Among the many important challenges facing the development of Li-air batteries, understanding the electrolyte's role in producing the appropriate reversible electrochemistry (i.e., 2Li(+) + O2 + 2e(-) ↔ Li2O2) is critical. Quantitative differential electrochemical mass spectrometry (DEMS), coupled with isotopic labeling of oxygen gas, was used to study Li-O2 electrochemistry in various solvents, including carbonates (typical Li ion battery solvents) and dimethoxyethane (DME). In conjunction with the gas-phase DEMS analysis, electrodeposits formed during discharge on Li-O2 cell cathodes were characterized using ex situ analytical techniques, such as X-ray diffraction and Raman spectroscopy. Carbonate-based solvents were found to irreversibly decompose upon cell discharge. DME-based cells, however, produced mainly lithium peroxide on discharge. Upon cell charge, the lithium peroxide both decomposed to evolve oxygen and oxidized DME at high potentials. Our results lead to two conclusions; (1) coulometry has to be coupled with quantitative gas consumption and evolution data to properly characterize the rechargeability of Li-air batteries, and (2) chemical and electrochemical electrolyte stability in the presence of lithium peroxide and its intermediates is essential to produce a truly reversible Li-O2 electrochemistry.

Heterogeneous electrocatalysis has become a focal point in rechargeable Li-air battery research to reduce overpotentials in both the oxygen reduction (discharge) and especially oxygen evolution (charge) reactions. In this study, we show that past reports of traditional cathode electrocatalysis in nonaqueous Li-O(2) batteries were indeed true, but that gas evolution related to electrolyte solvent decomposition was the dominant process being catalyzed. In dimethoxyethane, where Li(2)O(2) formation is the dominant product of the electrochemistry, no catalytic activity (compared to pure carbon) is observed using the same (Au, Pt, MnO(2)) nanoparticles. Nevertheless, the onset potential of oxygen evolution is only slightly higher than the open circuit potential of the cell, indicating conventional oxygen evolution electrocatalysis may be unnecessary.

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to three computing paradigms: spiking neural networks (SNNs), deep neural networks (DNNs), and 'Memcomputing'. In SNNs, NVM synaptic connections are updated by a local learning rule such as spike-timing-dependent-plasticity, a computational approach directly inspired by biology. For DNNs, NVM arrays can represent matrices of synaptic weights, implementing the matrix-vector multiplication needed for algorithms such as backpropagation in an analog yet massively-parallel fashion. This approach could provide significant improvements in power and speed compared to GPU-based DNN training, for applications of commercial significance. We then survey recent research in which different types of NVM devices-including phase change memory, conductive-bridging RAM, filamentary and nonfilamentary RRAM, and other NVMs-have been proposed, either as a synapse or as a neuron, for use within a neuromorphic computing application. The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability.

Neural-network training can be slow and energy intensive, owing to the need to transfer the weight data for the network between conventional digital memory chips and processor chips. Analogue non-volatile memory can accelerate the neural-network training algorithm known as backpropagation by performing parallelized multiply-accumulate operations in the analogue domain at the location of the weight data. However, the classification accuracies of such in situ training using non-volatile-memory hardware have generally been less than those of software-based training, owing to insufficient dynamic range and excessive weight-update asymmetry. Here we demonstrate mixed hardware-software neural-network implementations that involve up to 204,900 synapses and that combine long-term storage in phase-change memory, near-linear updates of volatile capacitors and weight-data transfer with 'polarity inversion' to cancel out inherent device-to-device variations. We achieve generalization accuracies (on previously unseen data) equivalent to those of software-based training on various commonly used machine-learning test datasets (MNIST, MNIST-backrand, CIFAR-10 and CIFAR-100). The computational energy efficiency of 28,065 billion operations per second per watt and throughput per area of 3.6 trillion operations per second per square millimetre that we calculate for our implementation exceed those of today's graphical processing units by two orders of magnitude. This work provides a path towards hardware accelerators that are both fast and energy efficient, particularly on fully connected neural-network layers.

Quantitative differential electrochemical mass spectrometry (DEMS) is used to measure the Coulombic efficiency of discharge and charge [(e(-)/O2)dis and (e(-)/O2)chg] and chemical rechargeability (characterized by the O2 recovery efficiency, OER/ORR) for Li-O2 electrochemistry in a variety of nonaqueous electrolytes. We find that none of the electrolytes studied are truly rechargeable, with OER/ORR <90% for all. Our findings emphasize that neither the overpotential for recharge nor capacity fade during cycling are adequate to assess rechargeability. Coulometry has to be coupled to quantitative measurements of the chemistry to measure the rechargeability truly. We show that rechargeability in the various electrolytes is limited both by chemical reaction of Li2O2 with the solvent and by electrochemical oxidation reactions during charging at potentials below the onset of electrolyte oxidation on an inert electrode. Possible mechanisms are suggested for electrolyte decomposition, which taken together, impose stringent conditions on the liquid electrolyte in Li-O2 batteries.

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Nonvolatile RAM using resistance contrast in phase-change materials [or phase-change RAM (PCRAM)] is a promising technology for future storage-class memory. However, such a technology can succeed only if it can scale smaller in size, given the increasingly tiny memory cells that are projected for future technology nodes (i.e., generations). We first discuss the critical aspects that may affect the scaling of PCRAM, including materials properties, power consumption during programming and read operations, thermal cross-talk between memory cells, and failure mechanisms. We then discuss experiments that directly address the scaling properties of the phase-change materials themselves, including studies of phase transitions in both nanoparticles and ultrathin films as a function of particle size and film thickness. This work in materials directly motivated the successful creation of a series of prototype PCRAM devices, which have been fabricated and tested at phase-change material cross-sections with extremely small dimensions as low as 3 nm • 20 nm. These device measurements provide a clear demonstration of the excellent scaling potential offered by this technology, and they are also consistent with the scaling behavior predicted by extensive device simulations. Finally, we discuss issues of device integration and cell design, manufacturability, and reliability.

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