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Effects of composition and irradiation on glass transition temperature of methyl methacrylate- styrene copolymers
The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.
Experiments were performed and results obtained to determine the temperature dependence of the modulus of elasticity for a thermoplastic isogrid tube. The isogrid tube was subjected to axial tensile loads of 0-100 lbf and strain was measured at room and elevated temperatures of 100, 120, 140, 160, 180, 190, and 200 F. These were based on tube manufacturer specifying an incorrect glass transition temperature of 210 F. Two protocols were used. For the first protocol the tube was brought to temperature and a tensile test performed. The tube was allowed to cool between tests. For the second protocol the tube was ramped to the desired test temperature and held. A tensile test was performed and the tube temperature ramped to the next test temperature. The second protocol spanned the entire test range. The strain rate was constant at 0.008 in/min. Room temperature tests resulted in the determination of an average modulus of 2.34 x 106 Psi. The modulus decreased above 100 F. At 140 F the modulus had decreased by 7.26%. The two test protocols showed good agreement below 160 F. At this point the glass transition temperature had been exceeded. The two protocols were not repeated because the tube failed.
Yield stress master curves for polymethyl methacrylate, polystyrene, polyvinyl chloride and polyethylene terephthalate below glass transition temperature - polymer chemistry
In many fields, from semiconductors for opto-electronic applications to ionic liquids (ILs) for separations, the glass transition temperature (Tg) of a material is a useful gauge for its potential use in practical settings. As a result, there is a great deal of interest in predicting Tg using molecular simulations. However, the uncertainty and variation in the trend shift method, a common approach in simulations to predict Tg, can be high. This is due to the need for human intervention in defining a fitting range for linear fits of density with temperature assumed for the liquid and glass phases across the simulated cooling. The definition of such fitting ranges then defines the estimate for the Tg as the intersection of linear fits. We eliminate this need for human intervention by leveraging the Shapiro–Wilk normality test and proposing an algorithm to define the fitting ranges and, consequently, Tg. Through this integration, we incorporate into our automated methodology that residuals must be normally distributed around zero for any fit, a requirement that must be met for any regression problem. Consequently, fitting ranges for realizing linear fits for each phase are statistically defined rather than visually inferred, obtaining an estimate for Tg without any human intervention. The method is also capable of finding multiple linear regimes across density vs temperature curves. We compare the predictions of our proposed method across multiple IL and semiconductor molecular dynamics simulation results from the literature and compare other proposed methods for automatically detecting Tg from density–temperature data. We believe that our proposed method would allow for more consistent predictions of Tg. We make this methodology available and open source through GitHub.
The relationship between chain microstructure and glass transition temperature (T g ) is complicated for poly(alkyl methacrylate)s and poly(alkyl acrylate)s. Despite intensive studies, relationships between the structures of these polymers and their properties, including solution characteristics and T g , are still controversial. Solution properties, chain conformations, including Flory's characteristic ratio (C ∞ ), persistence length (l p ), and chain diameters, and T g are reported for series of poly(n-alkyl acrylate)s and poly(n-alkyl methacrylate)s having alkyl side chain lengths (n) ranging from 1 to 10 carbons in length, with uniform and well-characterized tacticities. Chain flexibilities of both series of polymers decrease as n increases, reflecting increased hindrances to rotation about backbone bonds as side chains become longer. Conversely, the T g s for both series of polymers decrease substantially as n increases, reflecting the greater side chain mobilities of long alkyl substituents. For shorter alkyl chain lengths, T g s for the poly(n-alkyl acrylate)s are much lower than for the corresponding poly(n-alkyl methacrylate)s, a difference which has been attributed in the past to the presumed reduced chain flexibility of polymethacrylates due to the presence of the α-methyl substituent. However, contrary to such expectations, C ∞ and l p values for these two series of polymers are nearly identical at a given n value, except for the longest n-alkyl substituents. Instead, the differences in T g may be attributed to the differences in tacticity of the two series – almost ideally atactic for the poly(n-alkyl acrylate)s but high in syndiotacticity for the poly(n-alkyl methacrylate)s. The T g s for the two series of polymers approach that of polyethylene at longer alkyl chain lengths. In addition, the effects of tacticity and chain dynamics on C ∞ , l p , and T g of poly(methyl methacrylate) is discussed.
