Glass Transition Temperature Of Polymer Research Articles

A General Approach to Activate Second-Scale Room Temperature Photoluminescence in Organic Small Molecules.

Organic small molecules that exhibit second-scale phosphorescence at room temperature are of interest for potential applications in sensing, anticounterfeiting, and bioimaging. However, such materials systems are uncommon-requiring millisecond to second-scale triplet lifetimes, efficient intersystem crossing, and slow rates of nonradiative recombination. Here, a simple and scalable approach is demonstrated to activate long-lived phosphorescence in a wide variety of molecules by suspending them in rigid polymer hosts and annealing them above the polymer's glass transition temperature. This process produces submicron aggregates of the chromophore, which suppresses intramolecular motion that leads to nonradiative recombination and minimizes triplet-triplet annihilation that quenches phosphorescence in larger aggregates. In some cases, evidence of excimer-mediated intersystem crossing that enhances triplet generation in aggregated chromophores is found. In short, this approach circumvents the current design rules for long-lived phosphors, which will streamline their discovery and development.

Photorefractivity and photocurrent dynamics of triphenylamine-based polymer composites

The photorefractive properties of triphenylamine polymer-based composites with various composition ratios were investigated via optical diffraction, response time, asymmetric energy transfer, and transient photocurrent. The composite consisted of a photoconductive polymer of poly((4-diphenylamino)benzyl acrylate), a photoconductive plasticizer of (4-diphenylamino)phenyl)methanol, a sensitizer of [6,6]-phenyl-C61-butyric acid methyl ester, and a nonlinear optical dye of (4-(azepan-1-yl)-benzylidene)malononitrile. The photorefractive properties and related quantities were dependent on the composition, which was related to the glass transition temperature of the photorefractive polymers. The quantum efficiency (QE) of photocarrier generation was evaluated from the initial slope of the transient photocurrent. Transient photocurrents were measured and showed two unique peaks: one in the range of 10−4 to 10−3 s and the other in the range of 10−1 to 1 s. The transient photocurrents was well simulated (or reproduced) by the expanded two-trapping site model with two kinds of photocarrier generation and recombination processes and two different trapping sites. The obtained photorefractive quantity of trap density was significantly related to the photoconductive parameters of QE.

Polymer design using machine learning: A quest for high glass transition temperature

This research introduces an advanced data-centric framework tailored for polymer design. Developing a machine learning (ML) model utilizing molecular descriptors, the study aims to forecast the glass transition temperature of polymers. Employing the Retrosynthetically Interesting Chemical Substructures (BRICS) technique, a set of 10,000 novel polymers is synthesized, followed by predicting their glass transition temperature (Tg) values using a pre-existing ML model trained for this purpose. Gradient Boosting and Extra Trees models outperform K Neighbors and Random Forest (KNN) models in predicting performance parameters with R2 scores of 0.99 and 0.99, and lower root mean squared error values of 24 and 32 K, respectively. The selection of polymers is done on the basis of glass transition temperature, those with higher glass transition temperature are selected. We evaluate the ease of synthetic accessibilities of the selected polymers alongwith their structural variations which indicates a diverse range among them. Through the successful identification and enhancement of novel polymers, these innovative methods greatly enhance the possibilities of discovering superior materials tailored for cutting-edge applications.

Свойства эпоксидной матрицы, модифицированной нитридом бора

In this work, the viscoelastic properties of epoxy polymers modified with hexagonal boron nitride were studied. The calculated elastic modulus is compared with its experimental value. It has been established that boron nitride does not significantly affect the glass transition temperature of epoxy polymers

Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers.

The glass transition temperature of polymers is a key parameter in meeting the application requirements for energy absorption. Previous studies have provided some data from slow, expensive trial-and-error procedures. By recognizing these data, machine learning algorithms are able to extract valuable knowledge and disclose essential insights. In this study, a dataset of 7174 samples was utilized. The polymers were numerically represented using two methods: Morgan fingerprint and molecular descriptor. During preprocessing, the dataset was scaled using a standard scaler technique. We removed the features with small variance from the dataset and used the Pearson correlation technique to exclude the features that were highly connected. Then, the most significant features were selected using the recursive feature elimination method. Nine machine learning techniques were employed to predict the glass transition temperature and tune their hyperparameters. The models were compared using the performance metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). We observed that the extra tree regressor provided the best results. Significant features were also identified using statistical machine learning methods. The SHAP method was also employed to demonstrate the influence of each feature on the model's output. This framework can be adaptable to other properties at a low computational expense.

