Electric eels have been the subject of investigation and curiosity for centuries . They use high voltage to track  and control  prey, as well as to exhaust prey by causing involuntary fatigue through remote activation of prey muscles . But their most astonishing behavior is the leaping attack, during which eels emerge from the water to directly electrify a threat [5, 6]. This unique defense has reportedly been used against both horses  and humans . Yet the dynamics of the circuit that develops when a living animal is contacted and the electrical power transmitted to the target have not been directly investigated. In this study, the electromotive force and circuit resistances that develop during an eel’s leaping behavior were determined. Next, the current that passed through a human subject during the attack was measured. The results allowed each variable in the equivalent circuit to be estimated. Findings can be extrapolated to a range of different eel sizes that might be encountered in the wild. Despite the comparatively small size of the eel used in this study, electrical currents in the target peaked at 40-50 mA, greatly exceeding thresholds for nociceptor activation reported for both humans  and horses [10, 11]. No subjective sensation of involuntary tetanus was reported, and aversive sensations were restricted to the affected limb. Results suggest that the main purpose of the leaping attack is to strongly deter potential eel predators by briefly causing intense pain. Apparently a strong offense is the eel’s best defense.
Neuromorphic computing promises to markedly improve the efficiency of certain computational tasks, such as perception and decision-making. Although software and specialized hardware implementations of neural networks have made tremendous accomplishments, both implementations are still many orders of magnitude less energy efficient than the human brain. We demonstrate a new form of artificial synapse based on dynamically reconfigurable superconducting Josephson junctions with magnetic nanoclusters in the barrier. The spiking energy per pulse varies with the magnetic configuration, but in our demonstration devices, the spiking energy is always less than 1 aJ. This compares very favorably with the roughly 10 fJ per synaptic event in the human brain. Each artificial synapse is composed of a Si barrier containing Mn nanoclusters with superconducting Nb electrodes. The critical current of each synapse junction, which is analogous to the synaptic weight, can be tuned using input voltage spikes that change the spin alignment of Mn nanoclusters. We demonstrate synaptic weight training with electrical pulses as small as 3 aJ. Further, the Josephson plasma frequencies of the devices, which determine the dynamical time scales, all exceed 100 GHz. These new artificial synapses provide a significant step toward a neuromorphic platform that is faster, more energy-efficient, and thus can attain far greater complexity than has been demonstrated with other technologies.
Mechanical energy harvesters are needed for diverse applications, including self-powered wireless sensors, structural and human health monitoring systems, and the extraction of energy from ocean waves. We report carbon nanotube yarn harvesters that electrochemically convert tensile or torsional mechanical energy into electrical energy without requiring an external bias voltage. Stretching coiled yarns generated 250 watts per kilogram of peak electrical power when cycled up to 30 hertz, as well as up to 41.2 joules per kilogram of electrical energy per mechanical cycle, when normalized to harvester yarn weight. These energy harvesters were used in the ocean to harvest wave energy, combined with thermally driven artificial muscles to convert temperature fluctuations to electrical energy, sewn into textiles for use as self-powered respiration sensors, and used to power a light-emitting diode and to charge a storage capacitor.
Despite several years of research into graphene electronics, sufficient on/off current ratio I(on)/I(off) in graphene transistors with conventional device structures has been impossible to obtain. We report on a three-terminal active device, a graphene variable-barrier “barristor” (GB), in which the key is an atomically sharp interface between graphene and hydrogenated silicon. Large modulation on the device current (on/off ratio of 10(5)) is achieved by adjusting the gate voltage to control the graphene-silicon Schottky barrier. The absence of Fermi-level pinning at the interface allows the barrier’s height to be tuned to 0.2 electron volt by adjusting graphene’s work function, which results in large shifts of diode threshold voltages. Fabricating GBs on respective 150-mm wafers and combining complementary p- and n-type GBs, we demonstrate inverter and half-adder logic circuits.
Monolayer graphene sheets were deposited on a transparent and flexible polydimethylsiloxane (PDMS) substrate, and a tensile strain was loaded by stretching the substrate in one direction. It was found that an electric potential difference between stretched and static monolayer graphene sheets reached 8 mV when the strain was 5%. Theoretical calculations for the band structure and total energy revealed an alternative way to experimentally tune the band gap of monolayer graphene, and induce the generation of electricity.
