Magneto-mechanical effect of magnetic microhydrogel for improvement of magnetic neuro-stimulation

Superparamagnetic iron oxide(SPIO)nanoparticles play an important role in mediating precise and effective magnetic neurostimulation and can help overcome limitations related to penetration depth and spatial resolution.However,nanoparticles readily diffuse in vivo,decreasing the spatial resolution and activation efficiency.In this study,we employed a microfluidic means to fabricate injectable microhydrogels encapsulated with SPIO nanoparticles,which significantly improved the stability of nanoparticles,increased the magnetic properties,reinforced the stimulation effectivity.The fabricated magnetic microhydrogels were highly uniform in size and sphericity,enabling minimally invasive injection into brain tissue.The long-term residency in the cortex up to 22 weeks and the safety of brain tissue were shown using a mouse model.In addition,we quantitatively determined the magneto-mechanical force yielded by only one magnetic microhydrogel using a video-based method.The force was found to be within 7–8 pN under 10 Hz magnetic stimulation by both theoretical simulation and experimental measurement.Lastly,electrophysiological measurement of brain slices showed that the magnetic microhydrogels offer significant advantages in terms of neural activation relative to dissociative SPIO nanoparticles.A universal strategy is thus offered for performing magnetic neuro-stimulation with an improved prospect for biomedical translation.

Machine Learning-Based Multi-Modal Information Perception for Soft Robotic Hands

This paper focuses on multi-modal Information Perception(IP)for Soft Robotic Hands(SRHs)using Machine Learning(ML)algorithms.A flexible Optical Fiber-based Curvature Sensor(OFCS)is fabricated,consisting of a Light-Emitting Diode(LED),photosensitive detector,and optical fiber.Bending the roughened optical fiber generates lower light intensity,which reflecting the curvature of the soft finger.Together with the curvature and pressure information,multi-modal IP is performed to improve the recognition accuracy.Recognitions of gesture,object shape,size,and weight are implemented with multiple ML approaches,including the Supervised Learning Algorithms(SLAs)of K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Logistic Regression(LR),and the unSupervised Learning Algorithm(un-SLA)of K-Means Clustering(KMC).Moreover,Optical Sensor Information(OSI),Pressure Sensor Information(PSI),and Double-Sensor Information(DSI)are adopted to compare the recognition accuracies.The experiment results demonstrate that the proposed sensors and recognition approaches are feasible and effective.The recognition accuracies obtained using the above ML algorithms and three modes of sensor information are higer than 85 percent for almost all combinations.Moreover,DSI is more accurate when compared to single modal sensor information and the KNN algorithm with a DSI outperforms the other combinations in recognition accuracy.