Photoluminescent Semiconducting Graphene Nanoribbons via Longitudinally Unzipping Single-Walled Carbon Nanotubes.

The lack of a sizeable band gap has so far prevented graphene from building effective electronic and optoelectronic devices despite its numerous exceptional properties. Intensive theoretical research reveals that a band gap larger than 1 eV can only be achieved in sub-3 nm wide graphene nanoribbons (GNRs), but real fabrication of such ultranarrow GNRs still remains a critical challenge. Herein, we demonstrate an approach for the synthesis of ultranarrow and photoluminescent semiconducting GNRs by longitudinally unzipping single-walled carbon nanotubes. Atomic force microscopy reveals the unzipping process, and the resulting 2.2 nm wide GNRs are found to emit strong and sharp photoluminescence at ∼685 nm, demonstrating a very desirable semiconducting nature. This band gap of 1.8 eV is further confirmed by follow-up photoconductivity measurements, where a considerable photocurrent is generated, as the excitation wavelength becomes shorter than 700 nm. More importantly, our fabricated GNR field-effect transistors (FETs), by employing the hexagonal boron nitride-encapsulated heterostructure to achieve edge-bonded contacts, demonstrate a high current on/off ratio beyond 105 and carrier mobility of 840 cm2/V s, approaching the theoretical scattering limit in semiconducting GNRs at room temperature. Especially, highly aligned GNR bundles with lengths up to a millimeter are also achieved by prepatterning a template, and the fabricated GNR bundle FETs show a high on/off ratio reaching 105, well-defined saturation currents, and strong light-emitting properties. Therefore, GNRs produced by this method open a door for promising applications in graphene-based electronics and optoelectronics.

An Artificial Sensory Neuron with Tactile Perceptual Learning.

Sensory neurons within skin form an interface between the external physical reality and the inner tactile perception. This interface enables sensory information to be organized identified, and interpreted through perceptual learning-the process whereby the sensing abilities improve through experience. Here, an artificial sensory neuron that can integrate and differentiate the spatiotemporal features of touched patterns for recognition is shown. The system comprises sensing, transmitting, and processing components that are parallel to those found in a sensory neuron. A resistive pressure sensor converts pressure stimuli into electric signals, which are transmitted to a synaptic transistor through interfacial ionic/electronic coupling via a soft ionic conductor. Furthermore, the recognition error rate can be dramatically decreased from 44% to 0.4% by integrating with the machine learning method. This work represents a step toward the design and use of neuromorphic electronic skin with artificial intelligence for robotics and prosthetics.