Paolo Elvati

TL;DR: In this article, the authors conducted a thermodynamic analysis of the physical growth of poly-aromatic hydrocarbons using atomistic models and found that the formation of soot nuclei can be related to two classes of mechanisms (physical and chemical growth), their relative importance is still under debate.

TL;DR: This paper applies a hybrid machine learning approach consisting of graph convolutional networks used to extract molecular shape features as input to a random forest classifier and shows that simple linear/logistic regression models can predict the values of these global features from the shape features extracted using the framework.

TL;DR: Experimental and computational results show that graphene quantum dots efficiently dock near the N-terminus of the peptide and change the secondary structure of phenol-soluble modulins, which disrupts their fibrillation and represents a strategy for mitigation of bacterial communities.

TL;DR: In this paper, a very fast process provided dispersed gold nanoparticles with average diameters ranging from 3 to 20 nm depending on the host polymer matrix and the irradiation time, and the anisotropic distribution of packed assemblies of gold particles, providing oriented films with polarizationdependent tunable optical properties.