Modulating nanostructures with polyvinylpyrrolidone: Design and development of a porous, biocompatible, and pH-Stable core-shell magnetic microrobot for demonstrating drug absorption from wastewater.

Increased antineoplastic drug concentrations in wastewater stem from ineffective treatment plants and increased usage. Although microrobots are promising for pollutant removal, they face hurdles in developing a superstructure with superior adsorption capabilities, biocompatibility, porosity, and pH stability. This study focused on adjusting the PVP concentration from 0.05 to 0.375 mM during synthesis to create a favorable CMOC structure for drug absorption. Lower PVP concentrations (0.05 mM) yielded a three-dimensional nanoflower structure of CaMoO4 and CuS nanostructures, whereas five-fold concentrations (0.25 mM) produced a porous structure with a dense CuS core encased in a transparent CaMoO4 shell. The magnetically movable and pH-stable COF@CMOC microrobot, achieved by attaching CMOC to cobalt ferrite (CoF) NPs, captured doxorubicin efficiently, with up to 57 % efficiency at 200 ng/mL concentration for 30 min, facilitated by electrostatic interaction, hydrogen bonding, and pore filling of DOX. The results demonstrated that DOX removal through magnetic motion showed superior performance, with an estimated improvement of 57% compared to stirring conditions (17 %). A prototype PDMS microchannel system was developed to study drug absorption and microrobot recovery. The CaMoO4 shell of the microrobots exhibited remarkable robustness, ensuring long-lasting functionality in harsh wastewater environments and improving biocompatibility while safeguarding the CuS core from degradation. Therefore, microrobots are a promising eco-friendly solution for drug extraction. These microrobots show promise for the selective removal of doxorubicin from contaminated wastewater.

Study on Intention Recognition and Sensory Feedback: Control of Robotic Prosthetic Hand Through EMG Classification and Proprioceptive Feedback Using Rule-based Haptic Device.

In this study, for intention recognition, a convolutional neural network (CNN) classification model using the electromyography (EMG) signals acquired from the subject was developed. For sensory feedback, a rule-based wearable proprioceptive feedback haptic device, a new method for providing feedback on the grip information of a robotic prosthesis was proposed. Then, we constructed a closed-loop integrated system consisting of the CNN-based EMG classification model, the proposed haptic device, and a robotic prosthetic hand. Finally, an experiment was conducted in which the closed-loop integrated system was used to simultaneously evaluate the performance of the intention recognition and sensory feedback for a subject. The trained EMG classification model and the proposed haptic device showed the intention recognition and sensory feedback performance with 97% or higher accuracy in 10 grip states. Although some errors occurred in the intention recognition using the EMG classification model, in general, the grip intention of the subject was grasped relatively accurately, and the grip pattern was also accurately transmitted to the subject by the proposed haptic device. The integrated system which consists of the intention recognition using the CNN-based EMG classification model and the sensory feedback using the proposed haptic device is expected to be utilized for robotic prosthetic hand prosthesis control of limb loss participants.