Experimental and molecular dynamics study on thermal‐mechanical properties of epoxy shape memory polymers

AbstractExperimental and molecular dynamics simulations were employed to investigate epoxy shape memory polymers' thermal‐mechanical properties and shape memory characteristics, utilizing polyether amine and modified hexanediamine as curing agents following the material prerequisites for variable stiffness fixtures. Glass transition temperature and Young's modulus of epoxy shape memory polymers are characterized by dynamic mechanical analysis. The consistency of the glass transition temperature between the simulation model and the experimental findings verified the accuracy of the molecular dynamics simulation. The uniaxial tensile simulation revealed that non‐bond energy predominantly exerts its influence in stretch. After reaching the yield stress, the weight fraction of the curing agent affects the generation of internal pores, with the yield stress of systems exhibiting substantial pores being minor. Systems characterized by a higher weight fraction of curing agents are more prone to pore formulation. Simulation of shape memory properties demonstrates that the entropy resulting from differences in molecular weight impacts the simulated system's shape fixation ratio and shape recovery ratio.

Applying machine learning and quantum chemistry to predict the glass transition temperatures of polymers

Glass transition temperature (Tg) is important for understanding the physical and mechanical properties of a polymer material because it relates to the thermal energy required to transition between a hard glassy state and a soft rubbery one. Over the years, various models have been developed for predicting this thermal property from molecular structure to aid in designing novel polymers in selected classes. This work builds on those efforts by utilizing both machine learning (ML) and quantum chemistry (QC) techniques to develop models that can predict Tg values from the molecular structure under different data availability scenarios and for a wide variety of polymer types. For the ML model, a graph convolutional network (GCN) was used to map topological polymer features; this model was trained against a dataset of more than 7500 Tg values and resulted in a root mean square error (RMSE) of 38.1 °C. The QC-based regression model was trained on 83 Tg values and produced an RMSE of 34.5 °C. This work demonstrated that while both model techniques produce accurate predictions and are suitable for different data availability scenarios, the QC-based regression model offered a more interpretable model framework with significantly less training data.

Systematic studies on polyacrylates containing side‐chain hydrogen bonding units: Significant effects of the pendant functions on thermal behaviors and solubility of polymers

AbstractThe synthesis of polyacrylates containing various pendant hydrogen bonding or non‐hydrogen bonding functions and the effects of side‐chain functions on their physical properties are described. The polyacrylates possessing urea, urethane, N‐substituted urea, or carbonate groups are prepared by the free radical polymerization of acrylate monomers possessing the corresponding functional groups. The presence of stronger hydrogen bonding interactions both in bulk and solutions of a highly polar solvent such as dimethyl sulfoxide (DMSO) is supported by the infrared (IR) spectra, whereas the urethane‐based polyacrylate does not exhibit the interaction in the solution. The presence of the stronger hydrogen bonding interactions of the urea unit is discussed by the IR and nuclear magnetic resonance spectra and the density functional theory calculations of the corresponding model compounds. The solubility in organic solvents and the glass transition temperature (Tg) of polymers are also dependent on the structure of the pendant groups. Polar aprotic solvents such as DMSO can only dissolve the urea‐based polyacrylate, while the other polymers are soluble in a much wider range of solvents such as tetrahydrofuran. The urea‐based polyacrylate shows the highest Tg among the polymers studied owing to the presence of plural hydrogen bonding sites in the urea groups.

Preparation and Properties of Soft-/Hard-Switchable Transparent Wood with 0 °C as a Boundary

Transparent wood has excellent optical and thermal properties and has great potential utilization value in energy-saving building materials, optoelectronic devices, and decorative materials. In this work, transparent wood with soft-/hard-switchable and shape recovery capabilities was prepared by introducing an epoxy-based polymer with a glass transition temperature of about 0 °C into the delignified wood template. The epoxy resin was well filled in the pore structure of the delignified wood, and the as-prepared wood exhibited excellent transparency; the optical transmittance and haze of the transparent wood with a thickness of 2.0 mm were approximately 70% and 95%, respectively. Because the glass transition temperature of the epoxy-based polymer was about 0 °C, the prepared transparent wood was rigid below 0 °C and flexible above °C; meanwhile, the transparent wood exhibited shape change and shape recovery properties. Incorporating optical transparency and soft-/hard-switchable ability into the transparent wood opens a new avenue for developing advanced functional wood-based materials.

