Deciphering the Self-Catalytic Mechanisms of Polymerization and Transesterification in Polybenzoxazine Vitrimers.

The use of internal catalysts has emerged as a pivotal design principle to facilitate dynamic exchanges within covalent adaptable networks (CANs). Polybenzoxazines, specifically, have shown considerable potential in generating vitrimers through thermally induced transesterification reactions catalyzed internally by tertiary amines. This study aims to investigate the chemical complexities of transesterification reactions within benzoxazine vitrimers. To achieve this, model molecules using various phenolic acids and amino-alcohol derivatives were synthesized as precursors. The structure of these model molecules was fully elucidated by using nuclear magnetic resonance (NMR). Differential scanning calorimetry (DSC) and rheology experiments evidenced the accelerated network formation of the precursors due to the presence of aliphatic -OH groups. Thermogravimetric analysis coupled with microcomputed gas chromatography (TGA-µGC) was used to provide evidence of transesterification reactions. The results showed that the spatial proximity between tertiary amine and hydroxyl groups significantly enhances the rate exchange, attributed to a neighboring group participation (NGP) effect. Interestingly, kinetic experiments using complementary NMR techniques revealed the thermal latency of the tertiary amine of benzoxazine toward transesterification reactions as its opening is needed to trigger the dynamic exchange. The study highlights the crucial role of steric hindrance and tertiary amine basicity in promoting the dynamic exchange in an internally catalyzed system.

Unique Method for Facile Postsynthetic Modification of Nonisocyanate Polyurethanes.

Nonisocyanate polyurethanes (NIPUs) are broadly investigated as a potential replacement for conventional polyurethanes (PUs) to eliminate the use of toxic isocyanates and reduce occupational hazards. One of the most popular approaches to NIPU synthesis is the polyaddition of cyclic bis(carbonate)s and diamines to form poly(hydroxyurethane)s (PHUs). However, such PHUs are highly hydrophilic due to the presence of two hydroxyl groups per repeat unit, and the resulting moisture absorption significantly degrades their thermomechanical performance and physical stability upon exposure to humidity, thus limiting their utility. Here, we introduce a simple and scalable approach for the modification of PHUs to increase hydrophobicity and adjust their properties. The proposed reaction between aldehydes and appropriately spaced hydroxyl groups in the polymer backbone resulted in high degrees of modification (up to 84%) and up to 3-fold reductions in water uptake at 85% RH. Furthermore, the use of aromatic aldehydes in particular enabled the retention of mechanical properties over a wide range of humidity levels, resulting in performance comparable to conventional PUs. Finally, we note that this approach is not limited to reducing moisture sensitivity alone and provides ample opportunities for imparting a broad range of novel properties to PHUs through an appropriate selection of functional aldehydes.

Original Basic Activation for Enhancing Silica Particle Reactivity: Characterization by Liquid Phase Silanization and Silica-Rubber Nanocomposite Properties.

Silica fillers are used in various nanocomposites in combination with silanes as a reinforcing filler. In tire technology, silica is generally functionalized before (pre-treated) or during mixing (in-situ silanization or post-treated). In both cases, a soft base catalyst (e.g., triethylamine or diphenyl guanidine, DPG) is typically used to accelerate and increase the yield of the silane/silica coupling reaction. In this study, we investigated how pre-treatments of silica particles with either strong amine or hydride bases impact the silanization of silica prior to or during SBR mixing for silica-rubber nanocomposite fabrication. Our findings are supported by molecular characterization (solid state 29Si NMR, 1H NMR and TGA), and scanning electron microscopy. In addition, the impact of these silica pre-treatments on a nanocomposite's mechanical properties was evaluated using dynamic mechanical analysis (DMA).

Semiquantitative Solid-State NMR Study of the Adsorption of Soybean Oils on Silica and Its Significance for Rubber Processing.

