Quantitative Structure-Permittivity Relationship Study of a Series of Polymers

Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure-property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. The best model showed a high predictive performance for training and test sets, with R2 values of 0.905 and 0.812, respectively. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied, the models were proven to have a good predictive ability and robustness for further applications in polymer permittivity prediction.

Combined Machine Learning, Computational, and Experimental Analysis of the Iridium(III) Complexes with Red to Near-Infrared Emission

Various coordination complexes have been the subject of experimental and theoretical studies in recent decades because of their fascinating photophysical properties. In this work, a combined experimental and computational approach was applied to investigate the optical properties of monocationic Ir(III) complexes. An interpretative machine learning-based quantitative structure-property relationship (ML/QSPR) model was successfully developed that could reliably predict the emission wavelength of the Ir(III) complexes and provide a foundation for the theoretical evaluation of the optical properties of Ir(III) complexes. A hypothesis was proposed to explain the differences in the emission wavelengths between structurally different individual Ir(III) complexes. The efficacy of the developed model was demonstrated by high R2 values of 0.84 and 0.87 for the training and test sets, respectively. It is worth noting that a relationship between the N-N distance in the diimine ligands of the Ir(III) complexes and emission wavelengths is detected. This effect is most probably associated with a degree of distortion in the octahedral geometry of the complexes, resulting in a perturbed ligand field. This combined experimental and computational approach shows great potential for the rational design of new Ir(III) complexes with the desired optical properties. Moreover, the developed methodology could be extended to other transition-metal complexes.

Experimental and Simulation Studies on Nonwoven Polypropylene-Nitrile Rubber Blend: Recycling of Medical Face Masks to an Engineering Product

The battle against the COVID-19 pandemic counters the waste management system, as billions of single-use face masks are used per day all over the world. Proper disposal of used face masks without jeopardizing the health and the environment is a challenge. Herein, a novel method for recycling of medical face masks has been studied. This method incorporates the nonwoven polypropylene (PP) fiber, which is taken off from the mask after disinfecting it, with acrylonitrile butadiene rubber (NBR) using maleic anhydride as the compatibilizer, which results in a PP-NBR blend with a high percentage economy. The PP-NBR blends show enhanced thermomechanical properties among which, 70 wt % PP content shows superior properties compared to other composites with 40, 50, and 60 wt % of PP. The fully Atomistic simulation of PP-NBR blend with compatibilizer shows an improved tensile and barrier properties, which is in good agreement with the experimental studies. The molecular dynamics simulation confirms that the compatibility between non-polar PP and polar NBR phases are vitally important for increasing the interfacial adhesion and impeding the phase separation.