Electrospray cooling for microelectronics Deng, Weiwei; Gomez, Alessandro
International journal of heat and mass transfer,
05/2011, Volume:
54, Issue:
11
Journal Article
Peer reviewed
The challenge of effectively removing high heat flux from microelectronic chips may hinder future advancements in the semiconductor industry. Spray cooling is a promising solution to dissipate high ...heat flux, but traditional sprays suffer from low cooling efficiency partly because of droplet rebound. Here we show that electrosprays provide highly efficient cooling by completely avoiding the droplet rebound, when the electrically charged droplets are pinned on the heated conducting surface by the electric image force. We demonstrate a cooling system consisting of microfabricated multiplexed electrosprays in the cone-jet mode generating electrically charged microdroplets that remove a heat flux of 96
W/cm
2 with a cooling efficiency reaching 97%. Scale-up considerations suggest that the electrospray approach is well suited for practical applications by increasing the level of multiplexing and by preserving the system compactness using microfabrication.
Patent transfer is a common practice for companies to obtain competitive advantages. However, they encounter the difficulty of selecting suitable patents because the number of patents is increasingly ...large. Many patent recommendation methods have been proposed to ease the difficulty, but they ignore patent quality and cannot explain why certain patents are recommended. Patent quality and recommendation explanations affect companies' decision-making in the patent transfer context. Failing to consider them in the recommendation process leads to less effective recommendation results. To fill these gaps, this paper proposes an interpretable patent recommendation method based on knowledge graph and deep learning. The proposed method organizes heterogeneous patent information as a knowledge graph. Then it extracts connectivity and quality features from the knowledge graph for pairs of patents and companies. The former features indicate the relevance of the pairs while the latter features reflect the quality of the patents. Based on the features, we design an interpretable recommendation model by combining a deep neural network with a relevance propagation technique. We conduct experiments with real-world data to evaluate the proposed method. Recommendation lists with varying lengths show that the average precision, recall, and mean average precision of the proposed method are 0.596, 0.636, and 0.584, which improve corresponding performance of best baselines by 7.28%, 18.35%, and 8.60%, respectively. Besides, our method interprets recommendation results by identifying important features leading to the results.
Communities of interest promote knowledge sharing and discovery in social network platforms. However, platform users face difficulties of finding suitable communities, given their increasing number. ...Although recommendations have been proposed to help users find communities of interest, these methods ignore or exclude heterogeneous interactions between users and communities. In addition, widely used meta-paths help capture the complex semantic relation among entities but heavily rely on domain knowledge. In this study, we propose a novel recommendation model based on informative meta-path discovery in heterogeneous information networks and deep learning. Users, communities, relevant items and their relations are considered as entities in a heterogeneous information network, from where informative meta-paths are extracted on the basis of information theory to measure user-community similarities. Finally, similarities are incorporated in a deep learning model to predict whether target users join candidate communities. The proposed recommendation model is evaluated and compared against baseline methods using two data sets. Results demonstrate the superior performance of the present model in terms of precision, recall and F score.
Motor neuron death is supposed to result in primary motor cortex atrophy after spinal cord injury (SCI), which is relevant to poorer motor recovery for patients with SCI. However, the exact ...mechanisms of motor neuron death remain elusive. Here, we demonstrated that iron deposition in the motor cortex was significantly increased in both SCI patients and rats, which triggered the accumulation of lipid reactive oxygen species (ROS) and resulted in motor neuronal ferroptosis ultimately. While iron chelator, ROS inhibitor and ferroptosis inhibitor reduced iron overload-induced motor neuron death and promoted motor functional recovery. Further, we found that activated microglia in the motor cortex following SCI secreted abundant nitric oxide (NO), which regulated cellular iron homeostasis-related proteins to induce iron overload in motor neurons. Thus, we conclude that microglial activation induced iron overload in the motor cortex after SCI triggered motor neuronal ferroptosis and impeded motor functional recovery. These findings might provide novel therapeutic strategies for SCI.
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•SCI induces iron overload in the motor cortex.•Iron overload after SCI induces lipid peroxidation, thus triggers neuronal ferroptosis.•Activated microglia in M1 secrete superfluous NO to disturb iron metabolism after SCI.
There is an emerging interest of applying magnetic resonance imaging (MRI) to radiotherapy (RT) due to its superior soft tissue contrast for accurate target delineation as well as functional ...information for evaluating treatment response. MRI-based RT planning has great potential to enable dose escalation to tumors while reducing toxicities to surrounding normal tissues in RT treatments of nasopharyngeal carcinoma (NPC). Our study aims to generate synthetic CT from T2-weighted MRI using a deep learning algorithm.
Thirty-three NPC patients were retrospectively selected for this study with local IRB's approval. All patients underwent clinical CT simulation and 1.5T MRI within the same week in our hospital. Prior to CT/MRI image registration, we had to normalize two different modalities to a similar intensity scale using the histogram matching method. Then CT and T2 weighted MRI were rigidly and deformably registered using intensity-based registration toolbox
(version 4.9). A U-net deep learning algorithm with 23 convolutional layers was developed to generate synthetic CT (sCT) using 23 NPC patients' images as the training set. The rest 10 NPC patients were used as the test set (~1/3 of all datasets). Mean absolute error (MAE) and mean error (ME) were calculated to evaluate HU differences between true CT and sCT in bone, soft tissue and overall region.
