No abstract

Insulating layers based on oxides and nitrides provide high capacitance, low leakage, high breakdown field and resistance to electrical stresses when used in electronic devices based on rigid substrates. However, their typically high process temperatures and brittleness make it difficult to achieve similar performance in flexible or organic electronics. Here, we show that poly(1,3,5-trimethyl-1,3,5-trivinyl cyclotrisiloxane) (pV3D3) prepared via a one-step, solvent-free technique called initiated chemical vapour deposition (iCVD) is a versatile polymeric insulating layer that meets a wide range of requirements for next-generation electronic devices. Highly uniform and pure ultrathin films of pV3D3 with excellent insulating properties, a large energy gap (>8 eV), tunnelling-limited leakage characteristics and resistance to a tensile strain of up to 4% are demonstrated. The low process temperature, surface-growth character, and solvent-free nature of the iCVD process enable pV3D3 to be grown conformally on plastic substrates to yield flexible field-effect transistors as well as on a variety of channel layers, including organics, oxides, and graphene.

Various biophysical and biochemical factors are important for determining the fate of neural stem cells (NSCs). Among biophysical signals, topographical stimulation by micro/nanopatterns has been applied to control NSC differentiation. In this study, we developed a hierarchically patterned substrate (HPS) platform that can synergistically enhance the differentiation of human NSCs (hNSCs) by simultaneously providing microscale and nanoscale spatial controls to facilitate the alignment of the cytoskeleton and the formation of focal adhesions. The multiscale HPS was fabricated by combining microgroove patterns (groove size: 1.5 μm), prepared by a conventional photolithographic process, and nanopore patterns (pore diameter: 10 nm), prepared from cylinder-forming block copolymer thin films. The hNSCs grown on the HPS exhibited not only a highly aligned, elongated morphology, but also a greatly enhanced differentiation into neuronal and astrocyte lineages, compared to hNSCs on a flat substrate (FS) or single-type patterned substrates [microgroove patterned substrate (MPS) and nanopore patterned substrate (NPS)]. Interestingly, the application of the HPS directed hNSC differentiation toward neurons rather than astrocytes. The expression of focal adhesion proteins in hNSCs was also significantly increased on the HPS compared to the FS, MPS, and NPS, likely a result of the presence of more focal contact points provided by nanopore structures. Inhibition of both β1 integrin-mediated binding and the intracellular Rho-associated protein kinase pathway of hNSCs eliminated the beneficial effects of the HPS on focal adhesion formation and actin filament alignment, which subsequently reduced hNSC differentiation. More importantly, hNSCs on the HPS differentiated into functional neurons exhibiting sodium currents and action potentials. The multiscale, hierarchically patterned topography would be useful for the design of functional biomaterial scaffolds to potentiate NSC therapeutic efficacy.

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.

No abstract