Northwestern University

About: Northwestern University is a education organization based out in Evanston, Illinois, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 75430 authors who have published 188857 publications receiving 9463252 citations. The organization is also known as: Northwestern & NU.

Functionalized graphene sheets for polymer nanocomposites

Abstract: Polymer-based composites were heralded in the 1960s as a new paradigm for materials. By dispersing strong, highly stiff fibres in a polymer matrix, high-performance lightweight composites could be developed and tailored to individual applications. Today we stand at a similar threshold in the realm of polymer nanocomposites with the promise of strong, durable, multifunctional materials with low nanofiller content. However, the cost of nanoparticles, their availability and the challenges that remain to achieve good dispersion pose significant obstacles to these goals. Here, we report the creation of polymer nanocomposites with functionalized graphene sheets, which overcome these obstacles and provide superb polymer-particle interactions. An unprecedented shift in glass transition temperature of over 40 degrees C is obtained for poly(acrylonitrile) at 1 wt% functionalized graphene sheet, and with only 0.05 wt% functionalized graphene sheet in poly(methyl methacrylate) there is an improvement of nearly 30 degrees C. Modulus, ultimate strength and thermal stability follow a similar trend, with values for functionalized graphene sheet- poly(methyl methacrylate) rivaling those for single-walled carbon nanotube-poly(methyl methacrylate) composites.

Using social and behavioural science to support COVID-19 pandemic response.

Abstract: The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.

Ultrahigh Porosity in Metal-Organic Frameworks

Abstract: Crystalline solids with extended non-interpenetrating three-dimensional crystal structures were synthesized that support well-defined pores with internal diameters of up to 48 angstroms The Zn4O(CO2)6 unit was joined with either one or two kinds of organic link, 4,4',4''-[benzene-1,3,5-triyl-tris(ethyne-2,1-diyl)]tribenzoate (BTE), 4,4',44''-[benzene-1,3,5-triyl-tris(benzene-4,1-diyl)]tribenzoate (BBC), 4,4',44''-benzene-1,3,5-triyl-tribenzoate (BTB)/2,6-naphthalenedicarboxylate (NDC), and BTE/biphenyl-4,4'-dicarboxylate (BPDC), to give four metal-organic frameworks (MOFs), MOF-180, -200, -205, and -210, respectively Members of this series of MOFs show exceptional porosities and gas (hydrogen, methane, and carbon dioxide) uptake capacities For example, MOF-210 has Brunauer-Emmett-Teller and Langmuir surface areas of 6240 and 10,400 square meters per gram, respectively, and a total carbon dioxide storage capacity of 2870 milligrams per gram The volume-specific internal surface area of MOF-210 (2060 square meters per cubic centimeter) is equivalent to the outer surface of nanoparticles (3-nanometer cubes) and near the ultimate adsorption limit for solid materials

Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review

Abstract: SUMMARY Review date: 1969 to 2003, 34 years. Background and context: Simulations are now in widespread use in medical education and medical personnel evaluation. Outcomes research on the use and effectiveness of simulation technology in medical education is scattered, inconsistent and varies widely in methodological rigor and substantive focus. Objectives: Review and synthesize existing evidence in educational science that addresses the question, ‘What are the features and uses of high-fidelity medical simulations that lead to most effective learning?’. Search strategy: The search covered five literature databases (ERIC, MEDLINE, PsycINFO, Web of Science and Timelit) and employed 91 single search terms and concepts and their Boolean combinations. Hand searching, Internet searches and attention to the ‘grey literature’ were also used. The aim was to perform the most thorough literature search possible of peer-reviewed publications and reports in the unpublished literature that have been judged for academic quality. Inclusion and exclusion criteria: Four screening criteria were used to reduce the initial pool of 670 journal articles to a focused set of 109 studies: (a) elimination of review articles in favor of empirical studies; (b) use of a simulator as an educational assessment or intervention with learner outcomes measured quantitatively; (c) comparative research, either experimental or quasi-experimental; and (d) research that involves simulation as an educational intervention. Data extraction: Data were extracted systematically from the 109 eligible journal articles by independent coders. Each coder used a standardized data extraction protocol. Data synthesis: Qualitative data synthesis and tabular presentation of research methods and outcomes were used. Heterogeneity of research designs, educational interventions, outcome measures and timeframe precluded data synthesis using meta-analysis. Headline results: Coding accuracy for features of the journal articles is high. The extant quality of the published research is generally weak. The weight of the best available evidence suggests that high-fidelity medical simulations facilitate learning under the right conditions. These include the following:

AMPL: A Modeling Language for Mathematical Programming

Abstract: Practical large-scale mathematical programming involves more than just the application of an algorithm to minimize or maximize an objective function. Before any optimizing routine can be invoked, considerable effort must be expended to formulate the underlying model and to generate the requisite computational data structures. AMPL is a new language designed to make these steps easier and less error-prone. AMPL closely resembles the symbolic algebraic notation that many modelers use to describe mathematical programs, yet it is regular and formal enough to be processed by a computer system; it is particularly notable for the generality of its syntax and for the variety of its indexing operations. We have implemented an efficient translator that takes as input a linear AMPL model and associated data, and produces output suitable for standard linear programming optimizers. Both the language and the translator admit straightforward extensions to more general mathematical programs that incorporate nonlinear expressions or discrete variables.