Soybean oil (SBO) is a renewable material used as an alternative to conventional petroleum-derived oils in the processing of rubber composites. Upon chemical modifications, such as epoxidation, its performance in the processing of rubber can be significantly improved, as indicated by a considerable reduction of the mixing energy. Although it has been hypothesized that hydrogen bonding between functional groups (e.g., epoxy) of SBOs and silanols present on the silica surface plays a key role, there is still a lack of direct evidence supporting this hypothesis. In this work, it is demonstrated that there is an overall correlation between the epoxy concentration of SBOs and the mixing energy, consistent with the long-held hypothesis. In particular, a correlation between the SBO-silica adsorption affinity and the degree of epoxidation is revealed by a set of surface-selective solid-state nuclear magnetic resonance (ssNMR) experiments. In addition, the surface-selective ssNMR technique demonstrated in this work could also be used to evaluate the adsorption affinity of other oils and/or additives more broadly.

Reinforcement of Styrene Butadiene Rubber Employing Poly(isobornyl methacrylate) (PIBOMA) as High Tg Thermoplastic Polymer.

A set of poly(isobornyl methacrylate)s (PIBOMA) having molar mass in the range of 26,000-283,000 g mol-1 was prepared either via RAFT process or using free radical polymerization. These linear polymers demonstrated high glass transition temperatures (Tg up to 201 °C) and thermal stability (Tonset up to 230 °C). They were further applied as reinforcing agents in the preparation of the vulcanized rubber compositions based on poly(styrene butadiene rubber) (SBR). The influence of the PIBOMA content and molar mass on the cure characteristics, rheological and mechanical properties of rubber compounds were studied in detail. Moving die rheometry revealed that all rubber compounds filled with PIBOMA demonstrated higher torque increase values ΔS in comparison with rubber compositions without filler, independent of PIBOMA content or molar mass, thus confirming its reinforcing effect. Reinforcement via PIBOMA addition was also observed for vulcanized rubbers in the viscoelastic region and the rubbery plateau, i.e. from -20 to 180 °C, by dynamic mechanical thermal analysis. Notably, while at temperatures above ~125 °C, ultra-high-molecular-weight polyethylene (UHMWPE) rapidly loses its ability to provide reinforcement due to softening/melting, all PIBOMA resins maintained their ability to reinforce rubber matrix up to 180 °C. For rubber compositions containing 20 phr of PIBOMA, both tensile strength and elongation at break decreased with increasing PIBOMA molecular weight. In summary, PIBOMA, with its outstanding high Tg among known poly(methacrylates), may be used in the preparation of advanced high-stiffness rubber compositions, where it provides reinforcement above 120 °C and gives properties appropriate for a range of applications.

An Investigation on the Thermally Induced Compatibilization of SBR and α-Methylstyrene/Styrene Resin.

The miscibility between two polymers such as rubbers and performance resins is crucial to achieve given targeted properties in terms of tire performances. To this aim, α-methylstyrene/styrene resin (poly(αMSt-co-St)) are used to modify the viscoelastic behavior of rubbers and to fulfill the requirements of the final applications. The initial aim of this work was to understand the influence of poly(αMSt-co-St) resins blended at different concentrations in a commercial styrene-butadiene rubber (SBR). Interestingly, while studying the viscoelastic properties of SBR blends with poly(αMSt-co-St), crosslinking of the rubber was observed under conditions where it was not expected to happen. Surprisingly, after the crosslinking reactions, the poly(αMSt-co-St) resin was irreversibly miscible with SBR at concentrations far above its immiscibility threshold. A detailed investigation involving characterization technics including solid state nuclear magnetic resonance led to the conclusion that poly(αMSt-co-St) is depolymerizing under heating and can graft onto the chains of SBR. It results in an irreversible compatibilization mechanism between the rubber and the resin.

Sustainable Esterification of a Soda Lignin with Phloretic Acid.

