17 research outputs found
Leading Lives That Matter : A Classroom-Centered Approach to Mission Integration for Posttraditional Learners
Heterogeneity in susceptibility dictates the order of epidemiological models
The fundamental models of epidemiology describe the progression of an
infectious disease through a population using compartmentalized differential
equations, but do not incorporate population-level heterogeneity in infection
susceptibility. We show that variation strongly influences the rate of
infection, while the infection process simultaneously sculpts the
susceptibility distribution. These joint dynamics influence the force of
infection and are, in turn, influenced by the shape of the initial variability.
Intriguingly, we find that certain susceptibility distributions (the
exponential and the gamma) are unchanged through the course of the outbreak,
and lead naturally to power-law behavior in the force of infection; other
distributions often tend towards these "eigen-distributions" through the
process of contagion. The power-law behavior fundamentally alters predictions
of the long-term infection rate, and suggests that first-order epidemic models
that are parameterized in the exponential-like phase may systematically and
significantly over-estimate the final severity of the outbreak
Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties
The study presents an ab-initio based framework for the automated construction of microkinetic mechanisms considering correlated uncertainties in all energetic parameters and estimation routines. 2000 unique microkinetic models were generated within the uncertainty space of the BEEF-vdW functional for the oxidation reactions of representative exhaust gas emissions from stoichiometric combustion engines over Pt(111) and compared to experiments through multiscale modeling. The ensemble of simulations stresses the importance of considering uncertainties. Within this set of first-principles-based models, it is possible to identify a microkinetic mechanism that agrees with experimental data. This mechanism can be traced back to a single exchange-correlation functional, and it suggests that Pt(111) could be the active site for the oxidation of light hydrocarbons. The study provides a universal framework for the automated construction of reaction mechanisms with correlated uncertainty quantification, enabling a DFT-constrained microkinetic model optimization for other heterogeneously catalyzed systems
Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties
The study presents an ab-initio based framework for the automated construction of microkinetic mechanisms considering correlated uncertainties in all energetic parameters and estimation routines. Two thousand unique microkinetic models were generated within the uncertainty space of the BEEF-vdW functional for the conversion of exhaust gas emissions from stoichiometric gasoline combustion engines over Pt(111) and compared to experiments through multiscale modeling. The ensemble of simulations stresses the importance of considering uncertainties. Within this set of first-principles-based models, it is possible to identify a microkinetic mechanism that agrees with experimental data. This mechanism can be traced back to a single exchange-correlation functional, and it suggests that Pt(111) could be the active site for the oxidation of light hydrocarbons. The study provides a universal framework for the automated construction of reaction mechanisms with correlated uncertainty quantification, enabling a DFT-constrained microkinetic model optimization for other heterogeneously catalyzed systems