PMR-15 polyimide, developed in the mid-1970's at the NASA Lewis Research Center, is recognized as a state-of-the-art high-temperature resin for composite applications in the temperature range of 500 to 550 F (260 to 288 C). PMR-15 offers easy processing and good property retention at a reasonable cost. For these reasons, it is widely used in both military and commercial aircraft engine components. Traditionally, polyimide composites have been designed for long-term use at 500 to 600 F over thousands of hours. However, new applications in reusable launch vehicles (RLV's) require lightweight materials that can perform for short times (tens of hours) at temperatures between 800 and 1000 F (425 and 538 C). Current efforts at Lewis are focused on raising the use temperature of polyimide composites by increasing the glass-transition temperature of the matrix resins. Achieving this dramatic increase in the upper use temperature without sacrificing polymer and composite processability is a major technical challenge.
We developed tgBoost a machine learning model to predict glass transition temperature ( T g) of organic species considering their molecular structure and functionality for better predictions of the phase state of secondary organic aerosols.
The presence of a hexafluoroisopropylidene (6F) connecting group in aryl dianhydrides used to prepare aromatic condensation polyimides provides high glass transition temperature (T sub g) polyimides with excellent thermo-oxidative stability. The purpose of this study was to determine if a trifluorophenyl-ethylidene (3F) connecting group would have a similar effect on the T sub g of aromatic condensation polyimides. A new dianhydride containing the 3F connecting group was synthesized. This dianhydride and an aromatic diamine also containing the 3F connecting group were used together and in various combinations with known diamines or known dianhydrides, respectively, to prepare new 3F containing condensation polyimides. Known polyimides, including some with the 6F connecting linkage, were also prepared for comparison purposes. The new 3F containing polymers and the comparison polymers were prepared by condensation polymerization via the traditional amic-acid polymerization method in N,N-dimethylacetamide solvent. The solutions were characterized by determining their inherent viscosities and then were thermally converted into polyimide films under nitrogen atmosphere at 300 to 500 C, usually 350 C. The T sub g's of the films and resin discs were then determined by thermomechanical analysis and were correlated as a function of the final processing temperatures of the films and resin discs. The results showed that similarities existed in the T sub g's depending on the nature of the connecting linkage in the monomers used to prepare the condensation polyimides.
We develop a Stockmayer fluid model for molecular dynamics simulations of ionic liquids that captures molecular polarization, ionic conductivity, viscosity, and glass transition temperature, using ethylammonium nitrate (EAN) as an example. The ions in EAN are treated as spheres interacting via the Lennard-Jones potential with an embedded point charge and a permanent dipole moment. We show that our simulation results for EAN are consistent with experimental data and then explore the effects of the molecular parameters on the viscosity of ionic liquids. Our results indicate that viscosity monotonically increases with ionic charge and dipole moment but non-monotonically changes with ionic diameter (or molar volume). This non-monotonic trend arises from the competition among the electrostatic interactions, molecular packing, and size asymmetry between the cation and anion. In conclusion, our model also shows that long-lived ion pairs result in higher viscosities.
