Pure voltage-driven spintronic neuron based on stochastic magnetization switching behaviour.

Voltage-driven stochastic magnetization switching in a nanomagnet has attracted more attention recently with its superiority in achieving energy-efficient artificial neuron. Here, a novel pure voltage-driven scheme with ∼27.66 aJ energy dissipation is proposed, which could rotate magnetization vector randomly using only a pair of electrodes covered on the multiferroic nanomagnet. Results show that the probability of 180° magnetization switching is examined as a sigmoid-like function of the voltage pulse width and magnitude, which can be utilized as the activation function of designed neuron. Considering the size errors of designed neuron in fabrication, it's found that reasonable thickness and width variations cause little effect on recognition accuracy for MNIST hand-written dataset. In other words, the designed pure voltage-driven spintronic neuron could tolerate size errors. These results open a new way toward the realization of artificial neural network with low power consumption and high reliability.

Reduced Graphene Oxide/Mesoporous ZnO NSs Hybrid Fibers for Flexible, Stretchable, Twisted, and Wearable NO2 E-Textile Gas Sensor.

E-textiles are gaining growing popularity recently due to low cost, light weight, and conformable compatibility with clothes in wearable and portable smart electronics. Here, an easy-handing, low cost, and scalable fabricating strategy is reported to fabricate conductive, highly flexible, and mechanically stretchable/twisted fiber gas sensor with great wearability and knittability. The proposed gas sensor is built using commercially available cotton/elastic threads as flexible/stretchable templates and reduced graphene oxide/mesoporous zinc oxide nanosheets as sensing layers to form conducting fibers. The as-prepared fiber demonstrates sensitive sensing response, excellent long-term stability (84 days), low theoretical detection limit (43.5 ppb NO2), great mechanical deformation tolerance (3000 bending cycles, 1000 twisting cycles and 65% strain strength), and washing durability in room-temperature gas detection. More significantly, scalable wearable characteristics including repairability, reliability, stability, and practicability have been efficiently improved, which are achieved by knotting the fractured fibers, incorporating multiple sensors in series/parallel and weaving multisensor array networks integrated into clothes. The good sensing properties, superior flexibility, and scalable applications of wearable fibers may provide a broad window for widespread monitoring of numerous human activities in personal mobile electronics and human-machine interactions.

Sprayed, Scalable, Wearable, and Portable NO2 Sensor Array Using Fully Flexible AgNPs-All-Carbon Nanostructures.

Flexible chemical sensors usually require transfer of prepared layers or whole device onto special flexible substrates and further attachment to target objects, limiting the practical applications. Herein, a sprayed gas sensor array utilizing silver nanoparticles (AgNPs)-all-carbon hybrid nanostructures is introduced to enable direct device preparation on various target objects. The fully flexible device is formed using metallic single-walled carbon nanotubes as conductive electrodes and AgNPs-decorated reduced graphene oxide as sensing layers. The sensor presents sensitive response ( Ra/ Rg) of 6.0-20 ppm NO2, great mechanical robustness (3000 bending cycles), and obvious sensing ability as low as 0.2 ppm NO2 at room temperature. The sensitivity is about 3.3 and 13 times as that of the sample based on metal electrodes and the sample without AgNP decoration. The fabrication method demonstrates good scalability and suitability on the planar and nonplanar supports. The devices attached on a lab coat or the human body perform stable performance, indicating practicability in wearable and portable fields. The flexible and scalable sensor provides a new choice for real-time monitoring of toxic gases in personal mobile electronics and human-machine interactions.