Novel Design of Co-Poly(Hydrazide Imide) and Its Complex with Cu(I) for Membrane Separation of Methanol/Dimethyl Carbonate Mixture.

Poly(2,2'-biquinoline-6,6'-dicarbohydrazide)-co-(bistrimelliteimide)methylene-bisanthranylide (PHI) and its metal-polymer complex PHI-Cu(I) containing several types of functional groups (hydrazide, carboxyl, amide, and imide fragments) were synthesized to prepare two types of dense nonporous membranes. The study on morphology using scanning electron microscopy (SEM), measurements of mechanical, thermal, and transport properties of the membrane samples was carried out. The main mechanical properties of both membranes do not differ significantly, but the values of ultimate deformation differ palpably as a result of a non-uniform character of the deformation process for the PHI membrane. The thermal analysis based on the curves of thermogravimetric (TGA) and differential thermal (DTA) analyses of the PHI and PHI-Cu(I) membranes revealed peculiarities of the membrane structure. Transport properties were studied in pervaporation (PV) of methanol (MeOH) and dimethyl carbonate (DMC) mixtures including an azeotropic point. Intrinsic properties of the penetrant-membrane system were also determined. It was found that the total flux is higher through the PHI membrane, but the PHI-Cu(I) membrane exhibits a higher separation factor. Calculation of the pervaporation separation index (PSI) allowed to conclude that the PHI-Cu(I) membrane exhibits better transport properties as compared with the PHI membrane.

Machine Learning with Enormous "Synthetic" Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks.

In the present work, we address the problem of utilizing machine learning (ML) methods to predict the thermal properties of polymers by establishing "structure-property" relationships. Having focused on a particular class of heterocyclic polymers, namely polyimides (PIs), we developed a graph convolutional neural network (GCNN), being one of the most promising tools for working with big data, to predict the PI glass transition temperature T g as an example of the fundamental property of polymers. To train the GCNN, we propose an original methodology based on using a "transfer learning" approach with an enormous "synthetic" data set for pretraining and a small experimental data set for its fine-tuning. The "synthetic" data set contains more than 6 million combinatorically generated repeating units of PIs and theoretical values of their T g values calculated using the well-established Askadskii's quantitative structure-property relationship (QSPR) computational scheme. Additionally, an experimental data set for 214 PIs was also collected from the literature for training, fine-tuning, and validation of the GCNN. Both "synthetic" and experimental data sets are included into a PolyAskInG database (Polymer Askadskii's Intelligent Gateway). By using the PolyAskInG database, we developed GCNN which allows estimation of T g of PI with a mean absolute error (MAE) of about 20 K, which is 1.5 times lower than in the case of Askadskii QSPR analysis (33 K). To prove the efficiency and usability of the proposed GCNN architecture and training methodology for predicting polymer properties, we also employed "transfer learning" to develop alternative GCNN pretrained on proxy-characteristics taken from the popular quantum-chemical QM9 database for small compounds and fine-tuned on an experimental T g values data set from PolyAskInG database. The obtained results indicate that pretraining of GCNN on the "synthetic" polymer data set provides MAE which is almost twice as low as that in the case of using the QM9 data set in the pretraining stage (∼41 K). Furthermore, we address the questions associated with the influence of the differences in the size of the experimental and "synthetic" data sets (so-called "reality gap" problem), as well as their chemical composition on the training quality. Our results state the overall priority of using polymer data sets for developing deep neural networks, and GCNN in particular, for efficient prediction of polymer properties. Moreover, our work opens up a challenge for the theoretically supported generation of large "synthetic" data sets of polymer properties for the training of the complex ML models. The proposed methodology is rather versatile and may be generalized for predicting other properties of different polymers and copolymers synthesized through the polycondensation reaction.

New copolyhydrazides with anthrazoline fragments in the main chain: synthesis and optical properties.

Several new 2,8-diphenylpyrido[3,2-g]quinoline-4,6-dicarbohydrazides were synthesized and copolyhydrazides based on them were obtained. Molecular weight, thermal, stress-strain and optical properties were investigated. It was shown that all polymer films exhibit significant luminescence in the 450-650 nm region, the profile and intensity of which depended on the nature and position of substituents in the phenylene fragment.

