22,251 research outputs found
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Current commercialization status of electrowetting-on-dielectric (EWOD) digital microfluidics.
The emergence of electrowetting-on-dielectric (EWOD) in the early 2000s made the once-obscure electrowetting phenomenon practical and led to numerous activities over the last two decades. As an eloquent microscale liquid handling technology that gave birth to digital microfluidics, EWOD has served as the basis for many commercial products over two major application areas: optical, such as liquid lenses and reflective displays, and biomedical, such as DNA library preparation and molecular diagnostics. A number of research or start-up companies (e.g., Phillips Research, Varioptic, Liquavista, and Advanced Liquid Logic) led the early commercialization efforts and eventually attracted major companies from various industry sectors (e.g., Corning, Amazon, and Illumina). Although not all of the pioneering products became an instant success, the persistent growth of liquid lenses and the recent FDA approvals of biomedical analyzers proved that EWOD is a powerful tool that deserves a wider recognition and more aggressive exploration. This review presents the history around major EWOD products that hit the market to show their winding paths to commercialization and summarizes the current state of product development to peek into the future. In providing the readers with a big picture of commercializing EWOD and digital microfluidics technology, our goal is to inspire further research exploration and new entrepreneurial adventures
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Low-cost and low-topography fabrication of multilayer interconnections for microfluidic devices
Multilayer interconnections are needed for microdevices with a large number of independent electrodes. A multi-level photolithographic process is commonly employed to provide multilayer interconnections in integrated circuit (IC) devices, but it is often too expensive for large-area or disposable devices frequently needed for microfluidics. The printed circuit board (PCB) can provide multilayer interconnection at low cost, but its rough topography poses a challenge for small droplets to slide over. Here we report a low-cost fabrication of low-topography multilayer interconnects by selective and controlled anodization of thin-film metal layers. The process utilizes anodization of metal (tantalum in this paper) or, more specifically, repetitions of a partial anodization to form insulation layers between conductive layers and a full anodization to form isolating regions between electrodes, replacing the usual process of depositing, planarizing, and etching insulation layers. After verifying the electric connections and insulations as intended, the developed method is applied to electrowetting-on-dielectric (EWOD), whose complex microfluidic products are currently built on PCB or thin-film transistor (TFT) substrates. To demonstrate the utility, we fabricated a 3 metal-layer EWOD device with steps (surface topography) less than 1 micrometer (vs. > 10 micrometers of PCB EWOD devices) and confirmed basic digital microfluidic operations
Video Captioning with Guidance of Multimodal Latent Topics
The topic diversity of open-domain videos leads to various vocabularies and
linguistic expressions in describing video contents, and therefore, makes the
video captioning task even more challenging. In this paper, we propose an
unified caption framework, M&M TGM, which mines multimodal topics in
unsupervised fashion from data and guides the caption decoder with these
topics. Compared to pre-defined topics, the mined multimodal topics are more
semantically and visually coherent and can reflect the topic distribution of
videos better. We formulate the topic-aware caption generation as a multi-task
learning problem, in which we add a parallel task, topic prediction, in
addition to the caption task. For the topic prediction task, we use the mined
topics as the teacher to train a student topic prediction model, which learns
to predict the latent topics from multimodal contents of videos. The topic
prediction provides intermediate supervision to the learning process. As for
the caption task, we propose a novel topic-aware decoder to generate more
accurate and detailed video descriptions with the guidance from latent topics.
The entire learning procedure is end-to-end and it optimizes both tasks
simultaneously. The results from extensive experiments conducted on the MSR-VTT
and Youtube2Text datasets demonstrate the effectiveness of our proposed model.
M&M TGM not only outperforms prior state-of-the-art methods on multiple
evaluation metrics and on both benchmark datasets, but also achieves better
generalization ability.Comment: ACM Multimedia 201
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