Publications

2019

Understanding the mechanisms and structural mappings between molecules and pathway classes is critical for design of reaction predictors for synthesizing new molecules. This paper studies the problem of prediction of classes of metabolic pathways (series of chemical reactions occurring within a cell) in which a given biochemical compound participates. We apply a hybrid machine learning approach consisting of graph convolutional networks used to extract molecular shape features as input to a random forest classifier.

A deep learning architecture for metabolic pathway prediction

REF Mayank Baranwal, Abram Magner, Paolo Elvati, Jacob Saldinger, Angela Violi, Alfred O Hero


Current approaches to improve the efficiency of Spark-Ignition (SI) gasoline engines have been focusing on turbocharging, increasing the compression ratio, and pursuing advanced low-temperature combustion concepts. In order to maximize these strategies, it is important to optimize the knock resistance of the fuel, and therefore knowledge of the sensitivity of the ignition process under a wide range of engine operating conditions is required. Octane sensitivity (OS), which is defined as the difference between Research Octane Number (RON) and Motored Octane Number (MON), has been introduced to represent how fuel’s ignition reactivity changes relative to the primary reference fuels (n-heptane/ iso-octane) within RON/MON conditions. Previous works have indicated that OS is intimately related to low temperature re- activity of the fuel, which can be revealed as two-stage heat release characteristics during an ignition event.

Two-stage ignition behavior and octane sensitivity of toluene reference fuels as gasoline surrogate

REF Doohyun Kim, Charles K. Westbrook, Angela Violi

Prompted by these findings, in this paper, we investigate the relationship between two-stage ignition behavior and OS, using chemical kinetic simulations of 24 Toluene Reference Fuels (TRFs)/ethanol blends. Simulation results show that fuels with weak or no two-stage ignition behavior tend to have high OS, due to their lack of Negative Temperature Coefficient (NTC) effect and high sensitivity in ignition delay time. The results demonstrate the effectiveness of the metric as a representation of the two-stage ignition behavior in practical combustion systems, highlighting the importance of the proposed relationship, and its potential as a simple and effective OS predictor.

PREDICTING THE TIME OF ENTRY OF NANOPARTICLES IN LIPID MEMBRANES

REF Changjiang Liu, Paolo Elvati, Sagardip Majumder, Yichun Wang, Allen P. Liu, Angela Violi

The behavior of nanoparticles in a biological matrix is a very complex problem that depends not only on the type of nanoparticle, but also on its size, shape, phase, surface charge, chemical composition and agglomeration state. In this paper, we introduce a streamlined theoretical model that predicts the average time of entry of nanoparticles in lipid membranes, using a combination of molecular dynamics simulations and statistical approaches.

Spatial Dependence of the Growth of Polycyclic Aromatic Compounds in an Ethylene Counterflow Flame

REF Qi Wang, Paolo Elvati, Doohyun Kim, K. Olof Johansson, Paul E. Schrader, Hope A.Michelsen, Angela Violi

The complex environments that characterize combustion systems can influence the distribution of gas-phase species, the relative importance of various growth mechanisms and the chemical and physical characteristics of the soot precursors generated. In order to provide molecular insights on the effect of combustion environments on the formation of gas-phase species, in this paper...



Anti-Activity of Graphene Quantum Dots via Self-Assembly with Bacterial Amyloid Proteins

REF Yichun Wang, Usha Kadiyala, Zhibei Qu, Paolo Elvati, Christopher Altheim, Nicholas A. Kotov, Angela Violi, and J. Scott VanEpps

Bacterial biofilms represent an essential part of Earth’s ecosystem that can cause multiple ecological, technological and health problems. The environmental resilience and sophisticated organization of biofilms are enabled by the extracellular matrix that creates a protective network of biomolecules around the bacterial community....