Advanced onboard storage concepts for natural gas-fueled automotive vehicles

The evaluation of several advanced concepts for storing natural gas at reduced pressure is presented. The advanced concepts include adsorption on high surface area carbon, adsorption in high porosity zeolite, storage in clathration compounds, and storage by dissolution in liquid solvents. High surface area carbons with high packing density are the best low pressure storage mediums. A simple mathematical model is used to compare adsorption storage on a state of the art carbon with compression storage. The model indicates that a vehicle using adsorption storage of natural gas at 3.6 MPa will have 36 percent of the range, on the EPA city cycle, of a vehicle operating on a compression storage system having the same physical size and a peak storage pressure of 21 MPa. Preliminary experiments and current literature suggest that the storage capacity of state of the art carbons could be improved by as much as 50 percent, and that adsorption systems having a capacity equal to compression storage at 14 MPa are possible without exceeding a maximum pressure of 3.6 MPa

Approximate Bayesian Confidence Intervals For The Variance Of A Gaussian Distribution

The aim of the present study is to obtain and compare confidence intervals for the variance of a Gaussian distribution. Considering respectively the square error and the Higgins-Tsokos loss functions, approximate Bayesian confidence intervals for the variance of a normal population are derived. Using normal data and SAS software, the obtained approximate Bayesian confidence intervals will then be compared to the ones obtained with the well known classical method. The Bayesian approach relies only on the observations. It is shown that the proposed approximate Bayesian approach relies only on the observations. The classical method, that uses the Chi-square statistic, does not always yield the best confidence intervals

A New Approximate Bayesian Approach for Decision Making About the Variance of a Gaussian Distribution Versus the Classical Approach

Rules of decision-making about the variance of a Gaussian distribution are obtained and compared. Considering the square error loss function, an approximate Bayesian decision rule for the variance of a normal population is derived. Using normal data and SAS software, the obtained approximate Bayesian test results were compared to their counterparts obtained with the well-known classical decision rule. It is shown that the proposed approximate Bayesian decision rule relies only on observations. The classical decision rule, which uses the Chi-square statistic, does not always yield the best results: the proposed approach often performs better

Approximate Bayesian Confidence Intervals for The Mean of a Gaussian Distribution Versus Bayesian Models

This study obtained and compared confidence intervals for the mean of a Gaussian distribution. Considering the square error and the Higgins-Tsokos loss functions, approximate Bayesian confidence intervals for the mean of a normal population are derived. Using normal data and SAS software, the obtained approximate Bayesian confidence intervals were compared to a published Bayesian model. Whereas the published Bayesian method is sensitive to the choice of the hyper-parameters and does not always yield the best confidence intervals, it is shown that the proposed approximate Bayesian approach relies only on the observations and often performs better

Approximate Bayesian Confidence Intervals for the Mean of an Exponential Distribution Versus Fisher Matrix Bounds Models

The aim of this article is to obtain and compare confidence intervals for the mean of an exponential distribution. Considering respectively the square error and the Higgins-Tsokos loss functions, approximate Bayesian confidence intervals for parameters of exponential population are derived. Using exponential data, the obtained approximate Bayesian confidence intervals will then be compared to the ones obtained with Fisher Matrix bounds method. It is shown that the proposed approximate Bayesian approach relies only on the observations. The Fisher Matrix bounds method, that uses the z-table, does not always yield the best confidence intervals, and the proposed approach often performs better

Selective epitaxial growth of graphene on SiC

We present an innovative method of selective epitaxial growth of few layers graphene (FLG) on a pre-patterned SiC substrate. The methods involves, successively, the sputtering of a thin AlN layer on top of a mono-crystalline SiC substrate and, then, patterning it with e-beam lithography (EBL) and wet etching. The sublimation of few atomic layers of Si from the SiC substrate occurs only through the selectively etched AlN layer. The presence of the Raman G-band at ~1582 cm-1 in the AlN-free areas is used to validate the concept, it gives absolute evidence of the selective FLG growth.Comment: comments: 3 pages, reference 3 replace

Promoção de eventos culturais na Biblioteca da Embrapa Florestas: responsabilidade social e cultural.

Apresenta uma reflexão sobre a responsabilidade e compromisso social das bibliotecas e descreve ações desenvolvidas por uma biblioteca especializada na área de pesquisa com o objetivo de promover a biblioteca, incentivar atividades culturais, bem como de sociabilizar a mesma na empresa