Supercritical carbon dioxide Brayton cycle for concentrated solar power

Supercritical carbon dioxide based Brayton cycle for possible concentrated solar power applications is investigated and compared with trans- and sub-critical operations of the same fluid. Thermal efficiency, specific work output and magnitude of irreversibility generation are used as some of the performance indicators. While the thermal efficiency increases almost linearly with low side pressure in the sub- and trans-critical cycles, it attains a maximum in the supercritical regime at 85 bar after which there are diminishing returns on increasing the low side pressure. It is also found that supercritical cycle is capable of producing power with a thermal efficiency of >30% even at a lower source temperature (820K) and accounting for foreseeable non-idealities albeit with a higher turbine inlet pressure (similar to 300 bar) which is not matched by a conventional sub-critical cycle even with a high source temperature of 978K. The reasons for lower efficiency than in an ideal cycle are extracted from an irreversibility analysis of components, namely, compressor, regenerator, turbine and gas cooler. Low sensitivity to the source temperature and extremely small volumetric flow rates in the supercritical cycle could offset the drawback of high pressures through a compact system.

The effect of cell size and channel density on neuronal information encoding and energy efficiency

Identifying the determinants of neuronal energy consumption and their relationship to information coding is critical to understanding neuronal function and evolution. Three of the main determinants are cell size, ion channel density, and stimulus statistics. Here we investigate their impact on neuronal energy consumption and information coding by comparing single-compartment spiking neuron models of different sizes with different densities of stochastic voltage-gated Na+ and K+ channels and different statistics of synaptic inputs. The largest compartments have the highest information rates but the lowest energy efficiency for a given voltage-gated ion channel density, and the highest signaling efficiency (bits spike(-1)) for a given firing rate. For a given cell size, our models revealed that the ion channel density that maximizes energy efficiency is lower than that maximizing information rate. Low rates of small synaptic inputs improve energy efficiency but the highest information rates occur with higher rates and larger inputs. These relationships produce a Law of Diminishing Returns that penalizes costly excess information coding capacity, promoting the reduction of cell size, channel density, and input stimuli to the minimum possible, suggesting that the trade-off between energy and information has influenced all aspects of neuronal anatomy and physiology.