Ultra High Pressure Hydrogen Studies

Hydrogen has been called the fuel of the future, and as it’s non- renewable counterparts become scarce the economic viability of hydrogen gains traction. The potential of hydrogen is marked by its high mass specific energy density and wide applicability as a fuel in fuel cell vehicles and homes. However hydrogen’s volume must be reduced via pressurization or liquefaction in order to make it more transportable and volume efficient. Currently the vast majority of industrially produced hydrogen comes from steam reforming of natural gas. This practice yields low-pressure gas which must then be compressed at considerable cost and uses fossil fuels as a feedstock leaving behind harmful CO and CO2 gases as a by-product. The second method used by industry to produce hydrogen gas is low pressure electrolysis. In comparison the electrolysis of water at low pressure can produce pure hydrogen and oxygen gas with no harmful by-products using only water as a feedstock, but it will still need to be compressed before use. Multiple theoretical works agree that high pressure electrolysis could reduce the energy losses due to product gas compression. However these works openly admit that their projected gains are purely theoretical and ignore the practical limitations and resistances of a real life high pressure system. The goal of this work is to experimentally confirm the proposed thermodynamic gains of ultra-high pressure electrolysis in alkaline solution and characterize the behavior of a real life high pressure system.

Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays.

Determination of copy number variants (CNVs) inferred in genome wide single nucleotide polymorphism arrays has shown increasing utility in genetic variant disease associations. Several CNV detection methods are available, but differences in CNV call thresholds and characteristics exist. We evaluated the relative performance of seven methods: circular binary segmentation, CNVFinder, cnvPartition, gain and loss of DNA, Nexus algorithms, PennCNV and QuantiSNP. Tested data included real and simulated Illumina HumHap 550 data from the Singapore cohort study of the risk factors for Myopia (SCORM) and simulated data from Affymetrix 6.0 and platform-independent distributions. The normalized singleton ratio (NSR) is proposed as a metric for parameter optimization before enacting full analysis. We used 10 SCORM samples for optimizing parameter settings for each method and then evaluated method performance at optimal parameters using 100 SCORM samples. The statistical power, false positive rates, and receiver operating characteristic (ROC) curve residuals were evaluated by simulation studies. Optimal parameters, as determined by NSR and ROC curve residuals, were consistent across datasets. QuantiSNP outperformed other methods based on ROC curve residuals over most datasets. Nexus Rank and SNPRank have low specificity and high power. Nexus Rank calls oversized CNVs. PennCNV detects one of the fewest numbers of CNVs.