Modulation of nanoparticle separation by initial contact angle in coffee ring effect

Abstract The coffee ring effect occurs when a droplet of a suspension evaporates on a substrate; this process can separate suspended nanoparticles (NPs) by size as a result of geometric constraints at the contact line of the evaporating droplet. In the study, we used a polydimethylsiloxane (PDMS) stamp to make an even contact line, and we changed the contact angle θ of the droplet by selectively configuring hydrophilic and hydrophobic surfaces. In experiments, the temperature, relative humidity were held constant and glass was used as substrate. When the initial θ of the droplet was changed by using the PDMS stamp to coat the glass, NP separation was governed by θ, not by droplet volume VD. When droplets had different initial θ but the same VD, the NP separation in the droplet was ~ 8 µm at θ = 50°, ~ 10 µm at θ = 30°, and ~ 16 µm at θ = 14°. This ability to increase the separation between particles by changing the initial θ of the evaporating droplet may allow clear separation of NPs in evaporating droplets.https://deepblue.lib.umich.edu/bitstream/2027.42/146760/1/40486_2018_Article_79.pd

Extension of Zero Voltage Switching Capability for CLLC Resonant Converter

TheCLLC resonant converter has been widely used to obtaina high power conversion efficiency with sinusoidal current waveforms and a soft switching capability. However, it has a limited voltage gain range according to the input voltage variation. The current-fed structure canbe one solution to extend the voltage gain range for the wide input voltage variation, butit has a limited zero voltage switching (ZVS) range. In this paper, the current-fed CLLC resonant converter with additional inductance is proposed to extend the ZVS range. The operational principle is analyzed to design the additional inductance for obtaining the extended ZVS range. The design methodology of the additional inductance is proposed to maximize the ZVS capability for the entire load range. The performance of the proposed method is verified with a 20 W prototype converter

Design Methodology of Tightly Regulated Dual-Output LLC Resonant Converter Using PFM-APWM Hybrid Control Method

A dual-output LLC resonant converter using pulse frequency modulation (PFM) and asymmetrical pulse width modulation (APWM) can achieve tight output voltage regulation, high power density, and high cost-effectiveness. However, an improper resonant tank design cannot achieve tight cross regulation of the dual-output channels at the worst-case load conditions. In addition, proper magnetizing inductance is required to achieve zero voltage switching (ZVS) of the power MOSFETs in the LLC resonant converter. In this paper, voltage gain of modulation methods and steady state operations are analyzed to implement the hybrid control method. In addition, the operation of the hybrid control algorithm is analyzed to achieve tight cross regulation performance. From this analysis, the design methodology of the resonant tank and the magnetizing inductance are proposed to compensate the output error of both outputs and to achieve ZVS over the entire load range. The cross regulation performance is verified with simulation and experimental results using a 190 W prototype converter

Real-Time State-of-Charge Estimation Using an Embedded Board for Li-Ion Batteries

With the use of batteries increases, the complexity of battery management systems (BMSs) also rises. Thus, assessing the functionality of BMSs and performance of the BMS hardware is of utmost importance. Testing with embedded boards at an early stage of BMS development is a pragmatic approach for developing a BMS because it is cost- and time-efficient and considers hardware performance. In this study, we tested and analyzed the real-time state-of-charge (SOC) estimation using a test platform with limited CPU performance as well as memory resources of the embedded board. We collected battery data on a single-cell basis using a first-order RC equivalent circuit and achieved an accuracy of 95% compared to the measured data obtained using actual battery tests. The SOC estimation method applies the extended Kalman filter (EKF) and unscented Kalman filter (UKF). The experiment was performed on the real-time test platform, with 1%, 2%, and 5% noise in the measurement data. The algorithm complexity and hardware implementation were evaluated in terms of the resources used and processing speed. Although the EKF is cost-effective, its error rate increases by 5% with noise interference. The UKF exhibits high accuracy and noise robustness; however, it has a high resource occupancy

SAFP-YOLO: Enhanced Object Detection Speed Using Spatial Attention-Based Filter Pruning