Novel Polyester Amide Membranes Containing Biquinoline Units and Complex with Cu(I): Synthesis, Characterization, and Approbation for n-Heptane Isolation from Organic Mixtures.

The wide possibilities of designing a chemical structure and creating complexes with transition metals make polymers of heteroaromatic structure interesting objects, from both scientific and practical aspects. In this work, modern biquinoline-containing polymers, namely polyester amide (PEA) and its metal-polymer complex (PEA-Cu(I)), were synthesized and used to form dense flat membranes. A comparative study of their morphology, same physical properties (density, free volume, and contact angles), and thermomechanical characteristics was carried out. The transport properties of the modern membranes were studied during pervaporation, to solve a problem of n-heptane isolation from its binary mixtures with thiophene and methanol. It was shown that only the PEA membrane is selective for the separation of thiophene impurities from the mixture with n-heptane. In pervaporation of methanol/n-heptane mixture, the РЕА-Cu(I) membrane exhibits significantly higher pervaporation separation index, as compared with that of the РЕА membrane.

Asymmetric Membranes Based on Copolyheteroarylenes with Imide, Biquinoline, and Oxazinone Units: Formation and Characterization.

Modern ultrafiltration requires novel perfect membranes with narrow pore size, high porosity, and minimal pore tortuosity to achieve high separation performance. In this work, copolyamic acid (co-PAA) was synthesized and used for the preparation of asymmetric porous membranes by phase inversion technique. Several co-PAA membranes were heated up to 250 °C; during heating, they undergo solid-phase transformation into co-polybenzoxazinoneimide (co-PBOI) via dehydration and cyclization. Comparative characterization of both co-PAA and co-PBOI membranes was realized by scanning electron microscopy, mechanical testing, thermogravimetric analysis, and ultrafiltration experiments. Membrane calibration was carried out using a mixture of seven proteins with different molecular weights. During heat treatment, the molecular weight cut-off of the membranes decreased from 20 × 103 g/mol (co-PAA) to 3 × 103 g/mol (co-PBOI). Abnormally low dispersions of rejection (0.3 for co-PAA and 0.45 for co-PBOI) were observed for the studied membranes; this fact indicates that the membranes possess enhanced resolving power.

Synthesis and photoluminescence properties of co-polyamides with anthrazoline-containing units in the main chain.

A series of anthrazoline-containing monomers are synthesized, and eight co-polyamides of different chemical structures, containing 1,9-anthrazoline fragments in the main chain, are obtained and investigated. Photoluminescent, stress-strain, and thermal properties of these polymers are studied. It is shown that polymers with fragments of 4,4'-(pyrido[3,2-g]-quinoline-2,8-diyl)dianiline and 4,4'-(10-methylpyrido[3,2-g]quinoline-2,8-diyl)dianiline possess an intense luminescence in the range 550-650 nm. The performed investigations made it possible to determine the effect of substituents of various natures in the anthrazoline cycle and the position of amide group (meta- and para-configurations) on optical, stress-strain, and thermal properties of copolymers, opening up a prospect for further developments of principles of design of polymers with optimal characteristics.

Aromatic Copolyamides with Anthrazoline Units in the Backbone: Synthesis, Characterization, Pervaporation Application.

Copolyamides with anthrazoline units in the backbone (coPA) were synthesized and dense nonporous films were prepared by solvent evaporation. Glass transition temperature, density, and fractional free volume were determined for the dense nonporous films composed of polyamide and two of its copolymers containing 20 and 30 mol % anthrazoline units in the backbone. Transport properties of the polymer films were estimated by sorption and pervaporation tests toward methanol, toluene, and their mixtures. An increase in anthrazoline fragments content leads to an increasing degree of methanol sorption but to a decreasing degree of toluene sorption. Pervaporation of a methanol⁻toluene mixture was studied over a wide range of feed concentration (10⁻90 wt % methanol). Maximal separation factor was observed for coPA-20 containing 20 mol % fragments with anthrazoline units; maximal total flux was observed for coPA-30 with the highest fractional free volume.