Efficient Prediction of Polymer Glass Transition Temperatures through Machine Learning Methods

The glass transition temperature (Tg) plays a crucial role in defining polymer properties. Despite the widespread use of machine learning for material design and property prediction, there are still challenges concerning the interpretability and model performance when predicting Tg. In this study, Simplified Molecular Input Line Entry System strings are utilised to encode the polymer structure, which are then transformed into molecular descriptors for analytical training and prediction of Tg using Artificial Neural Network and Random Forest models. Meticulous hyperparameter tuning of the Random Forest model was performed, resulting in reasonable Tg predictions. This methodology forges a connection between polymer structure and Tg, opening up new avenues for research in the field of polymers.

Effect of silane coupling agent modification on shape memory behavior and mechanical properties of knitting fabric reinforced composites

Continuous glass fiber fabrics can improve the shortcomings of shape memory polymers in terms of mechanical properties. However, the mechanical properties of shape memory composites will not be optimal if the interface between the glass fibers and the matrix is poorly bonded. The purpose of this study is to explore the thermomechanical properties and shape memory behavior of shape memory epoxy resin and composites before and after the chemical modification of glass fiber. The effects of the ratios of epoxy to curing agent and whether the knitting fabrics are modified with silane coupling agent on the properties of the composites are investigated. The results show that the thermal decomposition temperature and glass transition temperature of the shape memory epoxy polymers increase with the increasing mass ratio of diaminodiphenylmethane. The shape fixing ratios and shape recovery ratios of the knitting fabric reinforced composites with different mass contents and silane coupling agent modification are above 98.0% at 18.0% tensile strain. After 10 cycles, the shape memory performance of the composites modified with KH560 is still good. Besides, the addition of silane coupling agent to the composites can reduce the shape recovery time by 17.9% and increase the recovery force with 30.0% subjected to 18.0% tensile strain. The tensile strength of the shape memory composites modified with silane coupling agent can increase 32.8% as compared with that of the composites without modification.

Mechanical properties and glass transition temperature of metal-organic framework-filled epoxy resin: A molecular dynamics study

The effect of ZIF-8 (zeolitic imidazolate framework-8) nanoparticles on the mechanical properties of thermoset epoxy-based nanocomposite is investigated using molecular dynamic (MD) simulation. To investigate the effect of the force field type on the results, the mechanical properties and glass transition temperature (Tg) of pure EPON862-TETA polymer were compared at various temperatures using UFF and COMPASS force fields. The results showed that the COMPASS force field provided better simulation results for systems containing polymers. To investigate the effect of ZIF-8 nanoparticle and its size, three simulation boxes were constructed containing cross-linked epoxy resin and ZIF-8 with different sizes while maintaining a weight ratio of 90:10. The mechanical properties and glass transition temperature were calculated for the epoxy/ZIF-8 nanocomposite. The results indicated that adding ZIF-8 nanoparticles improves the Tg, as well as Young's modulus, bulk modulus, and shear modulus at different temperatures compared to the pure epoxy resin.

The glass transition temperature investigation of polymers by molecular dynamic simulations

In this paper, the process of glass transition is studied, the theory of which is not fully developed. Computer modeling can be used to understand the theory of this phenomenon. The glass transition temperature is influenced by a large number of polymer parameters: cooling rate, pressure, presence of diluent, structural features, etc. We are considering a number of different polymers to test the ability of the pcff+ force field to determine the glass transition temperature. The effect of tactility, composition, pressure and the presence of a diluent on the glass transition temperature of polymers will be shown using molecular dynamics (MD) and pcff+ force-field modeling. The effect of tact was studied using the use of atactic, isotactic and syndiotactic poly(methyl methacrylate) and atactic, isotactic and syndiotactic polypropylene. The LAMMPS code integrated into the MedeA computing environment was used to simulate the molecular dynamics of polymers. The calculation of the glass transition temperature at different cooling rates is closely related to the balanced initial systems. Due to the inability of current atomistic simulations to achieve the required cooling rates, as in experiments, the gap was not sharp and quite obvious. The data obtained show that the pcf+ force field describes tactics quite effectively and gives differences in the glass transition temperature for different types of tact. For polymers diluted with CO2, the glass transition temperature decreases almost linearly, which is in good agreement with the experiment. As expected, the pressure increases the glass transition temperature. However, at high pressure, the slope fracture disappears, and the determination of Tg becomes extremely difficult. The simulated annealing process will be applied to a set of polymers to obtain graphs of the specific volume versus temperature and determine the glass transition temperature.

