Ahmed Choukri Abdullah, Erfan Ahmadinejad, Savas Tasoglu
{"title":"Optimizing Solid Microneedle Design: A Comprehensive ML-Augmented DOE Approach","authors":"Ahmed Choukri Abdullah, Erfan Ahmadinejad, Savas Tasoglu","doi":"10.1021/acsmeasuresciau.4c00021","DOIUrl":null,"url":null,"abstract":"Microneedles (MNs), that is, a matrix of micrometer-scale needles, have diverse applications in drug delivery, skincare therapy, and health monitoring. MNs offer a minimally invasive alternative to hypodermic needles, characterized by rapid and painless procedures, cost-effective fabrication methods, and reduced tissue damage. This study explores four MN designs, cone-shaped, tapered cone-shaped, pyramidal with a square base, and pyramidal with a triangular-shaped base, and their optimization based on predefined criteria. The workflow encompasses three loading conditions: compressive load during insertion, critical buckling load, and bending loading resulting from incorrect insertion. Geometric parameters such as base radius/width, tip radius/width, height, and tapered angle tip influence the output criteria, namely, total deformation, critical buckling loads, factor of safety (FOS), and bending stress. The comprehensive framework employing a design of experiment approach within the ANSYS workbench toolbox establishes a mathematical model and a response surface fitting model. The resulting regression model, sensitivity chart, and response curve are used to create a multiobjective optimization problem that helps achieve an optimized MN geometrical design across the introduced four shapes, integrating machine learning (ML) techniques. This study contributes valuable insights into a potential ML-augmented optimization framework for MNs via needle designs to stay durable for various physiologically relevant conditions.","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Measurement Science Au","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1021/acsmeasuresciau.4c00021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Microneedles (MNs), that is, a matrix of micrometer-scale needles, have diverse applications in drug delivery, skincare therapy, and health monitoring. MNs offer a minimally invasive alternative to hypodermic needles, characterized by rapid and painless procedures, cost-effective fabrication methods, and reduced tissue damage. This study explores four MN designs, cone-shaped, tapered cone-shaped, pyramidal with a square base, and pyramidal with a triangular-shaped base, and their optimization based on predefined criteria. The workflow encompasses three loading conditions: compressive load during insertion, critical buckling load, and bending loading resulting from incorrect insertion. Geometric parameters such as base radius/width, tip radius/width, height, and tapered angle tip influence the output criteria, namely, total deformation, critical buckling loads, factor of safety (FOS), and bending stress. The comprehensive framework employing a design of experiment approach within the ANSYS workbench toolbox establishes a mathematical model and a response surface fitting model. The resulting regression model, sensitivity chart, and response curve are used to create a multiobjective optimization problem that helps achieve an optimized MN geometrical design across the introduced four shapes, integrating machine learning (ML) techniques. This study contributes valuable insights into a potential ML-augmented optimization framework for MNs via needle designs to stay durable for various physiologically relevant conditions.
期刊介绍:
ACS Measurement Science Au is an open access journal that publishes experimental computational or theoretical research in all areas of chemical measurement science. Short letters comprehensive articles reviews and perspectives are welcome on topics that report on any phase of analytical operations including sampling measurement and data analysis. This includes:Chemical Reactions and SelectivityChemometrics and Data ProcessingElectrochemistryElemental and Molecular CharacterizationImagingInstrumentationMass SpectrometryMicroscale and Nanoscale systemsOmics (Genomics Proteomics Metabonomics Metabolomics and Bioinformatics)Sensors and Sensing (Biosensors Chemical Sensors Gas Sensors Intracellular Sensors Single-Molecule Sensors Cell Chips Arrays Microfluidic Devices)SeparationsSpectroscopySurface analysisPapers dealing with established methods need to offer a significantly improved original application of the method.