Nanostructured Fe-N-C as bifunctional catalysts for oxygen reduction and hydrogen evolution

The development of electrocatalysts for energy conversion and storage devices is of paramount importance to promote sustainable development. Among the different families of materials, catalysts based on transition metals supported on a nitrogen-containing carbon matrix have been found to be effective catalysts toward oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER) with high potential to replace conventional precious metal-based catalysts. In this work, we developed a facile synthesis strategy to obtain a Fe-N-C bifunctional ORR/HER catalysts, involving wet impregnation and pyrolysis steps. Iron (II) acetate and imidazole were used as iron and nitrogen sources, respectively, and functionalized carbon black pearls were used as conductive support. The bifunctional performance of the Fe-N-C catalyst toward ORR and HER was investigated by cyclic voltammetry, rotating ring disk electrode experiments, and electrochemical impedance spectroscopy in alkaline environment. ORR onset potential and half-wave potential were 0.95 V and 0.86 V, respectively, indicating a competitive performance in comparison with the commercial platinum-based catalyst. In addition, Fe-N-C had also a good HER activity, with an overpotential of 478 mV @10 mAcm(-2) and Tafel slope of 133 mVdec(-1), demonstrating its activity as bifunctional catalyst in energy conversion and storage devices, such as alkaline microbial fuel cell and microbial electrolysis cells

Committee Machines—A Universal Method to Deal with Non-Idealities in RRAM-Based Neural Networks

Artificial neural networks (ANNs) are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Recent years have seen an emergence of research in hardware that strives to break the bottleneck of von Neumann architecture and optimise the data flow; namely to bring memory and computing closer together. One of the most often suggested solutions is the physical implementation of ANNs in which their synaptic weights are realised with analogue resistive devices, such as resistive random-access memory (RRAM). However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known concept in computer science -- committee machine (CM) -- in the context of RRAM-based neural networks. Using simulations and experimental data from three different types of RRAM devices, we show that CMs employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, programming non-linearities, random telegraph noise, cycle-to-cycle variability and line resistance. Importantly, we show that the accuracy can be improved even without increasing the number of devices

The over-reset phenomenon in Ta2O5 RRAM device investigated by the RTN-based defect probing technique

IEEE Despite the tremendous efforts in the past decade devoted to the development of filamentary resistive-switching devices (RRAM), there is still a lack of in-depth understanding of its over-reset phenomenon. At higher reset stop voltages that exceed a certain threshold, the resistance at high resistance state reduces, leading to an irrecoverable window reduction. The over-reset phenomenon limits the maximum resistance window that can be achieved by using a higher Vreset, which also degrades its potential in applications such as multi-level memory and neuromorphic synapses. In this work, the over-reset is investigated by cyclic reset operations with incremental stop voltages, and is explained by defect generation in the filament constriction region of Ta2O5 RRAM devices. This is supported by the statistical spatial defects profile obtained from the random telegraph noise based defect probing technique. The impact of forming compliance current on the over-reset is also evaluated

True Random Number Generator Based on Switching Probability of Volatile Gexse1-X Ovonic Threshold Switching Selectors

GexSe1-x Ovonic Threshold Switching (OTS) selector is a promising candidate to suppress the sneak current paths in resistive switching memory arrays. A novel method is developed to quantitatively characterize the variations in the threshold voltage (Vth), the hold voltage (Vhd), and the switching probability dependence on the OTS operation conditions. The time-to-switch-on/off (ton/toff) at a constant VOTS follows the Weibull distribution, based on which the dependence of switching probability on pulse waveform, bias, and time can be extracted and extrapolated. Based on this analysis, a novel technique for true random number generator (TRNG) application is proposed. The inherent variability in OTS threshold voltage results in a bimodal distribution of on/off states which can be easily converted into digital bits. The experimental evaluation shows that the proposed TRNG enables the generation of high-quality random bits that passed 12 tests in the NIST statistical test suite without complex external circuits for post-processing

Committee Machines—A Universal Method to Deal with Non-Idealities in Memristor-Based Neural Networks

Arti ficial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergence of research in machine learning hardware that strives to bring memory and computing closer together. A popular approach is to realise artifi cial neural networks in hardware by implementing their synaptic weights using memristive devices. However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known concept in computer science|committee machines|in the context of memristor-based neural networks. Using simulations and experimental data from three different types of memristive devices, we show that committee machines employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, device-to-device variability, random telegraph noise and line resistance. Importantly, we demonstrate that the accuracy can be improved even without increasing the total number of memristors