Correlation between fragility and surface glass transition temperature of polymers

The fragility of glass describes how rapidly its molecules slow down as it is cooled near its glass transition temperature. In nanoscale films, polymer glasses with higher fragility experience larger reductions in their Tg compared to those with lower fragility. We investigated whether this is due to the free surface of the polymers, which can cause the surface Tg (Tgsurf) to decrease relative to the bulk Tg. By measuring Tgsurf of various polymers, we found that the shift in Tgsurf relative to the bulk Tg increased with fragility. This suggests that more fragile polymers are more susceptible to the free surface effect. We explain this using the concept of energy landscape, as it is used to explain the different slowdown rates between strong and fragile glass formers at Tg.

Explainability and extrapolation of machine learning models for predicting the glass transition temperature of polymers

AbstractMachine learning (ML) offers promising tools to develop surrogate models for polymers' structure–property relations. Surrogate models can be built upon existing polymer data and are useful for rapidly predicting the properties of unknown polymers. The accuracy of such ML models appears to depend on the feature space representation of polymers, the range of training data, and learning algorithms. Here, we establish connections between these factors for predicting the glass transition temperature (Tg) of polymers. Our analysis suggests linear models with fewer fitting parameters are as accurate as nonlinear models with many hidden and unexplainable parameters. Also, the performance of a monomer topology‐based ML model is found to be qualitatively identical to that of a physicochemical descriptor‐based ML model. We find that the ML models's performance in the extrapolative region is enhanced as the property range of the training data increases. Moreover, we establish new Tg – polymer chemistry correlations via ML. Our work illustrates how ML can advance the fundamental understanding of polymer structure–property correlations and its efficacy for extrapolation problems.

Self-healing recyclable polymers based on azobenzenes with thermoset like behaviour

Self-healing materials are considered intelligent materials capable of recovering their original shapes from damaged or distorted forms after exposure to specific stimuli, such as chemicals, heat, moisture, or light. The utilization of thermoreversible crosslinks in polymer networks has garnered increasing interest as a method to create such materials. In these polymers, damage can be repaired simply by heating them above a gel-liquid transition temperature. Upon cooling, the material regains its previous cross-linked properties. However, finding alternative external stimuli other than heat presents an intriguing challenge. Among the various possibilities, light is the preferred choice due to its potential for remote and spatially-controlled activation. Additionally, the process can be easily paused and resumed by turning the excitation light on and off.Furthermore, the photo-switching of the glass transition temperature (Tg) in azobenzene-containing polymers induces reversible transitions from a solid to a liquid state. These reversible trans-to-cis transitions are triggered by UV and visible light irradiation. This phenomenon offers a novel approach to designing healable polymers and allows for precise control over the mechanical properties of polymers with spatio-temporal resolution.The main objective of this study is to investigate the feasibility of using light as a self-healing trigger in a recyclable polymer based on physically cross-linked epoxy/amine formulations. To achieve this goal, the matrix was modified with 4-phenylazophenol (PAP), which forms homogeneous blends with the polymer through hydrogen bond interactions. Films were prepared and characterized using various experimental techniques. Polymerization kinetics of samples with and without PAP was determined using FTIR, which was also employed for characterizing the final materials. Tg of the resulting products was measured using DSC, revealing a decrease in its value for PAP-modified samples. The transition from a solid to a liquid state during UV light irradiation was observed using rheometric analysis. The reversible photoisomerization behaviour of epoxy/amine/PAP was investigated using UV–visible spectroscopy in films prepared by spin-coating THF solutions.The ability of the material to self-heal at room temperature using light as a remote source was confirmed through TOM (Transmission Optical Microscopy) observations and quantified by profilometric measurements. In addition, surface mechanical properties were evaluated in order to determine the effect of the incorporation of the PAP, and the ability of the repaired material to recover its properties. Promising findings indicate that this recyclable polymer holds significant potential for multiple applications where its self-healing ability will be of great importance.

Algorithmic encoding of adaptive responses in temperature-sensing multimaterial architectures.

