@article{daystar_venditti_gonzalez_jameel_jett_reeb_2013, title={Impacts of feedstock composition on alcohol yields and greenhouse gas emissions from the NREL thermochemical ethanol conversion process}, volume={8}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84887201884&partnerID=MN8TOARS}, DOI={10.15376/biores.8.4.5261-5278}, abstractNote={There has been great attention focused on the effects of first and second generation biofuels on global warming. The Energy Independence and Security Act (EISA) and the Renewable Fuel Standard (RFS) have mandated production levels and performance criteria of biofuels in the United States. The thermochemical conversion of biomass to ethanol shows potential as a biofuel production pathway. The objective of this research was to examine the alcohol yields and GHG emissions from the thermochemical conversion process for six different feedstocks on a gate-to-gate basis. GHG analyses and life cycle assessments were performed for natural hardwood, loblolly pine, eucalyptus, miscanthus, corn stover, and switchgrass feedstocks using a NREL thermochemical model and SimaPro. Alcohol yield and GHG emission for the hybrid poplar baseline feedstock conversion were 105,400 L dry metric ton−1 and 2.8 kg CO2 eq. per liter, respectively. Compared with the baseline, loblolly pine produced the highest alcohol yields, an 8.5% increase, and the lowest GHG emissions per liter of ethanol, a 9.1% decrease. Corn stover, due to its high ash content, had the lowest yields and the highest GHG emissions per liter of ethanol. The results were highly sensitive to the ash and water content of the biomass, indicating that biomass properties can significantly affect the environmental impact of the thermochemical ethanol conversion process.}, number={4}, journal={BioResources}, author={Daystar, J. S. and Venditti, Richard and Gonzalez, R. and Jameel, H. and Jett, M. and Reeb, C. W.}, year={2013}, pages={5261–5278} }
 @article{gonzales_lalush_2012, title={Eigenvector decomposition of full-spectrum x-ray computed tomography}, volume={57}, ISSN={["1361-6560"]}, DOI={10.1088/0031-9155/57/5/1309}, abstractNote={Energy-discriminated x-ray computed tomography (CT) data were projected onto a set of basis functions to suppress the noise in filtered back-projection (FBP) reconstructions. The x-ray CT data were acquired using a novel x-ray system which incorporated a single-pixel photon-counting x-ray detector to measure the x-ray spectrum for each projection ray. A matrix of the spectral response of different materials was decomposed using eigenvalue decomposition to form the basis functions. Projection of FBP onto basis functions created a de facto image segmentation of multiple contrast agents. Final reconstructions showed significant noise suppression while preserving important energy-axis data. The noise suppression was demonstrated by a marked improvement in the signal-to-noise ratio (SNR) along the energy axis for multiple regions of interest in the reconstructed images. Basis functions used on a more coarsely sampled energy axis still showed an improved SNR. We conclude that the noise–resolution trade off along the energy axis was significantly improved using the eigenvalue decomposition basis functions.}, number={5}, journal={PHYSICS IN MEDICINE AND BIOLOGY}, author={Gonzales, Brian J. and Lalush, David S.}, year={2012}, month={Mar}, pages={1309–1323} }
 @article{gonzales_lalush_2011, title={Full-Spectrum CT Reconstruction Using a Weighted Least Squares Algorithm With an Energy-Axis Penalty}, volume={30}, ISSN={["1558-254X"]}, DOI={10.1109/tmi.2010.2048120}, abstractNote={Recent developments in X-ray detectors have created the potential to perform energy-sensitive X-ray computed tomography (CT); that is, to reconstruct a series of CT images associated with different X-ray energies from a single scan. In this paper we propose a penalized weighted least squares (PWLS) algorithm for reconstruction of polychromatic energy-differentiated X-ray CT data and a unique experimental setup to take energy-differentiated X-ray CT data. The experimental setup is designed to acquire a complete X-ray spectrum for every projection ray. We use these data to estimate the linear attenuation coefficient as a function of energy for every pixel in the reconstructed attenuation map. We use prior knowledge of the properties of attenuation spectra to smooth the reconstructions, significantly reducing the noise and improving the contrast-to-noise ratio (CNR) in the reconstructed images without significantly biasing the data. We conclude that this algorithm is an effective method for reconstructing energy-sensitive CT data and provides justification for further research in energy sensitive CT systems.}, number={2}, journal={IEEE TRANSACTIONS ON MEDICAL IMAGING}, author={Gonzales, Brian and Lalush, David}, year={2011}, month={Feb}, pages={173–183} }