Kenning et al. discovered the collapse of the effective response time, 𝑡$^{eff}_{𝐻}$(𝑡 w ;𝑇), independent of aging time, 𝑡 w , as one approached the spin glass transition temperature 𝑇 g from below. Nordblad and Lundgren observed the same effect by reducing the temperature from 𝑇 g (𝐻). We analyze the behavior of ln 𝑡$^{eff}_{𝐻}$(𝑡 w ;𝑇) in the temperature range near and below 𝑇 g using a scaling law that takes into account both the magnetic field 𝐻 and the time-dependent spin-glass coherence length 𝜉(𝑡,𝑡 w ;𝑇). As a result, an overdetermined predictive fit to the experimental data accounts for a quasioscillatory structure of ln 𝑡$^{eff}_{𝐻}$ (𝑡 w ;𝑇) as a function of magnetic field 𝐻 2 for reduced temperatures as large as 𝑇/𝑇 g = 0.98.
Abstract Atmospheric aerosols can assume liquid, amorphous semi-solid or glassy, and crystalline phase states. Particle phase state plays a critical role in understanding and predicting aerosol impacts on human health, visibility, cloud formation, and climate. Melting point depression increases with decreasing particle diameter and is predicted by the Gibbs–Thompson relationship. This work reviews existing data on the melting point depression to constrain a simple parameterization of the process. The parameter $$\xi $$ ξ describes the degree to which particle size lowers the melting point and is found to vary between 300 and 1800 K nm for a wide range of particle compositions. The parameterization is used together with existing frameworks for modeling the temperature and RH dependence of viscosity to predict the influence of particle size on the glass transition temperature and viscosity of secondary organic aerosol formed from the oxidation of $$\alpha $$ α -pinene. Literature data are broadly consistent with the predictions. The model predicts a sharp decrease in viscosity for particles less than 100 nm in diameter. It is computationally efficient and suitable for inclusion in models to evaluate the potential influence of the phase change on atmospheric processes. New experimental data of the size-dependence of particle viscosity for atmospheric aerosol mimics are needed to thoroughly validate the predictions.
PMR (polymerization of monomeric reactants) methodology was used to prepare 70 different polyimide oligomeric resins and 30 different unidirectional graphite fiber/polyimide composites. Monomeric composition as well as chain length between sites of crosslinks were varied to examine their effects on resin thermo-oxidative stability and glass transition temperature (Tg) of the cured/postcured resins. A linear correlation of decreasing 316 C resin weight loss/surface area versus (1) decreasing aliphatic content, or (2) increasing benzylic/aliphatic content stoichiometry ratio over a wide range of resin compositions was observed. An almost linear correlation of Tg versus molecular distance between the crosslinks was also observed. An attempt was made to correlate Tg with initial composite mechanical properties (flexural strength and interlaminar shear strength). However, the scatter in mechanical strength data prevented obtaining a clear correlation. Instead, only a range of composite mechanical properties was obtained at 25, 288, and 316 C. Perhaps more importantly, what did become apparent during the correlation study was (1) the PMR methodology could be used to prepare composites from resins containing a wide variety of monomer modifications, (2) that these composites almost invariably provided satisfactory initial mechanical properties as long as the resins formulated exhibited satisfactory processing flow, and (3) that PMR resins exhibited predictable rates of 316 C weight loss/surface area based on their benzylic/aliphatic stoichiometery ratio.
