The Physio-Chemical Properties for the Interior of Enceladus

We have reviewed the current physical and chemical conditions of the Enceladus sub-surface environment, including the composition, temperature, pH and pressure. Here we have defined some of these parameters and, through the aid of modelling, will define and refine the remaining parameters needed for our experimental work. Simulations of the chemical reactions occurring within Enceladus can then be carried out to advance our understanding of the internal environment of Enceladus and help evaluate its potential habitability. Once a better understanding of the chemical reactions occurring at the rock-water interface has been carried out, then potential analogues on Earth can be evaluated and known microbial life can be tested to see if it could survive the conditions of Enceladus

The "zeroth law" of turbulence: Isotropic turbulence simulations revisited

The dimensionless kinetic energy dissipation rate C_epsilon is estimated from numerical simulations of statistically stationary isotropic box turbulence that is slightly compressible. The Taylor microscale Reynolds number Re_lambda range is 20 < Re_lambda < 220 and the statistical stationarity is achieved with a random phase forcing method. The strong Re_lambda dependence of C_epsilon abates when Re_lambda approx. 100 after which C_epsilon slowly approaches approx 0.5 a value slightly different to previously reported simulations but in good agreement with experimental results. If C_epsilon is estimated at a specific time step from the time series of the quantities involved it is necessary to account for the time lag between energy injection and energy dissipation. Also, the resulting value can differ from the ensemble averaged value by up to +-30%. This may explain the spread in results from previously published estimates of C_epsilon.Comment: 7 pages, 7 figures. Submitted to Phys. Rev.

Widespread evidence for heterogeneous accretion of the terrestrial planets and planetisimals

The abundance and relative proportion of highly siderophile elements (HSEs) in Earth’s mantle deviate from those predicted by low-pressure equilibrium partitioning between metal and silicate during formation of the core. For many elements, high-pressure equilibration in a deep molten silicate layer (or ‘magma ocean’) may account for this discrepancy [1], but some highly siderophile element abundances demand the late addition, a ‘late veneer’, of extraterrestrial material (i.e. heterogeneous accretion) after core formation was complete [2]. Siderophile elements in smaller asteroidal bodies will not be affected by high-pressure metal-silicate equilibration and so, with highly efficient core formation [3] and if a ‘late veneer’ is absent, significant differences in the proportions of HSEs can be anticipated. Here we present new HSE abundance and 187Os/188Os isotope data for basaltic meteorites, the HEDs (howardites, eucrites and diogenites thought to sample the asteroid 4 Vesta), anomalous eucrites (considered to be from distinct Vesta-like parent bodies) angrites and aubrites (from unidentified parent bodies) and SNCs (thought to be from Mars). Our data, taken with those for lunar rocks [4], demonstrate that these igneous meteorites all formed from mantle sources that possessed chondritic (i.e. primitive solar system) elemental and isotope compositions, indicating that late accretion is not unique to Earth, but is a common feature of differentiated planets and asteroidal bodies. Variations in the total HSE abundance suggest that the proportion of ‘late veneer’ added is a simple consequence of the size of each body (cross-section and/or gravitational-attraction), and may account for the volatile element budget, and the oxidationstate of Earth, Mars, the Moon and Vesta

The rocks from space initiative and the space safari

This paper reports the successes of a new initiative in the UK using electronic resources, such as virtual learning environments and e-classrooms, for planetary and space science public engagement activities

Comprehensive Organic Analysis of Antartic Micrometeorites

Introduction: Micrometeorites (MMs) are thought to be significant contributors of organic material to the early Earth [1], and a variety of techniques have been employed to identify their organic composition [2-6]. These include the identification of key organic groups using combinations of infrared, energy dispersive Xray, electron energy loss and Raman spectroscopy and scanning transmission X-ray microscopy [2-4], highlighting similarities between that of MMs and carbonaceous chondrites. Few studies, however, have focused on the characterisation of individual micrometeoritic organic components. Microscopic L2MS has been used to identify up to C5 polycyclic aromatic hydrocarbons and their alkyl derivatives [5]. A combination of ionexchange chromatography and fluorimetric detection has also been successful in identifying a number of protein amino acids including glycine and alanine [6]. We have previously reported a method to analyse ?g-sized quantities of extraterrestrial materials, with prior application to assessing organic volatile release from MM atmospheric entry heating simulations [7]. In this study we utilise this technique to characterise the organic composition of Antarctic terrestrial particles and MMs collected in 1994 from Cap-Prudhomme [8]

Logarithmic scaling in the near-dissipation range of turbulence

A logarithmic scaling for structure functions, in the form $S_p \sim [\ln (r/\eta)]^{\zeta_p}$, where $\eta$ is the Kolmogorov dissipation scale and $\zeta_p$ are the scaling exponents, is suggested for the statistical description of the near-dissipation range for which classical power-law scaling does not apply. From experimental data at moderate Reynolds numbers, it is shown that the logarithmic scaling, deduced from general considerations for the near-dissipation range, covers almost the entire range of scales (about two decades) of structure functions, for both velocity and passive scalar fields. This new scaling requires two empirical constants, just as the classical scaling does, and can be considered the basis for extended self-similarity

Mask-Less Crystalline Silicon Solar Cell (May 2009)

A mask-less crystalline silicon solar cell was made by using a surface texturing technique coupled with an oblique aluminum evaporation. To achieve this, trenches with a steep sidewall are mechanically grooved into the bulk silicon using the KS 775 Wafer Saw. More importantly, metal evaporation with the CVC evaporator at angles near parallel to the wafer surface allows deposition to occur along the side of the trenches creating the self-aligning front metal contacts. Of the four solar cells that made it through the processing, only one solar cell showed diode like 1-V characteristics. The dark conditions shows a diode 1-V where current doesn’t flow with a negative applied voltage and in the forward applied voltage, there is a turn on voltage around 0.6V, typical of a silicon diode. This is followed by an exponential gain in current. The n value of the diode is under dark conditions is 1.7. Under illuminated conditions, the I-V curve shows a dramatic negative current for voltages below 0.25V. This isn’t the I-V curve of a solar cell but it does show that this device is light sensitive. The other three solar cells made are resistors with resistances of 4 Ω, 2 Ω and 19.2 Ω for wafers 3, 4 and 5 respectively. The shorts on the solar cells are due to a nonuniformly coated N-250 spin on glass (SOG) for the n+ layer on the p type wafer. Air pockets remained in the trenches and kept certain spots on the wafer surface to remain p. When the Al front contacts and bus paste are applied to the solar cells, it creates the p-n junction shorts. This was confirmed by breaking wafer 3 into smaller pieces where one of the pieces had a uniform n+ layer that showed I-V curves of a diode

Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning

In this article we present the supervised iterative projections and rotations (s-ipr) algorithm, a method for learning discriminative incoherent subspaces from data. We derive s-ipr as a supervised extension of our previously proposed iterative projections and rotations (ipr) algorithm for incoherent dictionary learning, and we employ it to learn incoherent sub-spaces that model signals belonging to different classes. We test our method as a feature transform for supervised classification, first by visualising transformed features from a synthetic dataset and from the ‘iris’ dataset, then by using the resulting features in a classification experiment