F. De Proft

We report the use of novel biobased plasticizers prepared starting from citric acid (CA) in polylactic acid (PLA). Citric acid based plasticizers are well-known softeners for PLA, with citrate esters as the most commonly used class. However, citrate esters are known to leach out of the plastic material over time. This problem is currently addressed by acetylating the tertiary hydroxyl group of citric acid via complex and environmentally polluting processes. An alternative strategy consists in the reductive removal of the tertiary hydroxyl group, resulting in propane-1,2,3-tricarboxylic acid. Derivatizing this compound leads to novel plasticizers which have not been tested in PLA yet. Here, different esters of propane-1,2,3-tricarboxylic acid were synthesized and blended in PLA. Their influence on the thermal (T_g and T_m) and the mechanical properties (Young’s modulus, stress and strain) of PLA along with their migration out of the material were compared to those of commercially available citric acid based plasticizers. Our results show that similar or better results were obtained with these new PLA plasticizers.

The dissociation of environmentally hazardous NO through dissociative adsorption on metallic clusters supported by oxides, is receiving growing attention. Building on previous research on monometallic M 13  clusters [J. Phys. Chem. C, 2019, 123(33), 20314-20318], this work considers bimetallic Pt 12 M (M = Rh, Ru or Ir) clusters. The adsorption energy and activation energy of NO dissociation on the clusters have been calculated in vacuum using Koh,-Sham DFT, while their trends were rationalized using reactivity indices such as molecular electrostatic potential and global Fermi softness. The results shown that doping of the Pt clusters lowered the adsorption energy as well as the activation energy for NO dissociation. Furthermore, reactivity indices were calculated as a first estimate of the performance of the clusters in realistic amorphous silica pores (MCM-41) through  ab initio  molecular dynamics simulations.

In this work we present the development of a novel, quantitative solubility descriptor based on the non-covalent interaction index. It is presented as a more insightful alternative to Hansen's solubility parameters and the COSMO model to assess and predict polymer solubility in different solvents. To this end, we studied the solvation behaviour as a function of the chain length of a single chain of arguably the most simple polymer, polyethylene, in anisole (solvent) and methanol (poor solvent) via molecular dynamics simulations. It was found that in anisole the solute maximized its interface with the solvent, whereas in methanol the macromolecule formed rod-like structures by folding on itself once the chain length surpassed a certain barrier. We assessed this behaviour – which can be related to solubility – quantitatively and qualitatively via well-known descriptors, namely the solvation free energy, and the solvent accessible surface area. In addition, we propose the non-covalent interaction (NCI) index as a versatile descriptor, providing information on the strength, as well as the nature, of the solute–solvent interactions, the solute's intramolecular interactions and on the solute's conformation, both qualitatively and quantitatively. Finally, as a quantitative measure for solubility, defined in this context as the solute's tendency to maximize its interactions with the solvent, we propose two new NCI-based descriptors: the relative integrated NCI density and the integrated NCI difference. The former represents the quantitative difference in solute–solvent interactions between a fully extended coil and the actual conformation during simulation and the latter the quantitative difference between the intermolecular (solute–solvent) and the intramolecular (in the solute) non-covalent interactions. The easy interpretation and calculation of these novel quantities open up the possibility of fast, reliable and insightful high-throughput screening of different (anti)solvent and solute combinations.