Material-level countermeasures for securing microfluidic biochips
Navajit Singh Baban, Sohini Saha, Sofija Jancheska, Inderjeet Singh, Sachin Khapli, Maksat Khobdabayev, Jongmin Kim, Sukanta Bhattacharjee, Yong‐Ak Song, Krishnendu Chakrabarty, Ramesh Karri
Abstract
= 0.971) between the normalized excimer intensity change and the maximum principal strain of the actuated microvalves. To detect curing ratio-based attacks, we adapted machine learning (ML) models, which were trained on the force-displacement data obtained from a mechanical punch test method. Our ML models achieved more than 99% accuracy in detecting curing ratio anomalies. These countermeasures can be used to proactively safeguard FMBs against material-level attacks in the era of global pandemics and diagnostics based on POCTs.
Topics & Concepts
PolydimethylsiloxaneMicrofluidicsMaterials scienceCuring (chemistry)BiochipNanotechnologyComputer scienceBiomedical engineeringComposite materialEngineeringMicrofluidic and Capillary Electrophoresis ApplicationsMicrofluidic and Bio-sensing TechnologiesBiosensors and Analytical Detection