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    Rapid and cost-effective fabrication of microfluidic chips with resin 3D printing
    (Springer Science+Business Media, 2026-01-01)
    The fabrication of microfluidic chips has traditionally relied on photolithographic techniques. Although highly precise, these methods require specialized cleanroom facilities and involve multiple complex steps. In recent years, additive manufacturing—particularly resin-based 3D printing—has emerged as a promising alternative, offering more accessible, cost-effective, and rapid prototyping. In this study, we present a protocol for fabricating high-resolution microfluidic templates using an LCD-based resin 3D printer, followed by replication of microchannels in polydimethylsiloxane (PDMS). Our results show that while LCD 3D printing enables fast prototyping, it has limitations in accurately reproducing fine features (especially channel widths below 100 μm) due to overexpansion of cured resin. Morphological and dimensional analyses by scanning electron microscopy (SEM) revealed discrepancies between the designed and actual channel dimensions, primarily attributed to the printer’s pixel size constraints and light diffusion during polymerization. Despite these challenges, the ability to reuse a printed template for multiple PDMS replications significantly enhances fabrication scalability and cost efficiency. This study underscores the potential of resin-based 3D printing for microfluidic applications and provides optimization strategies to improve dimensional accuracy in future development.
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    Deep Neural Network-Assisted Microfluidic pH Sensor
    (Institute of Electrical and Electronics Engineers Inc., 2025)
    Water pH measurement is vital as it provides fundamental information about its quality and suitability for agriculture, aquatic ecosystems, industry, and human consumption. Each of these applications may require numerical readings of acidity or alkalinity, preferably using tools that are already ubiquitous, such as cellphones. This work presents a microfluidic lab-on-a-chip system to measure the pH of liquid samples. We used purple cabbage as the colorimetric reagent to produce a 2640-image dataset with pH levels in the range of [2–12] on a polydimethylsiloxane (PDMS) microfluidic recipient. We fed our dataset to our parameterized deep neural network (DNN) to classify our samples and found an accuracy of 99.7%. In addition, we developed a mobile application with an easy-to-use graphic user interface that recognizes the microfluidic device shape, classifies the image’s color, and returns the pH level.
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