Self-powered, light-controlled, bioresorbable platforms for programmed drug delivery.

Degradable polymer matrices and porous scaffolds provide powerful mechanisms for passive, sustained release of drugs relevant to the treatment of a broad range of diseases and conditions. Growing interest is in active control of pharmacokinetics tailored to the needs of the patient via programmable engineering platforms that include power sources, delivery mechanisms, communication hardware, and associated electronics, most typically in forms that require surgical extraction after a period of use. Here we report a light-controlled, self-powered technology that bypasses key disadvantages of these systems, in an overall design that is bioresorbable. Programmability relies on the use of an external light source to illuminate an implanted, wavelength-sensitive phototransistor to trigger a short circuit in an electrochemical cell structure that includes a metal gate valve as its anode. Consequent electrochemical corrosion eliminates the gate, thereby opening an underlying reservoir to release a dose of drugs by passive diffusion into surrounding tissue. A wavelength-division multiplexing strategy allows release to be programmed from any one or any arbitrary combination of a collection of reservoirs built into an integrated device. Studies of various bioresorbable electrode materials define the key considerations and guide optimized choices in designs. In vivo demonstrations of programmed release of lidocaine adjacent the sciatic nerves in rat models illustrate the functionality in the context of pain management, an essential aspect of patient care that could benefit from the results presented here.

High-Fidelity, Sub-5 nm Patterns from High-χ Block Copolymer Films with Vapor-Deposited Ultrathin, Cross-Linked Surface-Modification Layers.

Despite their capability, sub-10 nm periodic nano-patterns formed by strongly segregating block copolymer (BCP) thin films cannot be easily oriented perpendicular to the substrate due to the huge surface energy differences of the constituent blocks. To produce perpendicular nano-patterns, the interfacial energies of both the substrate and free interfaces should be controlled precisely to induce non-preferential wetting. Unfortunately, high-performance surface modification layers are challenging to design, and different kinds of surface modification methods must be devised respectively for each neutral layer and top coat. Furthermore, conventional approaches, largely based on spin-coating processes, are highly prone to defect formation and may readily cause dewetting at sub-10 nm thickness. To date, these obstacles have hampered the development of high-fidelity, sub-5 nm BCP patterns. Herein, an all-vapor phase deposition approach initiated chemical vapor deposition is demonstrated to form 9-nm-thick, uniform neutral bottom layer and top coat with exquisite control of composition and thickness. These layers are employed in BCP films to produce perpendicular cylinders with a diameter of ≈4 nm that propagate throughout a BCP thickness of up to ≈60 nm, corresponding to five natural domain spacings of the BCP. Such a robust approach will serve as an advancement for the reliable generation of sub-10 nm nano-patterns.

Polymer Analog Memristive Synapse with Atomic-Scale Conductive Filament for Flexible Neuromorphic Computing System.

With the advent of artificial intelligence (AI), memristors have received significant interest as a synaptic building block for neuromorphic systems, where each synaptic memristor should operate in an analog fashion, exhibiting multilevel accessible conductance states. Here, we demonstrate that the transition of the operation mode in poly(1,3,5-trivinyl-1,3,5-trimethyl cyclotrisiloxane) (pV3D3)-based flexible memristor from conventional binary to synaptic analog switching can be achieved simply by reducing the size of the formed filament. With the quantized conductance states observed in the flexible pV3D3 memristor, analog potentiation and depression characteristics of the memristive synapse are obtained through the growth of atomically thin Cu filament and lateral dissolution of the filament via dominant electric field effect, respectively. The face classification capability of our memristor is evaluated via simulation using an artificial neural network consisting of pV3D3 memristor synapses. These results will encourage the development of soft neuromorphic intelligent systems.