Because object detection accuracy has significantly improved advancements in deep learning techniques, many real-time applications have applied one-stage detectors, such as You Only Look Once (YOLO), owing to their fast execution speed and accuracy. However, for a practical deployment, the deployment cost should be considered. In this paper, a method for pruning the unimportant filters of YOLO is proposed to satisfy the real-time requirements of a low-cost embedded board. Attention mechanisms have been widely used to improve the accuracy of deep learning models. However, the proposed method uses spatial attention to improve the execution speed of YOLO by evaluating the importance of each YOLO filter. The feature maps before and after spatial attention are compared, and then the unimportant filters of YOLO can be pruned based on this comparison. To the best of our knowledge, this is the first report considering both accuracy and speed with Spatial Attention-based Filter Pruning (SAFP) for lightweight object detectors. To demonstrate the effectiveness of the proposed method, it was applied to the YOLOv4 and YOLOv7 baseline models. With the pig (baseline YOLOv4 84.4%@3.9FPS vs. proposed SAFP-YOLO 78.6%@20.9FPS) and vehicle (baseline YOLOv7 81.8%@3.8FPS vs. proposed SAFP-YOLO 75.7%@20.0FPS) datasets, the proposed method significantly improved the execution speed of YOLOv4 and YOLOv7 (i.e., by a factor of five) on a low-cost embedded board, TX-2, with acceptable accuracy

The robustness of T2 value as a trabecular structural index at multiple spatial resolutions of 7 Tesla MRI

Purpose: To evaluate the robustness of MR transverse relaxation times of trabecular bone from spin-echo and gradient-echo acquisitions at multiple spatial resolutions of 7 T. Methods: The effects of MRI resolutions to T-2 and T-2* trabecular bone were numerically evaluated by Monte Carlo simulations. T-2, T-2* and trabecular structural indices from multislice multi-echo and UTE acquisitions were measured in defatted human distal femoral condyles on a 7 T scanner. Reference structural indices were extracted from high-resolution microcomputed tomography images. For bovine knee trabecular samples with intact bone marrow, T-2 and T-2* were measured by degrading spatial resolutions on a 7 T system. Results: In the defatted trabecular experiment, both T-2 and T-2* values showed strong (vertical bar r vertical bar> 0.80) correlations with trabecular spacing and number, at a high spatial resolution of 125 mu m(3). The correlations for MR image-segmentation-olutions of 250 and 500 mu m(3). The correlations for T-2* rapidly dropped (vertical bar r vertical bar< 0.50) at a spatial resolution of 500 mu m(3), whereas those for T-2* remained consistently high (vertical bar r vertical bar> 0.85). In the bovine trabecular experiments with intact marrow, low- resolution (approximately 1 mm(3), 2 minutes) T-2 values did not shorten (vertical bar r vertical bar> 0.95 with respect to approximately 0.4mm(3), 11 minutes) and maintained consistent correlations (vertical bar r vertical bar > 0.70) with respect to trabecular spacing (turbo spin echo, 22.5 minutes). Conclusion: T-2 measurements of trabeculae at 7 T are robust with degrading spatial resolution and may be preferable in assessing trabecular spacing index with reduced scan time, when high-resolution 3D micro-MRI is difficult to obtain

StaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility Information

The automatic detection of individual pigs can improve the overall management of pig farms. The accuracy of single-image object detection has significantly improved over the years with advancements in deep learning techniques. However, differences in pig sizes and complex structures within pig pen of a commercial pig farm, such as feeding facilities, present challenges to the detection accuracy for pig monitoring. To implement such detection in practice, the differences should be analyzed by video recorded from a static camera. To accurately detect individual pigs that may be different in size or occluded by complex structures, we present a deep-learning-based object detection method utilizing generated background and facility information from image sequences (i.e., video) recorded from a static camera, which contain relevant information. As all images are preprocessed to reduce differences in pig sizes. We then used the extracted background and facility information to create different combinations of gray images. Finally, these images are combined into different combinations of three-channel composite images, which are used as training datasets to improve detection accuracy. Using the proposed method as a component of image processing improved overall accuracy from 84% to 94%. From the study, an accurate facility and background image was able to be generated after updating for a long time that helped detection accuracy. For the further studies, improving detection accuracy on overlapping pigs can also be considered

Analysis of Extracellular Vesicles Using Coffee Ring

Extracellular vesicles are categorized in subsets according to their biogenesis processes. To facilitate the investigation of subsets, an effective method is needed for isolating subpopulations. The efficacy of existing density and size-based isolation methods is limited, and as a result, the correlation of properties within separated subpopulations is modest. Here, we introduced size separation with ∼48 nm resolution that exploits Marangoni flow and the coffee-ring effect in microdroplets in which extracellular vesicles are spatially deposited at different location according to size of extracellular vesicle. Interestingly, the analysis of tetraspanin proteins of the extracellular vesicles facilitated by this method reveals that the size of extracellular vesicles is correlated with expression of tetraspanin proteins (CD9, CD63, CD81) that are associated with the size of extracellular vesicles. The findings show that CD9 and CD81 are uniformly expressed regardless of size, CD63 is highly expressed only in larger extracellular vesicles. This evidence indicates that extracellular vesicles can be classified based on size and expression of CD63