We envision programmable matters that can alter their physical properties in desirable manners based on user input or autonomous sensing. This vision motivates the pursuit of mechanical metamaterials that interact with the environment in a programmable fashion. However, this has not been systematically achieved for soft metamaterials because of the highly nonlinear deformation and underdevelopment of rational design strategies. Here, we use computational morphogenesis and multimaterial polymer 3D printing to systematically create soft metamaterials with arbitrarily programmable temperature-switchable nonlinear mechanical responses under large deformations. This is made possible by harnessing the distinct glass transition temperatures of different polymers, which, when optimally synthesized, produce local and giant stiffness changes in a controllable manner. Featuring complex geometries, the generated structures and metamaterials exhibit fundamentally different yet programmable nonlinear force-displacement relations and deformation patterns as temperature varies. The rational design and fabrication establish an objective-oriented synthesis of metamaterials with freely tunable thermally adaptive behaviors. This imbues structures and materials with environment-aware intelligence.

AFM dilatometry measurements on ultra‐stable fluoropolymer glasses: Further evidence of extreme fictive temperature reduction

AbstractUltra‐stable amorphous fluoropolymers glasses were created using vacuum pyrolysis deposition (VPD). Glass films with thickness ranging from 90 to 160 nm were grown at a substrate temperature of 0.86 Tg, where Tg is the glass transition temperature of the virgin polymer and is in units of K. Atomic force microscope (AFM) dilatometry measurements were conducted to investigate density behavior of the ultra‐stable glasses. Thickness measurements were made in stepwise fashion over a range of temperatures from ambient to above the Tg. Results show that the intersections of the line for the equilibrium liquid and those for the rejuvenated and stable glasses identifying the fictive temperature Tf result in Tf, rejuvenated = 347.3 K and Tf, stable = 269.5 K, that is, nearly 80 K below the Tg of the rejuvenated material and well below the notional Kauzmann temperature as estimated from the Vogel‐Fultcher‐Tammann (VFT) analysis of the cooling rate dependence of the calorimetric glass transition temperature reported previously. The results corroborate the published calorimetric results on the same ultra‐stable fluoropolymer glasses that witnessed Tf reductions of up to 62.6 K below the Tg of the rejuvenated system. In addition, to demonstrate the versatility of the AFM dilatometry methodology for the thin film response, isothermal de‐aging experiments were carried out to illustrate the devitrification kinetics. We also carried out one of the Kovacs’ signature key experiments, the asymmetry of approach, to further illustrate the method.

Measuring glass transition of a polymer coating layer over working fiber using forward Brillouin scattering.

The glass transition temperature is a key parameter of polymer coating layers that protect optical fibers, and it affects the proper function of the fibers in their service environment. Established protocols for glass transition temperature measurements are destructive, require samples of specific geometries, and may only be carried out offline. In this work, we report the nondestructive measurement of the glass transition temperature of an acrylate polymer coating layer over a working standard fiber. The method is based on forward stimulated Brillouin scattering. A large decrease in the modulus of the coating layer above the glass transition temperature manifests in the narrowing of the modal linewidths in the forward Brillouin scattering spectrum. The transition temperature agrees with the standard dynamic mechanical analysis of samples made of the same polymer. The protocol can be useful for coating materials research and development, production line quality assurance, and preventive maintenance.

Maximizing the electrochemical performance of supercapacitor electrodes from plastic waste

The management of the increasing volume of plastic waste has become a key challenge for society. A promising strategy now consists in the transformation of plastic waste into high-value materials that can be utilized in energy storage devices such as batteries and supercapacitors. In this study, we demonstrate a two-step procedure, involving pyrolysis, followed by chemical activation that will convert common plastic waste into activated carbons (ACs). This technique makes ACs suitable for supercapacitor electrode materials. Further, the electrochemical performance of ACs is outstanding in terms of capacitance, energy density, and cycling stability. Besides the well-established parameters, including a specific surface area and micropore volume, we found that other critical factors such as polymer glass transition temperature, polymer-activating agent miscibility, activating agent (K2CO3):AC ratio, and AC water dispersion stability also play a crucial role in determining the supercapacitors performance. Controlling these parameters, we obtained ACs as supercapacitor electrodes from a range of plastic waste materials with a competitive electrochemical performance. Specifically, the ACs exhibited a specific capacitance of 220 F g−1 (at a current density of 1 A g−1), energy and power densities of 61.1 Wh kg−1 and 36.9 kW kg−1, respectively, and excellent cycling stability (95 % retention after 30,000 cycles). Our findings provide a pathway towards transforming plastic waste into valuable electrode materials for supercapacitors.