Stimuli-responsive polymeric micelles (PMs) have shown great potential in drug delivery and controlled release in cancer chemotherapy. Herein, inspired by the features of the tumor microenvironment, we developed dual pH/redox-responsive mixed PMs which are self-assembled from two kinds of amphiphilic diblock copolymers (poly(ethylene glycol) methyl ether-b-poly(β-amino esters) (mPEG-b-PAE) and poly(ethylene glycol) methyl ether-grafted disulfide-poly(β-amino esters) (PAE-ss-mPEG)) for anticancer drug delivery and controlled release. The co-micellization of two copolymers is evaluated by measurement of critical micelle concentration (CMC) values at different ratios of the two copolymers. The pH/redox-responsiveness of PMs is thoroughly investigated by measurement of base dissociation constant (pKb) value, particle size, and zeta-potential in different conditions. The PMs can encapsulate doxorubicin (DOX) efficiently, with high drug-loading efficacy. The DOX was released due to the swelling and disassembly of nanoparticles triggered by low pH and high glutathione (GSH) concentrations in tumor cells. The in vitro results demonstrated that drug release rate and cumulative release are obviously dependent on pH values and reducing agents. Furthermore, the cytotoxicity test showed that the mixed PMs have negligible toxicity, whereas the DOX-loaded mixed PMs exhibit high cytotoxicity for HepG2 cells. Therefore, the results demonstrate that the dual pH/redox-responsive PMs self-assembled from PAE-based diblock copolymers could be potential anticancer drug delivery carriers with pH/redox-triggered drug release, and the fabrication of stimuli-responsive mixed PMs could be an efficient strategy for preparation of intelligent drug delivery platform for disease therapy.

Vision-based manipulation has been largely used in various robot applications. Normally, in order to obtain the spatial information of the operated target, a carefully calibrated stereo vision system is required. However, it limits the application of robots in the unstructured environment which limits both the number and the pose of the camera. In this study, a calibration-free monocular vision-based robot manipulation approach is proposed based on domain randomization and deep reinforcement learning (DRL). Firstly, a learning strategy combined domain randomization is developed to estimate the spatial information of the target from a single monocular camera arbitrarily mounted in a large area of the manipulation environment. Secondly, to address the monocular occlusion problem which regularly happens during robot manipulations, an occlusion awareness DRL policy has been designed to control the robot to avoid occlusions actively in the manipulation tasks. The performance of our method has been evaluated on two common manipulation tasks, reaching and lifting of a target building block, which show the efficiency and effectiveness of our proposed approach.

A facile method that combines alkali-assisted oxidation and –SH chelation with a click chemistry reaction was employed to create an F-POSS polymer surface (fluorinated octavinyl polyhedral oligomeric silsesquioxane polymer)-based Cu mesh (F-POSS-OM).

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