, and energy consumption is decreased to 20.35%. By improving sorting efficiency and reducing costs while maintaining sorting effectiveness, the proposed method enhances the economic feasibility of hierarchical utilization, which may contribute to the advancement of second-life utilization of retired batteries.

Precise size sorting of nanoparticles by bound states in the continuum in a dual finite grating.

We consider two parallel dielectric gratings (dual grating) which support accidental bound states in the continuum (BICs) mostly localized between gratings. As distinctive to true periodical BICs in an infinite dual grating, the enveloping intensity of quasi-BICs in a finite dual grating behaves as a standing wave. That behavior is a key property to trap nanoparticles into selected cells of the dual grating sorted by sizes of nanoparticles dragged by liquid flowing between gratings. For excitation of quasi-BIC with high quality factor by an electromagnetic plane wave with normal incidence and power 1 mW/µm2 we show high efficiency of sorting of nanoparticles by sizes.