In this work, a sustainable chemical process was developed through the Fischer esterification of Protobind® lignin, a wheat straw soda lignin, and phloretic acid, a naturally occurring phenolic acid. It aimed at increasing the reactivity of lignin by enhancing the number of unsubstituted phenolic groups via a green and solvent-free chemical pathway. The structural features of the technical and esterified lignins were characterized by complementary spectroscopic techniques, including 1H, 13C, 31P, and two-dimensional analysis. A substantial increase in p-hydroxyphenyl units was measured (+64%, corresponding to an increase of +1.3 mmol g-1). A full factorial design of the experiment was employed to quantify the impact of critical variables on the conversion yield. The subsequent statistical analysis suggested that the initial molar ratio between the two precursors was the factor predominating the yield of the reaction. Hansen solubility parameters of both the technical and esterified lignins were determined by solubility assays in multiple solvents, evidencing their high solubility in common organic solvents. The esterified lignin demonstrated a better thermal stability as the onset of thermal degradation shifted from 157 to 220 °C, concomitantly to the shift of the glass transition from 92 to 112 °C. In conclusion, the esterified lignin showed potential for being used as sustainable building blocks for composite and thermoset applications.

Isocyanate-Free Fully Biobased Star Polyester-Urethanes: Synthesis and Thermal Properties.

A green strategy for the synthesis of nonisocyanate polyester-urethanes (NIPHEUs) was developed. These NIPHEUs were synthesized by step growth polymerization combining sugar-derived dimethyl-2,5-furan dicarboxylate (DMFD) with polyhydroxylurethanes (PHUs) adducts bearing four hydroxyl groups. The later hydroxyl urethane tetraols (HU-tetraols) building blocks were prepared by aminolysis of glycerol carbonate with two different aliphatic diamines having different chain lengths, 8 and 12 carbons. Qualitative and quantitative NMR analyses of the HU-tetraols showed the presence of primary and secondary hydroxyl moieties at different ratios. Hence, in the polycondensation stage, the stoichiometry of the diester was varied from 1 to 6 equiv in order to tailor the structural features of the prepared NIPHEUs. The success of the chain extension through polycondensation was confirmed by FTIR and NMR analyses. Thermal analyses of these new polymers demonstrated satisfactory thermal stability, with onset degradation temperatures ranging from 170 to 220 °C where the main first degradation stage occurs. Their melting temperatures ranged between 93 and 110 °C and seem to be driven by the thermal behavior of HU-tetraol monomers. Surprisingly, preliminary results from thermal analyses revealed the occurrence of a striking thermal change in the NIPHEUs upon repetitive heating cycles. This behavior may be related to a thermal-induced bond exchange probably driven by transcarbamoylation reaction. Such interesting vitrimer-like behavior for this new type of NIPHEUs would be unique and should be confirmed by a deeper study before leading to a new range of functional green materials.

Chemical Modification and Processing of Chitin for Sustainable Production of Biobased Electrolytes.

In the present work we report on the development of a novel and sustainable electrolyte based on chitin. Chitin biopolymer was carboxymethylated in simple, mild, and green conditions in order to fine-tune the final properties of the electrolyte. To this end, chitin was modified for various reaction times, while the molar ratio of the reagents, e.g., sodium hydroxide and monochloroacetic acid, was maintained fixed. The resulting chitin derivatives were characterized using various techniques. Under optimized conditions, modified chitin derivatives exhibiting a distinct degree of carboxymethylation and acetylation were obtained. Structural features, morphology, and properties are discussed in relation to the chemical structure of the chitin derivatives. For electrolyte applications, the ionic conductivity increased by three magnitudes from 10-9 S·cm-1 for unmodified chitin to 10-6 S·cm-1 for modified chitin with the highest degree of acetylation. Interestingly, the chitin derivatives formed free-standing films with and without the addition of up to 60% of ionic liquid, the ionic conductivity of the obtained solid electrolyte system reaching the value of 10-3 S·cm-1.

Sensitivity enhancement via multiple contacts in the {1H-29Si}-1H cross polarization experiment: a case study of modified silica nanoparticle surfaces.

{1H-29Si}-1H double cross polarization inverse detection (DCPi) solid-state NMR, has recently been shown to be a powerful tool for studying molecules adsorbed on the silica surface. In this contribution, we develop an improved version (MCPi) which incorporates a block of multiple contact pulses, and quantitatively compare the sensitivities of MCPi and DCPi over a typical range of experimental parameters. The MCPi pulse sequence aims at higher sensitivity and robustness for studying samples with various relaxation characteristics. In the case of dimethyl sulfoxide (DMSO) molecules adsorbed on the silica surface, MCPi performs equally well or up to 2.5 times better than DCPi over a wide range of parameters. The applicability to and performance of MCPi on composite materials was demonstrated using a sample of polymer-silica composite, where significantly higher sensitivity could be achieved at very long total mixing times. The results also showed that both techniques are surface specific in the sense that only the groups close to the surface can be detected.

