Combining Poiseuille flow with an external electric field is a demonstrated method to drive transverse migration in capillary electrophoresis. Despite both computational and experimental studies, a number of questions about how to best model polymers under these conditions remains. Attempts have been made to develop a kinetic theory for a bead-spring dumbbell model, but these have only been accurate at low electric field strength and have not captured the nonmonotonic relationship between migration and electric field strength. In this paper, we revisit the development of a kinetic theory for a bead-spring dumbbell in a combination of parabolic flow and an external electric field. The resultant theory yields a compact formula that predicts polymer concentration profiles that agree excellently with our Brownian dynamics simulations including the aforementioned nonmonotonic relationship. Furthermore, we compare our theoretical results to experimental data and find that our model nearly quantitatively predicts the position of the maximum in migration.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1103/PhysRevE.100.052501 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Knowledge Reasoning- and Progressive Distillation-Integrated Detection of Electrical Construction Violations.
Sensors (Basel)
December 2024
Department of Automation, North China Electric Power University, Baoding 071003, China.
To address the difficulty in detecting workers' violation behaviors in electric power construction scenarios, this paper proposes an innovative method that integrates knowledge reasoning and progressive multi-level distillation techniques. First, standards, norms, and guidelines in the field of electric power construction are collected to build a comprehensive knowledge graph, aiming to provide accurate knowledge representation and normative analysis. Then, the knowledge graph is combined with the object-detection model in the form of triplets, where detected objects and their interactions are represented as subject-predicate-object relationship.
View Article and Find Full Text PDFA Scene Knowledge Integrating Network for Transmission Line Multi-Fitting Detection.
Sensors (Basel)
December 2024
Automation Department, North China Electric Power University, Baoding 071003, China.
Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings.
View Article and Find Full Text PDFInitial Pose Estimation Method for Robust LiDAR-Inertial Calibration and Mapping.
Sensors (Basel)
December 2024
Department of Intelligent Systems & Robotics, Chungbuk National University, Cheongju 28644, Republic of Korea.
Handheld LiDAR scanners, which typically consist of a LiDAR sensor, Inertial Measurement Unit, and processor, enable data capture while moving, offering flexibility for various applications, including indoor and outdoor 3D mapping in fields such as architecture and civil engineering. Unlike fixed LiDAR systems, handheld devices allow data collection from different angles, but this mobility introduces challenges in data quality, particularly when initial calibration between sensors is not precise. Accurate LiDAR-IMU calibration, essential for mapping accuracy in Simultaneous Localization and Mapping applications, involves precise alignment of the sensors' extrinsic parameters.
View Article and Find Full Text PDFStructural Design and Electromagnetic Performance Analysis of Octupole Active Radial Magnetic Bearing.
Sensors (Basel)
December 2024
Zhejiang Institute of Mechanical & Electrical Engineering Co., Ltd., Hangzhou 310051, China.
This study addresses the challenges of magnetic circuit coupling and control complexity in active radial magnetic bearings (ARMBs) by systematically investigating the electromagnetic performance of four magnetic pole configurations (NNSS, NSNS, NNNN, and SSSS). Initially, equivalent magnetic circuit modeling and finite element analysis (FEA) were employed to analyze the magnetic circuit coupling phenomena and their effects on the magnetic flux density distribution for each configuration. Subsequently, the air gap flux density and electromagnetic force were quantified under rotor eccentricity caused by unbalanced disturbances, and the dynamic performances of the ARMBs were evaluated for eccentricity along the x-axis and at 45°.
View Article and Find Full Text PDFDSiam-CnK: A CBAM- and KCF-Enabled Deep Siamese Region Proposal Network for Human Tracking in Dynamic and Occluded Scenes.
Sensors (Basel)
December 2024
Shanghai Research Institute of Microelectronics, Peking University, Shanghai 201203, China.
Despite the accuracy and robustness attained in the field of object tracking, algorithms based on Siamese neural networks often over-rely on information from the initial frame, neglecting necessary updates to the template; furthermore, in prolonged tracking situations, such methodologies encounter challenges in efficiently addressing issues such as complete occlusion or instances where the target exits the frame. To tackle these issues, this study enhances the SiamRPN algorithm by integrating the convolutional block attention module (CBAM), which enhances spatial channel attention. Additionally, it integrates the kernelized correlation filters (KCFs) for enhanced feature template representation.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!