Ultralow power dual-gated subthreshold oxide neuristors : an enabler for higher order neuronal temporal correlations

Recent progress of vapor-deposited perovskite solar cells (PSCs) has proved the feasibility of this deposition method in achieving promising photovoltaic devices. For the first time, it is probed the versatility of the co-evaporation process in creating perovskite layers customizable for different device architectures. A gradient of composition is created within the perovskite films by tuning the background chamber pressure during the growth process. This method leads to co-evaporated MAPbI 3 film with graded Fermi levels across the thickness. Here it is proved that this growth process is beneficial for p-i-n PSCs as it can guarantee a favorable energy alignment at the charge selective interfaces. Co-evaporated p-i-n PSCs, with different hole transporting layers, consistently achieve power conversion efficiency (PCE) over 20% with a champion value of 20.6%, one of the highest reported to date. The scaled-up p-i-n PSCs, with active areas of 1 and 1.96 cm 2 , achieved the record PCEs of 19.1% and 17.2%, respectively, while the flexible PSCs reached a PCE of 19.3%. Unencapsulated PSCs demonstrate remarkable long-term stability, retaining ≈90% of their initial PCE when stored in ambient for 1000 h. These PSCs also preserve over 80% of their initial PCE after 500 h of thermal aging at 85 °C.

Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decisionmaking, requiring the transfer of large amounts of data from periphery to central processors, at the cost of wiring, latency, fault tolerance and robustness. We envision a decentralized approach where intelligence is embedded in the sensing nodes, using a unique neuromorphic methodology to extract relevant information in robotic skins. Here we specifically address pain perception and the association of nociception with tactile perception to trigger the escape reflex in a sensorized robotic arm. The proposed system comprises self-healable materials and memtransistors as enabling technologies for the implementation of neuromorphic nociceptors, spiking local associative learning and communication. Configuring memtransistors as gated-threshold and-memristive switches, the demonstrated system features in-memory edge computing with minimal hardware circuitry and wiring, and enhanced fault tolerance and robustness.

An N-pivot diglycolamide extractant (DGA-TREN) was synthesized for the first time and its complexation behaviour was studied towards trivalent lanthanide/actinide ions. The solvent extraction studies suggested a unique selectivity reversal in the extraction of trivalent actinides versus trivalent lanthanides which was observed performing extraction studies in an ionic liquid vis-à-vis a molecular diluent for a tripodal TREN-based diglycolamide ligand (DGA-TREN) vs. a tripodal diglycolamide ligand (T-DGA) which may have great significance in radioactive waste remediation. The nature of the bonding to Eu(3+) ion was investigated by EXAFS as well as by DFT calculations.

A highly efficient, low-cost (precious-metal-free), highly stable nanohybrid electrocatalyst containing carbon-supported molybdenum carbide and nitride nanoparticles of size ranging from 8 to 12 nm exhibit excellent HER catalytic activity.

Shallow feed-forward networks are incapable of addressing complex tasks such as natural language processing that require learning of temporal signals. To address these requirements, we need deep neuromorphic architectures with recurrent connections such as deep recurrent neural networks. However, the training of such networks demand very high precision of weights, excellent conductance linearity and low write-noise-not satisfied by current memristive implementations. Inspired from optogenetics, here we report a neuromorphic computing platform comprised of photo-excitable neuristors capable of in-memory computations across 980 addressable states with a high signal-to-noise ratio of 77. The large linear dynamic range, low write noise and selective excitability allows high fidelity opto-electronic transfer of weights with a two-shot write scheme, while electrical in-memory inference provides energy efficiency. This method enables implementing a memristive deep recurrent neural network with twelve trainable layers with more than a million parameters to recognize spoken commands with >90% accuracy.

XANES study at Fe edge (a) and signature of ferromagnetism (b) of Zn1−xFexO nanoparticles.

Structural, local structural and optical properties of sol–gel derived Zn1−xCrxO (0 ≤ x ≤ 0.06) nanoparticles have been thoroughly studied by several complementary techniques.