Abstract. Volatility and viscosity are important properties of organic aerosols (OA), affecting aerosol processes such as formation, evolution, and partitioning of OA. Volatility distributions of ambient OA particles have often been measured, while viscosity measurements are scarce. We have previously developed a method to estimate the glass transition temperature (Tg) of an organic compound containing carbon, hydrogen, and oxygen. Based on analysis of over 2400 organic compounds including oxygenated organic compounds, as well as nitrogen- and sulfur-containing organic compounds, we extend this method to include nitrogen- and sulfur-containing compounds based on elemental composition. In addition, parameterizations are developed to predict Tg as a function of volatility and the atomic oxygen-to-carbon ratio based on a negative correlation between Tg and volatility. This prediction method of Tg is applied to ambient observations of volatility distributions at 11 field sites. The predicted Tg values of OA under dry conditions vary mainly from 290 to 339 K and the predicted viscosities are consistent with the results of ambient particle-phase-state measurements in the southeastern US and the Amazonian rain forest. Reducing the uncertainties in measured volatility distributions would improve predictions of viscosity, especially at low relative humidity. We also predict the Tg of OA components identified via positive matrix factorization of aerosol mass spectrometer (AMS) data. The predicted viscosity of oxidized OA is consistent with previously reported viscosity of secondary organic aerosols (SOA) derived from α-pinene, toluene, isoprene epoxydiol (IEPOX), and diesel fuel. Comparison of the predicted viscosity based on the observed volatility distributions with the viscosity simulated by a chemical transport model implies that missing low volatility compounds in a global model can lead to underestimation of OA viscosity at some sites. The relation between volatility and viscosity can be applied in the molecular corridor or volatility basis set approaches to improve OA simulations in chemical transport models by consideration of effects of particle viscosity in OA formation and evolution.
Abstract Machine 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 (T g ) 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 newT g – 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.
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Semiconducting donor–acceptor (D–A) polymers have attracted considerable attention toward the application of organic electronic and optoelectronic devices. However, a rational design rule for making semiconducting polymers with desired thermal and mechanical properties is currently lacking, which greatly limits the development of new polymers for advanced applications. Here, polydiketopyrrolopyrrole (PDPP)-based D–A polymers with varied alkyl side-chain lengths and backbone moieties are systematically designed, followed by investigating their thermal and thin film mechanical responses. The experimental results show a reduction in both elastic modulus and glass transition temperature (Tg) with increasing side-chain length, which is further verified through coarse-grained molecular dynamics simulations. Informed from experimental results, a mass-per-flexible bond model is developed to capture such observation through a linear correlation between Tg and polymer chain flexibility. Using this model, a wide range of backbone Tg over 80 °C and elastic modulus over 400 MPa can be predicted for PDPP-based polymers. This study highlights the important role of side-chain structure in influencing the thermomechanical performance of conjugated polymers, and provides an effective strategy to design and predict Tg and elastic modulus of future new D–A polymers.
Polyhydroxyalkanoates (PHAs) represent an emerging class of biosynthetic and biodegradable polyesters that exhibit considerable potential to replace petroleum-based plastics towards a sustainable future. Despite the promise, general structure–property mappings within this class of polymers remain largely unexplored. An efficient exploration of this vast chemical space calls for the development and validation of predictive methods for accurate estimation of a diverse range of properties for PHA-based polymers. Towards this aim, we present and validate in this work the results of our molecular dynamics (MD) simulation based approach aimed at predicting glass transition temperatures (T g ) of PHA-based polymers. Since generally available and widely used polymer forcefields exhibit a relatively poor performance for T g predictions, we have developed a new forcefield by modifying the polymer consistent force field (PCFF) via refining a selected set of torsion potentials of the polymer backbone using accurate density functional theory (DFT) computations. After carefully assessing the dependence of critical simulation parameters, such as, polymer chain length, number of polymer chains, supercell size, and thermal quenching rate used in the simulation, the applicability and transferability of the modified PCFF (mPCFF) is demonstrated by directly comparing the computed T g predictions of various polymers with different chemistries, polymer side chain lengths and functional groups forming the polymer side chains against the respective experimentally measured values. Furthermore, the transport properties such as self-diffusion coefficient and viscosity are computationally determined and their well-known correlation with the target properties is demonstrated. Lastly, we have employed the developed approach to predict T g values for a number of yet-to-be-synthesized PHA-based polymers with a diverse set of functional groups in the polymer side chains. The results are further rationalized by correlating the predicted T g values with the inter-chain H-bond formation tendencies of the different side chain functional groups. This work represents an important first step towards computationally guided design of PHA-based functional polymers and opens up new directions for a systematic investigation of composition- and configuration-dependent structure–property relationships in more complex binary and ternary copolymer systems.