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Department: search.filters.department.Department of Media and Visual ArtsSubject: search.filters.subject.Biochemical research methods

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    Increasing the packing density of assays in paper-based microfluidic devices
    (Aip Publishing, 2021) Becher, Elaina; Ghaderinezhad, Fariba; Özkan, Mehmed; Yetişen, Ali Kemal; N/A; Department of Mechanical Engineering; N/A; Department of Media and Visual Arts; Dabbagh, Sajjad Rahmani; Taşoğlu, Savaş; Havlucu, Hayati; Özcan, Oğuzhan; N/A; Faculty Member; PhD Student; Faculty Member; Department of Mechanical Engineering; Department of Media and Visual Arts; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; 291971; N/A; 12532
    Paper-based devices have a wide range of applications in point-of-care diagnostics, environmental analysis, and food monitoring. Paper-based devices can be deployed to resource-limited countries and remote settings in developed countries. Paper-based point-of-care devices can provide access to diagnostic assays without significant user training to perform the tests accurately and timely. The market penetration of paper-based assays requires decreased device fabrication costs, including larger packing density of assays (i.e., closely packed features) and minimization of assay reagents. In this review, we discuss fabrication methods that allow for increasing packing density and generating closely packed features in paper-based devices. To ensure that the paper-based device is low-cost, advanced fabrication methods have been developed for the mass production of closely packed assays. These emerging methods will enable minimizing the volume of required samples (e.g., liquid biopsies) and reagents in paper-based microfluidic devices.
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    Machine learning-enabled optimization of extrusion-based 3D printing
    (Academic Press Inc Elsevier Science, 2022) N/A; Department of Media and Visual Arts; Department of Mechanical Engineering; Dabbagh, Sajjad Rahmani; Özcan, Oğuzhan; Taşoğlu, Savaş; PhD Stud; ent; Faculty Member; Faculty Member; Department of Media and Visual Arts; Department of Mechanical Engineering; Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Graduate School of Sciences and Engineering; College of Social Sciences and Humanities; College of Engineering; N/A; 12532; 291971
    Machine learning (ML) and three-dimensional (3D) printing are among the fastest-growing branches of science. While ML can enable computers to independently learn from available data to make decisions with minimal human intervention, 3D printing has opened up an avenue for modern, multi-material, manufacture of complex 3D structures with a rapid turn-around ability for users with limited manufacturing experience. However, the determination of optimum printing parameters is still a challenge, increasing pre-printing process time and material wastage. Here, we present the first integration of ML and 3D printing through an easy-to-use graphical user interface (GUI) for printing parameter optimization. Unlike the widely held orthogonal design used in most of the 3D printing research, we, for the first time, used nine different computer-aided design (CAD) images and in order to enable ML algorithms to distinguish the difference between designs, we devised a self-designed method to calculate the "complexity index" of CAD designs. In addition, for the first time, the similarity of the print outcomes and CAD images are measured using four different self-designed labeling methods (both manually and automatically) to figure out the best labeling method for ML purposes. Subsequently, we trained eight ML algorithms on 224 datapoints to identify the best ML model for 3D printing applications. The "gradient boosting regression" model yields the best prediction performance with an R-2 score of 0.954. The ML-embedded GUI developed in this study enables users (either skilled or unskilled in 3D printing and/or ML) to simply upload a design (desired to print) to the GUI along with desired printing temperature and pressure to obtain the approximate similarity in the case of actual 3D printing of the uploaded design. This ultimately can prevent error-and-trial steps prior to printing which in return can speed up overall design-to-end-product time with less material waste and more cost-efficiency.