Journal Article10.1007/s00604-024-06231-5
Advancing 3D printed microfluidics with computational methods for sweat analysis
Emre Ece,Kadriye Ölmez,Nedim Hacıosmanoğlu,Maryam Atabay,Fatih İnci +4 more
TL;DR: 3D printed microfluidics for sweat analysis offers a promising platform for non-invasive health monitoring and diagnostics due to its affordability, portability, and ease of manufacture. However, challenges such as high throughput demands and material interactions limit its potential. Computational methodologies like DFT and MD can help overcome these challenges and optimize design and production processes.
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Abstract: Abstract The intricate tapestry of biomarkers, including proteins, lipids, carbohydrates, vesicles, and nucleic acids within sweat, exhibits a profound correlation with the ones in the bloodstream. The facile extraction of samples from sweat glands has recently positioned sweat sampling at the forefront of non-invasive health monitoring and diagnostics. While extant platforms for sweat analysis exist, the imperative for portability, cost-effectiveness, ease of manufacture, and expeditious turnaround underscores the necessity for parameters that transcend conventional considerations. In this regard, 3D printed microfluidic devices emerge as promising systems, offering a harmonious fusion of attributes such as multifunctional integration, flexibility, biocompatibility, a controlled closed environment, and a minimal requisite analyte volume—features that leverage their prominence in the realm of sweat analysis. However, formidable challenges, including high throughput demands, chemical interactions intrinsic to the printing materials, size constraints, and durability concerns, beset the landscape of 3D printed microfluidic devices. Within this paradigm, we expound upon the foundational aspects of 3D printed microfluidic devices and proffer a distinctive perspective by delving into the computational study of printing materials utilizing density functional theory (DFT) and molecular dynamics (MD) methodologies. This multifaceted approach serves manifold purposes: (i) understanding the complexity of microfluidic systems, (ii) facilitating comprehensive analyses, (iii) saving both cost and time, (iv) improving design optimization, and (v) augmenting resolution. In a nutshell, the allure of 3D printing lies in its capacity for affordable and expeditious production, offering seamless integration of diverse components into microfluidic devices—a testament to their inherent utility in the domain of sweat analysis. The synergistic fusion of computational assessment methodologies with materials science not only optimizes analysis and production processes, but also expedites their widespread accessibility, ensuring continuous biomarker monitoring from sweat for end-users. Graphical Abstract
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Figures
![Fig. 3 (A) Fabrication steps of 3D printed microfluidic sweat analysis device is depicted. (B) The layers of the device, (C) the analysis methods for the target analytes, (D−F) internal structure of the device with different dyes, and (G) the completed device are presented. Reprinted with permission [70]. Copyright 2023, Royal Society of Chemistry](/figures/figure3-1-4yjgzppc4gj7.png)
Fig. 3 (A) Fabrication steps of 3D printed microfluidic sweat analysis device is depicted. (B) The layers of the device, (C) the analysis methods for the target analytes, (D−F) internal structure of the device with different dyes, and (G) the completed device are presented. Reprinted with permission [70]. Copyright 2023, Royal Society of Chemistry 
Fig. 1 Schematic illustration of the 3D printed microfluidic platform and the application of such platforms to skin for measuring multiple parameters and biomarkers from sweat 
Table 1 Comparisons and properties of computational methods ![Fig. 4 DFT-calculated HOMO and LUMO values of TPE derivative. Reprinted with permission [95]. Copyright 2023, Elsevier](/figures/figure4-1-7o5eeu7bvk23.png)
Fig. 4 DFT-calculated HOMO and LUMO values of TPE derivative. Reprinted with permission [95]. Copyright 2023, Elsevier
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