We report Stark shift measurements for {sup 121}Sb donor electron spins in silicon using pulsed electron spin resonance. Interdigitated metal gates on top of a Sb-implanted {sup 28}Si epi-layer are used to apply electric fields. Two Stark effects are resolved: a decrease of the hyperfine coupling between electron and nuclear spins of the donor and a decrease in electron Zeeman g-factor. The hyperfine term prevails at X-band magnetic fields of 0.35T, while the g-factor term is expected to dominate at higher magnetic fields. A significant linear Stark effect is also resolved presumably arising from strain.

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The strength and deformation behavior of ball-milled, iron-base materials containing nano-scale subgrains have been evaluated. As reported by several authors, nanosubgrains form during the early stages of ball milling as a result of severe plastic deformation inherent in the ball milling process. The strength for these nano-scale subgrains are compared with the strength of larger-scale subgrains in iron and iron-base alloys produced by traditional mechanical working. The data covers over 2 orders of magnitude in subgrain size (from 30 nm to 6 {micro}m) and shows a continuous pattern of behavior. For all materials studied, the strength varied as {lambda}{sup -1}, where {lambda} is the subgrain size. Strengthening from subgrains was found to breakdown at a much smaller subgrain size than strengthening from grains. In addition, the ball-milled materials showed significant strengthening contributions from nano-scale oxide particles. Shear bands are developed during testing of ball-milled materials containing ultra-fine subgrains. A model for shear band development in nano-scale subgrains during deformation has also been developed. The model predicts a strain state of uniaxial compression in the shear band with a strain of -1.24. Subgrains are shown to offer the opportunity for high strength and good work hardening with the absence of yield …

Remediation by soil-vapor extraction has been used for over a decade at Lawrence Livermore National Laboratory (LLNL). We have found that natural changes in environmental conditions affect the rate of soil-vapor extraction. Data on flow rate observations collected over this time are compared to in-situ measurements of several different environmental parameters (soil-gas pressure, soil-temperature, soil-moisture, Electrical Resistance Tomography (ERT), rainfall and barometric pressure). Environmental changes that lead to increased soil-moisture are associated with reduced soil-vapor extraction flow rates. We have found that the use of higher extraction vacuums combined with dual-phase extraction can help to increase pneumatic conductivity when vadose zone saturation is a problem. Daily changes in barometric pressure and soil-gas temperature were found to change flow rate measurements by as much as 10% over the course of a day.

The charge-state-resolved ion energy distributions (IEDs) in filtered aluminum vacuum arc plasmas were measured and analyzed at different oxygen and argon pressures in the range 0.5 8.0 mTorr. A significant reduction of the ion energy was detected as the pressure was increased, most pronounced in an argon environment and for the higher charge states. The corresponding average charge state decreased from 1.87 to 1.0 with increasing pressure. The IEDs of all metal ions in oxygen were fitted with shifted Maxwellian distributions. The results show that it is possible to obtain a plasma composition with a narrow charge-state distribution as well as a narrow IED. These data may enable tailoring thin-film properties through selecting growth conditions that are characterized by predefined charge state and energy distributions.

Metagenomics projects based on shotgun sequencing of populations of micro-organisms yield insight into protein families. We used sequence similarity clustering to explore proteins with a comprehensive dataset consisting of sequences from available databases together with 6.12 million proteins predicted from an assembly of 7.7 million Global Ocean Sampling (GOS) sequences. The GOS dataset covers nearly all known prokaryotic protein families. A total of 3,995 medium- and large-sized clusters consisting of only GOS sequences are identified, out of which 1,700 have no detectable homology to known families. The GOS-only clusters contain a higher than expected proportion of sequences of viral origin, thus reflecting a poor sampling of viral diversity until now. Protein domain distributions in the GOS dataset and current protein databases show distinct biases. Several protein domains that were previously categorized as kingdom specific are shown to have GOS examples in other kingdoms. About 6,000 sequences (ORFans) from the literature that heretofore lacked similarity to known proteins have matches in the GOS data. The GOS dataset is also used to improve remote homology detection. Overall, besides nearly doubling the number of current proteins, the predicted GOS proteins also add a great deal of diversity to known protein families and shed …