The rechargeable aprotic lithium-air (Li-O2) battery is a promising potential technology for next-generation energy storage, but its practical realization still faces many challenges. In contrast to the standard Li-O2 cells, which cycle via the formation of Li2O2, we used a reduced graphene oxide electrode, the additive LiI, and the solvent dimethoxyethane to reversibly form and remove crystalline LiOH with particle sizes larger than 15 micrometers during discharge and charge. This leads to high specific capacities, excellent energy efficiency (93.2%) with a voltage gap of only 0.2 volt, and impressive rechargeability. The cells tolerate high concentrations of water, water being the dominant proton source for the LiOH; together with LiI, it has a decisive impact on the chemical nature of the discharge product and on battery performance.
Objectives A finite element model of the human coiled cochlea was used to model the voltage distribution due to stimulation by the individual electrodes of a cochlear implant. The scalar position of the electrode array was also varied in order to investigate its effect on the voltage distribution. Multi-electrode current focusing methods were then investigated, with the aim of increasing spatial selectivity. Methods Simultaneous current focusing is initially achieved, as in previous publications, by calculating the input currents to the 22 electrodes that best separates the voltages at these electrode positions. The benefits of this electrode focusing strategy do not, however, entirely carry over to the predicted voltage distributions at the position of the spiral ganglion cells, where excitation is believed to occur. A novel focusing strategy is then simulated, which compensates for the impedances between the currents at the electrode sites and the voltage distribution directly at the position of the spiral ganglion cells. Results The new strategy produces much better focusing at the sites of the spiral ganglion cells, as expected, but at the cost of increased current requirements. Regularization was introduced in order to reduce current requirements, which also reduced the sensitivity of the solution to uncertainties in the impedance matrix, so that improved focusing was achieved with similar current requirements to that of electrode focusing. Discussion Although such focusing strategies cannot be achieved in practice at the moment, since the responses from the electrodes to the neural sites cannot be determined with currently available recording methods, these results do support the feasibility of a more effective focusing strategy, which may provide improved spectral resolution leading to improved perception of sound.
- The Journal of sports medicine and physical fitness
- Published about 3 years ago
Metabolic power has not yet been investigated within elite Gaelic football. The aim of the current investigation was to compare the metabolic power demands between positional groups and examine the temporal profile of elite Gaelic football match play.
Energy dense power sources are critical to the development of compact, remote sensors for terrestrial and space applications. Nuclear batteries using β(-)-emitting radioisotopes possess energy densities 1000 times greater than chemical batteries. Their power generation is a function of β(-) flux saturation point relative to the planar (2D) configuration, β(-) range, and semiconductor converter. An approach to increase power density in a beta-photovoltaic (β-PV) nuclear battery is described. By using volumetric (3D) configuration, the radioisotope, nickel-63 ((63)Ni) in a chloride solution was integrated in a phosphor film (ZnS:Cu,Al) where the β(-) energy is converted into optical energy. The optical energy was converted to electrical energy via an indium gallium phosphate (InGaP) photovoltaic (PV) cell, which was optimized for low light illumination and closely matched to radioluminescence (RL) spectrum. With 15mCi of (63)Ni activity, the 3D configuration energy values surpassed 2D configuration results. The highest total power conversion efficiency (ηt) of 3D configuration was 0.289% at 200µm compared 0.0638% for 2D configuration at 50µm. The highest electrical power and ηt for the 3D configuration were 3.35 nWe/cm(2) at an activity of 30mCi and 0.289% at an activity of 15mCi, respectively. By using 3D configuration, the interaction space between the radioisotope source and scintillation material increased, allowing for significant electrical energy output, relative to the 2D configuration. These initial results represent a first step to increase nuclear battery power density from microwatts to milliwatts per 1000cm(3) with the implementation of higher energy β(-) sources.
“Smart Pavement” is an emerging infrastructure for various on-road applications in transportation and road engineering. However, existing road monitoring solutions demand a certain periodic maintenance effort due to battery life limits in the sensor systems. To this end, we present an end-to-end self-powered wireless sensor-ePave-to facilitate smart and autonomous pavements. The ePave system includes a self-power module, an ultra-low-power sensor system, a wireless transmission module and a built-in power management module. First, we performed an empirical study to characterize the piezoelectric module in order to optimize energy-harvesting efficiency. Second, we developed an integrated sensor system with the optimized energy harvester. An adaptive power knob is designated to adjust the power consumption according to energy budgeting. Finally, we intensively evaluated the ePave system in real-world applications to examine the system’s performance and explore the trade-off.