Neuromorphic computing is an emerging computing paradigm beyond the conventional digital von Neumann computation. An oxide-based resistive switching memory is engineered to emulate synaptic devices. At the device level, the gradual resistance modulation is characterized by hundreds of identical pulses, achieving a low energy consumption of less than 1 pJ per spike. Furthermore, a stochastic compact model is developed to quantify the device switching dynamics and variation. At system level, the performance of an artificial visual system on the image orientation or edge detection with 16 348 oxide-based synaptic devices is simulated, successfully demonstrating a key feature of neuromorphic computing: tolerance to device variation.

A postsynthesis assembly approach, an ex situ ligand exchange route, was developed for fast (within 2 h) and high loading (34% coverage) deposition of CdSe QDs on TiO(2) films. With the combination of high-quality QD sensitizers and the effective deposition technique, a record photovoltaic performance with an efficiency of 5.4% was observed for the resulting cell device.

We report a new strategy to synthesize core-shell metal nanoparticles with an interior, Raman tag-encoded nanogap by taking advantage of nanoparticle-templated self-assembly of amphiphilic block copolymers and localized metal precursor reduction by redox-active polymer brushes. Of particular interest for surface-enhanced Raman scattering (SERS) is that the nanogap size can be tailored flexibly, with the sub-2 nm nanogap leading to the highest SERS enhancement. Our results have further demonstrated that surface functionalization of the nanogapped Au nanoparticles with aptamer targeting ligands allows for specific recognition and ultrasensitive detection of cancer cells. The general applicability of this new synthetic strategy, coupled with recent advances in controlled wet-chemical synthesis of functional nanocrystals, opens new avenues to multifunctional core-shell nanoparticles with integrated optical, electronic, and magnetic properties.

We report a versatile strategy based on the use of multifunctional mussel-inspired polydopamine for constructing well-defined single-nanoparticle@metal–organic framework (MOF) core–shell nanohybrids. The capability of polydopamine to form a robust conformal coating on colloidal substrates of any composition and to direct the heterogeneous nucleation and growth of MOFs makes it possible for customized structural integration of a broad range of inorganic/organic nanoparticles and functional MOFs. Furthermore, the unique redox activity of polydopamine adds additional possibilities to tailor the functionalities of the nanohybrids by sandwiching plasmonic/catalytic metal nanostructures between the core and shell via localized reduction. The core–shell nanohybrids, with the molecular sieving effect of the MOF shell complementing the intrinsic properties of nanoparticle cores, represent a unique class of nanomaterials of considerable current interest for catalysis, sensing, and nanomedicine.

Recyclable nanocatalysts of core-shell bimetallic nanocrystals are developed through polydopamine coating-directed one-step seeded growth, interfacial assembly, and substrate-immobilization of Au@Ag core-shell nanocrystals. This strategy provides new opportunities to design and optimize heterogeneous nanocatalysts with tailored size, morphology, chemical configuration, and supporting substrates for metal-catalyzed reactions.

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Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital computing. In this work, we show that the SET (off-to-on) transition of metal oxide resistive switching memory becomes probabilistic under a weak programming condition. The switching variability of the binary synaptic device implements a stochastic learning rule. Such stochastic SET transition was statistically measured and modeled for a simulation of a winner-take-all network for competitive learning. The simulation illustrates that with such stochastic learning, the orientation classification function of input patterns can be effectively realized. The system performance metrics were compared between the conventional approach using the analog synapse and the approach in this work that employs the binary synapse utilizing the stochastic learning. The feasibility of using binary synapse in the neurormorphic computing may relax the constraints to engineer continuous multilevel intermediate states and widens the material choice for the synaptic device design.

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