Novel modified zeolites for energy-efficient hydrocarbon separations.

We present synthesis, characterization and testing results of our applied research project, which focuses on the effects of surface and skeletal modification of zeolites for significant enhancements in current hydrocarbon (HC) separations. Zeolites are commonly used by the chemical and petroleum industries as catalysts and ion-exchangers. They have high potential for separations owing to their unique pore structures and adsorption properties and their thermal, mechanical and chemical properties. Because of zeolites separation properties, low cost, and robustness in industrial process, they are natural choice for use as industrial adsorbents. This is a multidisciplinary effort to research, design, develop, engineer, and test new and improved materials for the separation of branched vs. linear organic molecules found in commercially important HC streams via adsorption based separations. The focus of this project was the surface and framework modification of the commercially available zeolites, while tuning the adsorption properties and the selectivities of the bulk and membrane separations. In particular, we are interested with our partners at Goodyear Chemical, on how to apply the modified zeolites to feedstock isoprene purification. For the characterization and the property measurements of the new and improved materials powder X-ray diffraction (PXRD), Residual Gas Analyzer-Mass Spectroscopy (RGA-MS), Electron Microscopy (SEM/EDAX), temperature programmed desorption (TPD) and surface area techniques were utilized. In-situ carbonization of MFI zeolite membranes allowed for the maximum separation of isoprene from n-pentane, with a 4.1% enrichment of the binary stream with n-pentane. In four component streams, a modified MFI membrane had high selectivities for n-pentane and 1-3-pentadiene over isoprene but virtually no separation for the 2-methyl-2-butene/isoprene pair

A case study integrating numerical simulation and GB-InSAR monitoring to analyze flexural toppling of an anti-dip slope in Fushun open pit

Toppling failure of rock slopes is a complicated mode due to a combination of both continuous and discontinuous deformation, especially in dealing with anti-dip rock slopes. In this paper, a novel continuum-based discrete element method (CDEM), which is useful in modeling the entire progressive process from continuous to discontinuous deformation, is proposed to analyze the deformation characteristics, the failure mechanism and the evolution process of a large-scale open pit slope with a typical anti-dip structure. To simulate the slope deformation, the shear strength reduction method (SSR) is adopted to represent the strength degradation of rock mass in the deterioration process. The simulated results are validated using data obtained from a field investigation and continuous monitoring by employing an advanced remote sensing technique called ground-based interferometric synthetic aperture radar (GB-InSAR). To analyze the evolution trend of the anti-dip slope, the subsequent toppling failure mode is predicted using the validated CDEM models. Based on a case study of a slope at the Fushun open pit mine (in Fushun, China), the unique geological structure with various joints and discontinuities, ground-water, intense rainfall, and mining activities are identified as the main triggers for different failure stages. The comparison between the field data and the simulation shows that CDEM accurately depicts the rock deformation and the failure pattern of the studied slope. The proposed numerical modeling techniques can be used for predicting failures of other similar excavated rock slopes. The simulated evolution process and the recorded deformation patterns help engineers to gain a better understanding of rock mass movement of anti-dip slopes, and the presented methodology could be utilized for similar studies and engineering designs. (C) 2015 Elsevier B.V. All rights reserved

Teaching Innovations Using Systems Thinking to Guide Fieldwork Projects in RN-to-BSN Education

Background: A critical need exists to improve quality and safety within RN-to-BSN education through innovative teaching strategies. RN-to-BSN students are poised to improve patient outcomes through system-level awareness by use of scholarly fieldwork projects within practice settings. The purpose of this scholarship of teaching project was to use an adapted version of the Systems Awareness Model to develop and categorize RN-to-BSN students’ learning experiences and capstone-type fieldwork projects guided by systems thinking. Faculty members of the Catalysts for Change Community led this project. Methods: A modified Delphi technique using multiple iterations to reach consensus by faculty experts was used in the design of this scholarship of teaching project. The philosophical underpinning guiding this project was collaborative scholarship. The seven steps of the System Awareness Model adapted for leadership and management were used to guide faculty championing quality and safety of innovative teaching strategies in face-to-face, hybrid, or online teaching-learning environments. Results: Faculty described examples of evidence-based practice (EBP), change, and practice projects including ideas, titles, and descriptions in alignment with Quality and Safety Education for Nurses (QSEN) competencies and with newly adopted American Association of Colleges of Nursing Education Essentials. A grading rubric is provided for evaluating fieldwork student project outcomes. Conclusions: The teaching strategies and fieldwork projects described in this paper reinforce the American Association of Colleges of Nursing (AACN) RN-to-BSN White Paper and the Commission on Collegiate Nursing Education (CCNE) Teaching Standards. Suggestions for future research are offered

Generation of “Virtual” Control Groups for Single Arm Prostate Cancer Adjuvant Trials

<div><p>It is difficult to construct a control group for trials of adjuvant therapy (Rx) of prostate cancer after radical prostatectomy (RP) due to ethical issues and patient acceptance. We utilized 8 curve-fitting models to estimate the time to 60%, 65%, … 95% chance of progression free survival (PFS) based on the data derived from Kattan post-RP nomogram. The 8 models were systematically applied to a training set of 153 post-RP cases without adjuvant Rx to develop 8 subsets of cases (reference case sets) whose observed PFS times were most accurately predicted by each model. To prepare a virtual control group for a single-arm adjuvant Rx trial, we first select the optimal model for the trial cases based on the minimum weighted Euclidean distance between the trial case set and the reference case set in terms of clinical features, and then compare the virtual PFS times calculated by the optimum model with the observed PFSs of the trial cases by the logrank test. The method was validated using an independent dataset of 155 post-RP patients without adjuvant Rx. We then applied the method to patients on a Phase II trial of adjuvant chemo-hormonal Rx post RP, which indicated that the adjuvant Rx is highly effective in prolonging PFS after RP in patients at high risk for prostate cancer recurrence. The method can accurately generate control groups for single-arm, post-RP adjuvant Rx trials for prostate cancer, facilitating development of new therapeutic strategies.</p></div