Stochastic computing based on volatile GeSe ovonic threshold switching selectors

Stochastic computing (SC) is a special type of digital compute strategy where values are represented by the probability of 1 and 0 in stochastic bit streams, which leads to superior hardware simplicity and error-tolerance. In this paper, we propose and demonstrate SC with GeSe based Ovonic Threshold Switching (OTS) selector devices by exploiting their probabilistic switching behavior. The stochastic bit streams generated by OTS are demonstrated with good computation accuracy in both multiplication operation and image processing circuit. Moreover, the bit distribution has been statistically studied and linked to the collective defect de/localization behavior in the chalcogenide material. Weibull distribution of the delay time supports the origin of such probabilistic switching, facilitates further optimization of the operation condition, and lays the foundation for device modelling and circuit design. Considering its other advantages such as simple structure, fast speed, and volatile nature, OTS is a promising material for implementing SC in a wide range of novel applications, such as image processors, neural networks, control systems and reliability analysis

GeSe-based Ovonic Threshold Switching Volatile True Random Number Generator

In this paper, we propose and demonstrate a novel technique for true random number generator (TRNG) application using GeSe-based Ovonic threshold switching (OTS) selector devices. The inherent variability in OTS threshold voltage results in a bimodal distribution of on/off states which can be easily converted into digital bits. The experimental evaluation shows that the proposed TRNG enables the generation of high-quality random bits that passed 12 tests in the National Institute of Standards and Technology statistical test suite without complex external circuits for post-processing. The randomness is further evidenced by the prediction rate of ∼50% using machine learning algorithm. Compared with the TRNGs based on non-volatile memories, the volatile nature of OTS avoids the reset operation, thus further simplifying the operation and improving the generation frequency

Impact of RTN on Pattern Recognition Accuracy of RRAM-based Synaptic Neural Network

Resistive switching memory devices can be categorized into either filamentary or non-filamentary ones depending on the switching mechanisms. Both types have been investigated as novel synaptic devices in hardware neural networks, but there is a lack of comparative study between them, especially in random telegraph noise (RTN) which could induce large resistance fluctuations. In this work, we analyze the amplitude and occurrence rate of RTN in both Ta2O5 filamentary and TiO2/a-Si (a-VMCO) non-filamentary RRAM devices and evaluate its impact on the pattern recognition accuracy of neural networks. It is revealed that the non-filamentary RRAM has a tighter RTN amplitude distribution and much lower RTN occurrence rate than its filamentary counterpart which leads to negligible RTN impact on recognition accuracy, making it a promising candidate in synaptic application

Dependence of switching probability on operation conditions in GexSe1-x ovonic threshold switching selectors

Ovonic threshold switching (OTS) selector is a promising candidate to suppress the sneak current paths in resistive switching memory (RRAM) arrays. Variations in the threshold voltage (Vth), and the hold voltage (Vhd) have been reported, but a quantitative analysis of the switching probability dependence on the OTS operation conditions is still missing. A novel characterization method is developed in this work, and the time-to-switch-on/off (ton/toff) at a constant VOTS is found following the Weibull distribution, based on which the dependence of switching probability on pulse bias and time can be extracted and extrapolated, and the switching probability can be ensured with appropriately chosen operation conditions. The difference between square and triangle switching pulses is also explained. This provides a practical guidance for predicting the switching probability under different operation conditions and for designing reliable one-selector-one-RRAM (1S1R) arrays

Genome of the Avirulent Human-Infective Trypanosome—Trypanosoma rangeli

Background: Trypanosoma rangeli is a hemoflagellate protozoan parasite infecting humans and other wild and domestic mammals across Central and South America. It does not cause human disease, but it can be mistaken for the etiologic agent of Chagas disease, Trypanosoma cruzi. We have sequenced the T. rangeli genome to provide new tools for elucidating the distinct and intriguing biology of this species and the key pathways related to interaction with its arthropod and mammalian hosts.  Methodology/Principal Findings: The T. rangeli haploid genome is ,24 Mb in length, and is the smallest and least repetitive trypanosomatid genome sequenced thus far. This parasite genome has shorter subtelomeric sequences compared to those of T. cruzi and T. brucei; displays intraspecific karyotype variability and lacks minichromosomes. Of the predicted 7,613 protein coding sequences, functional annotations could be determined for 2,415, while 5,043 are hypothetical proteins, some with evidence of protein expression. 7,101 genes (93%) are shared with other trypanosomatids that infect humans. An ortholog of the dcl2 gene involved in the T. brucei RNAi pathway was found in T. rangeli, but the RNAi machinery is non-functional since the other genes in this pathway are pseudogenized. T. rangeli is highly susceptible to oxidative stress, a phenotype that may be explained by a smaller number of anti-oxidant defense enzymes and heatshock proteins.  Conclusions/Significance: Phylogenetic comparison of nuclear and mitochondrial genes indicates that T. rangeli and T. cruzi are equidistant from T. brucei. In addition to revealing new aspects of trypanosome co-evolution within the vertebrate and invertebrate hosts, comparative genomic analysis with pathogenic trypanosomatids provides valuable new information that can be further explored with the aim of developing better diagnostic tools and/or therapeutic targets