Plasticization of Polylactide with Myrcene and Limonene as Bio-Based Plasticizers: Conventional vs. Reactive Extrusion.

Polylactide (PLA) was blended by conventional and reactive extrusion with limonene (LM) or myrcene (My) as bio-based plasticizers. As-processed blends were carefully analyzed by a multiscale and multidisciplinary approach to tentatively determine their chemical structure, microstructure, thermal properties, tensile and impact behaviors, and hydrothermal stability. The main results indicated that LM and My were efficient plasticizers for PLA, since compared to neat PLA, the glass transition temperature was reduced, the ultimate tensile strain was increased, and the impact strength was increased, independently of the type of extrusion. The addition of a free radical initiator during the extrusion of PLA/LM was beneficial for the mechanical properties. Indeed, the probable formation of local branched/crosslinked regions in the PLA matrix enhanced the matrix crystallinity, the tensile yield stress, and the tensile ultimate stress compared to the non-reactive blend PLA/LM, while the other properties were retained. For PLA/My blends, reactive extrusion was detrimental for the mechanical properties since My polymerization was accelerated resulting in a drop of the tensile ultimate strain and impact strength, and an increase of the glass transition temperature. Indeed, large inclusions of polymerized My were formed, decreasing the available content of My for the plasticization and enhancing cavitation from inclusion-matrix debonding.

"Ask Ernö": a self-learning tool for assignment and prediction of nuclear magnetic resonance spectra.

BACKGROUND: We present "Ask Ernö", a self-learning system for the automatic analysis of NMR spectra, consisting of integrated chemical shift assignment and prediction tools. The output of the automatic assignment component initializes and improves a database of assigned protons that is used by the chemical shift predictor. In turn, the predictions provided by the latter facilitate improvement of the assignment process. Iteration on these steps allows Ask Ernö to improve its ability to assign and predict spectra without any prior knowledge or assistance from human experts. RESULTS: This concept was tested by training such a system with a dataset of 2341 molecules and their (1)H-NMR spectra, and evaluating the accuracy of chemical shift predictions on a test set of 298 partially assigned molecules (2007 assigned protons). After 10 iterations, Ask Ernö was able to decrease its prediction error by 17 %, reaching an average error of 0.265 ppm. Over 60 % of the test chemical shifts were predicted within 0.2 ppm, while only 5 % still presented a prediction error of more than 1 ppm. CONCLUSIONS: Ask Ernö introduces an innovative approach to automatic NMR analysis that constantly learns and improves when provided with new data. Furthermore, it completely avoids the need for manually assigned spectra. This system has the potential to be turned into a fully autonomous tool able to compete with the best alternatives currently available.Graphical abstractSelf-learning loop. Any progress in the prediction (forward problem) will improve the assignment ability (reverse problem) and vice versa.

Development of dimethyl sulfoxide solubility models using 163,000 molecules: using a domain applicability metric to select more reliable predictions.

The dimethyl sulfoxide (DMSO) solubility data from Enamine and two UCB pharma compound collections were analyzed using 8 different machine learning methods and 12 descriptor sets. The analyzed data sets were highly imbalanced with 1.7-5.8% nonsoluble compounds. The libraries' enrichment by soluble molecules from the set of 10% of the most reliable predictions was used to compare prediction performances of the methods. The highest accuracies were calculated using a C4.5 decision classification tree, random forest, and associative neural networks. The performances of the methods developed were estimated on individual data sets and their combinations. The developed models provided on average a 2-fold decrease of the number of nonsoluble compounds amid all compounds predicted as soluble in DMSO. However, a 4-9-fold enrichment was observed if only 10% of the most reliable predictions were considered. The structural features influencing compounds to be soluble or nonsoluble in DMSO were also determined. The best models developed with the publicly available Enamine data set are freely available online at http://ochem.eu/article/33409 .