Van der Waals forces can easily link PAHs of such size and shape to form PAH dimers and larger clusters under the specified flame conditions. Our results provide direct experimental evidence that soot inception is initiated by a physical process under typical flame conditions. This work improves our understanding of aerosol particulates, which has implications for their environmental and climate change impacts.

REF Can Shao, Qi Wang, Wen Zhang, Anthony Bennett, Yang Li, Junjun Guo, Hong G. Im, William L. Roberts, Angela Violi, S. Mani Sarathy 

Polycyclic aromatic hydrocarbons are the main precursors to soot particles in combustion systems. A lack of direct experimental evidence has led to controversial theoretical explanations for the transition from gas-phase species to organic soot clusters. This work focuses on sampling infant soot particles from well-defined flames followed by analysis using state-of-the-art mass spectrometry. We found that PAH molecules present in soot particles are all stabilomers. Kinetic Monte Carlo simulations and thermodynamic stability calculations further identify the detected PAHs as peri-condensed and without aliphatic chains. 

These principles generally apply to S(k) obtained from particle positions in noncrystalline substances. The amorphous substance we simulate is a two-dimensional liquid, with a soft Yukawa interaction modeling a dusty plasma experiment.

REF Vitaliy Zhuravlyov, J. Goree, Paolo Elvati, and Angela Violi

Finite-size effects in the static structure factor S(k) are analyzed for an amorphous substance. As the number of particles is reduced, S(0) increases greatly, up to an order of magnitude. Meanwhile, there is a decrease in the height of the first peak S_peak. These finite-size effects are modeled accurately by the Binder formula for S(0) and our empirical formula for S_peak. Procedures are suggested to correct for finite-size effects in S(k) data and in the hyperuniformity index H≡S(0)/S_peak.

Although challenging, the accurate and rapid prediction of nanoscale interactions has broad applications for numerous biological processes and material properties. While several models have been developed to predict the interaction of specific biological components, they use system-specific information that hinders their application to more general materials.

REF Jacob Saldinger, Matt Raymond, Paolo Elvati, and Angela Violi

Here we present NeCLAS, a general and efficient machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions.NeCLAS outperforms current nanoscale prediction models for generic nanoparticles up to 10–20 nm, reproducing interactions for biological and non-biological systems. Two aspects contribute to these results: a low-dimensional representation of nanoparticles and molecules (to reduce the effect of data uncertainty), and environmental features (to encode the physicochemical neighborhood at multiple scales). This framework has several applications, from basic research to rapid prototyping and design in nanobiotechnology.

The present work focuses on the molecular details and characteristics of PSMα1-derived functional amyloids present in Staphylococcus aureus biofilms, using a combination of computational and experimental techniques, to develop a model that can aid the design of compounds to control amyloid formation. Results from molecular dynamics simulations, guided and supported by spectroscopy and microscopy, show that PSMα1 amyloid nanofibers present a helical structure formed by two protofilaments, have an average diameter of about 12 nm, and adopt a left-handed helicity with a periodicity of approximately 72 nm. The chirality of the self-assembled nanofibers, an intrinsic geometric property of its constituent peptides, is central to determining the fibers’ lateral growth.

REF Paolo Elvati, Chloe Luyet, Yichun Wang, Changjiang Liu, J. Scott VanEpps, Nicholas Kotov, and Angela Violi

Amyloid nanofibers are abundant in microorganisms and are integral components of many biofilms, serving various purposes, from virulent to structural. Nonetheless, the precise characterization of bacterial amyloid nanofibers has been elusive, with incomplete and contradicting results. 

Our approach provides also a data driven method to determine the molecular features most important to predicting the dimer stability. This work highlights the molecular complexity of the PAC monomers that must be accounted for in order to accurately represent physical aggregation. We anticipate that our approach is key to modeling soot inception as it allows for the efficient prediction of dimerization propensity from easily calculable molecular features.Indeed, we identified size, shape, oxygenation, and presence of rotatable bonds as the most influential characteristics of PACs that contribute to physical dimerization. 

REF Jacob Saldinger, Paolo Elvati, and Angela Violi

The physical aggregation of polycyclic aromatic compounds (PACs) is a key step in soot inception. In this work, we set out to elucidate which molecular properties of PACs influence the physical growth process and develop a machine learning framework to quantitatively relate these features to the propensity of PACs to physically dimerize. To this end, we identify a pool of compounds with a diverse range of properties and create a dataset of PAC monomers along with their calculated free energies of dimerization, obtained via molecular dynamics simulations enhanced by well-tempered Metadynamics. We then demonstrate that a machine learning model based on the least absolute shrinkage and selection operator (Lasso) is able to quantitatively learn how molecular features contribute to physical aggregation and predict the free energy of dimerization for new pairs of molecules. Results show that our model is able to accurately determine the stability of dimers obtained from both homo- and hetero-molecular dimerization cases. 

We find that asymmetry does not impact the vibrational spectra, and the impact of sterols depends on the mobility of the components of the membrane. We demonstrate that vibrational spectra can be used to distinguish between membranes and, therefore, could be used in identification of different organisms. The method presented, here, can be immediately extended to other biological structures (e.g., amyloid fibers, polysaccharides, and protein-ligand structures) in order to fingerprint and understand vibrations of numerous biologically-relevant nanoscale structures.

REF Chloe Luyet, Paolo Elvati, Jordan Vinh, and Angela Violi

A growing body of work has linked key biological activities to the mechanical properties of cellular membranes, and as a means of identification. Here, we present a computational approach to simulate and compare the vibrational spectra in the low- THz region for mammalian and bacterial membranes, investigating the effect of membrane asymmetry and composition, as well as the conserved frequencies of a specific cell.