Multiplexed DNA-functionalized graphene sensor with artificial intelligence-based discrimination performance for analyzing chemical vapor compositions

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene(MDFG)nanoelectrode without the need to condense the original vapor or target dilution.To the best of our knowledge,our artificial intelligence(Al)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in the early stage.This innovative technology comprised an optimized combination of nanodeposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that could operate within biological limits,resulting in the verification of mixed vapor chemical components.Highly selective sensors that are tolerant to high humidity levels provide a target for"breath chemovapor fingerprinting"for the early diagnosis of diseases.The feature selection analysis achieved recognition rates of 99%and above under low-humidity conditions and 98%and above under humid conditions for mixed chemical compositions.The 1D convolutional neural network analysis performed better,discriminating the compositional state of chemical vapor under low-and high-humidity conditions almost perfectly.This study provides a basis for the use of a multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence-based discrimination of chemical vapor compositions in breath analysis applications.

Feedback Control of an Achiral Robotic Microswimmer

Magnetic microswimmers are useful for navigating and performing tasks at small scales. To demonstrate effective control over such microswimmers, we implemented feedback control of the three-bead achiral microswimmers in both simulation and experiment. The achiral microswimmers with the ability to swim in bulk fluid are controlled wirelessly using magnetic fields generated from electromagnetic coils. The achirality of the microswimmers introduces unknown handedness resulting in uncertainty in swimming direction. We use a combination of rotating and static magnetic fields generated from an approximate Helmholtz coil system to overcome such uncertainty. There are also movement uncertainties due to environmental factors such as unsteady flow conditions. A kinematic model based feedback controller was created based on data fitting of experimental data. However, the controller was unable to yield satisfactory performance due to uncertainties from environmental factors; i.e., the time to reach target pose under adverse flow condition is too long. Following the implementation of an integral controller to control the microswimmers＇ swimming velocity, the mieroswimmers were able to reach the target in roughly half the time. Through simulation and experiments, we show that the feedback control law can move an achiral microswimmer from any initial conditions to a target pose.