The proposed U-net algorithm was able to create sCT based on T2-weighted MRI in NPC patients, which took 7 s per patient on average. Compared to true CT, MAE of sCT in all tested patients was 97 ± 13 Hounsfield Unit (HU) in soft tissue, 131 ± 24 HU in overall region, and 357 ± 44 HU in bone, respectively. ME was -48 ± 10 HU in soft tissue, -6 ± 13 HU in overall region, and 247 ± 44 HU in bone, respectively. The majority soft tissue and bone region was reconstructed accurately except the interface between soft tissue and bone and some delicate structures in nasal cavity, where the inaccuracy was induced by imperfect deformable registration. One patient example was shown with almost no difference in dose distribution using true CT vs. sCT in the PTV regions in the sinus area with fine bone structures.
Our study indicates that it is feasible to generate high quality sCT images based on T2-weighted MRI using the deep learning algorithm in patients with nasopharyngeal carcinoma, which may have great clinical potential for MRI-only treatment planning in the future.
PLGA microparticles of different morphologies were obtained by affecting the sequence of onset of polymer entanglements and Coulomb fission in electrospray-generated droplets.
We developed a ...well-controlled method to generate PLGA microparticles of different morphologies using the electrospray drying route. By judiciously selecting polymer molecular weight, concentration, and solution flow rate, we can control the order in which polymer entanglements and Coulomb fission occur in the droplets and their relative importance, and subsequently govern the morphology of the resulting polymer particles. We show that spherical, monodisperse particles are generated when sufficiently strong polymer entanglements set in the evaporating droplets
before they undergo any Coulomb fission. On the other hand, tailed and elongated particles are obtained if the Coulomb fission occurs first and if the droplets/particles are sufficiently evaporated to freeze in their irregular shape. Strictly spherical particles are unachievable for polymer solutions below a critical concentration, because the onset of Coulomb fission always sets in prior to the development of a sufficiently entangled polymer network. An extension of a simple model, originally used to determine the onset of electrospinning of polymer solutions, adequately predicts when non-spherical particles are produced. We conclude by demonstrating the scale-up of this approach to the synthesis of polymer particles using a compact, microfabricated, multiplexed electrospray system, which would make it suitable for practical applications.
There are growing demands for multimaterial three-dimensional (3D) printing to manufacture 3D object where voxels with different properties and functions are precisely arranged. Digital light ...processing (DLP) is a high-resolution fast-speed 3D printing technology suitable for various materials. However, multimaterial 3D printing is challenging for DLP as the current multimaterial switching methods require direct contact onto the printed part to remove residual resin. Here we report a DLP-based centrifugal multimaterial (CM) 3D printing method to generate large-volume heterogeneous 3D objects where composition, property and function are programmable at voxel scale. Centrifugal force enables non-contact, high-efficiency multimaterial switching, so that the CM 3D printer can print heterogenous 3D structures in large area (up to 180 mm × 130 mm) made of materials ranging from hydrogels to functional polymers, and even ceramics. Our CM 3D printing method exhibits excellent capability of fabricating digital materials, soft robots, and ceramic devices.
People often form different aesthetic preferences for natural and built environments, which affects their behavioral intention; however, it remains unknown whether this difference in aesthetic ...preference is due to differences in thinking styles. However, whether tourists' aesthetic preferences differ when using different visual attention processes has not been studied further. This study used eye-tracking and self-reporting to investigate these questions. The results show that natural environment images are more favored visually because they can evoke in tourists larger pupil diameters and longer scan paths, but we found no significant difference in fixation duration and fixation counts. We also found that the scanning path of tourists who predominantly rely on intuitive thinking is modulated by the bottom-up attention process, while the scanning path of tourists who prefer rational thinking is modulated by the top-down attention process. In the bottom-up process, tourists who prefer rational thinking exhibit more positive aesthetic preferences and emotional arousal. In summary, the present study verified that aesthetic preference is more likely to be influenced by both thinking style and visual attention processing. The results of the present work provide preliminary evidence that the aesthetic preference of the environment is not only related to visual attention but also affected by the individual visual attention process and thinking style.
This paper presents a new prescribed performance-based finite-time adaptive tracking control scheme for a class of pure-feedback nonlinear systems with input quantization and dynamical uncertainties. ...To process the input signal, a new quantizer combining the advantages of a hysteresis quantizer and uniform quantizer has been used. Radial basis function neural networks have been utilized to approximate unknown nonlinear smooth functions. An auxiliary system has been employed to estimate unmodeled dynamics by producing a dynamic signal. By introducing a hyperbolic tangent function and performance function, the tracking error was made to fall within the prescribed time-varying constraints. Using modified dynamic surface control (DSC) technology and a finite-time control method, a novel finite-time controller has been designed, and the singularity problem of differentiating each virtual control scheme in the existing finite-time control scheme has been removed. Theoretical analysis shows that all signals in the closed-loop system are semi-globally practically finite-time stable, and that the tracking error converges to a prescribed time-varying region. Simulation results for two numerical examples have been provided to illustrate the validity of the proposed control method.
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