The effects of molecular structure on the thermochemical properties of phenolics and related polymers
Effects of molecular structure on thermochemical properties of phenolic resins and related polymers
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Effects of molecular structure on thermochemical properties of phenolic resins and related polymers
We report the properties of molecular beam epitaxy deposited AlBN thin films on a recently developed epitaxial nitride metal electrode, Nb2N. While a control AlN thin film exhibits standard capacitive behavior, distinct ferroelectric switching is observed in the AlBN films with increasing Boron mole fraction. The measured remnant polarization Pr∼15μC/cm2 and coercive field Ec∼ 1.45 MV/cm in these films are smaller than those recently reported on films deposited by sputtering, due to incomplete wake-up, limited by current leakage. Because AlBN preserves the ultrawide energy bandgap of AlN compared to other nitride hi-K dielectrics and ferroelectrics, and it can be epitaxially integrated with GaN and AlN semiconductors, its development will enable several opportunities for unique electronic, photonic, and memory devices.
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Abstract The excited-state properties of molecular crystals are important for applications in organic electronic devices. TheGWapproximation and Bethe-Salpeter equation (GW+BSE) is the state-of-the-art method for calculating the excited-state properties of crystalline solids with periodic boundary conditions. We present the PAH101 dataset ofGW+BSE calculations for 101 molecular crystals of polycyclic aromatic hydrocarbons (PAHs) with up to ~500 atoms in the unit cell. To the best of our knowledge, this is the firstGW+BSE dataset for molecular crystals. The data records include theGWquasiparticle band structure, the fundamental band gap, the static dielectric constant, the first singlet exciton energy (optical gap), the first triplet exciton energy, the dielectric function, and optical absorption spectra for light polarized along the three lattice vectors. The dataset can be used to (i) discover materials with desired electronic/optical properties, (ii) identify correlations between DFT andGW+BSE quantities, and (iii) train machine learned models to help in materials discovery efforts.
density was performed over a range of temperatures below the glass transition temperature. The physical characterization, elastic properties and notched tensile strength all as a function of molecular weight and test temperature were determined. For the uncrosslinked SI material, it was shown that notched tensile strength is a strong function of both temperature and molecular weight, whereas stiffness is only a strong function of temperature. For the crosslinked PETI-SI material, it was shown that the effect of crosslinking significantly enhances the mechanical performance of the low molecular weight material; comparable to that exhibited by the high molecular weight material.
Molten salt mixtures are integral part of highly important technological applications such as nuclear reactors. However, due to inherent difficulties associated with experiment at high temperatures and the intrinsic complexity of liquid-phase multi-component systems, understanding their properties at a molecular level remains a challenge. Here, we report on an ab initio molecular dynamics investigation on structural, electronic, transport, and thermal properties of two common molten salt mixtures, (K, Li)Cl and (K,Na)Cl, at five different compositions and three temperatures. Most of the properties were found to depend on both composition and temperature. While properties, like atomic charges, show additive behaviors, other properties, such as electrical conductivity, show considerable deviations from additivity. We shall show that the mixing of the molten salt mixtures is mainly driven by entropy, and that the KCl and LiCl mix better than KCl and NaCl. Our computational results are in general consistent with available experimental data. Comparison with available theoretical data is also provided.
Classical molecular dynamics (MD) simulations provide insight into the structure and physicochemical properties of materials with atomic resolution. However, the length and time scales accessible to atomistic MD are orders of magnitude smaller than many relevant processes such as the response of a bulk material to experimentally accessible strain rates, which presents challenges when comparing models to experimental measurements. Bottom-up coarse-graining provides a means for systematically mapping atomistic information to lower resolution models to increase the length and time scales achievable by simulation. Cellulose is an abundant carbohydrate biopolymer with applications to many fields of research, such as materials science and renewable energy, due to its desirable mechanical properties and viability for conversion into biofuel. The effect of moisture content on the Young's modulus of cellulose is of special interest due to its native environment often being in the hydrated secondary plant cell wall and the grinding energy requirements for biomass feedstock preprocessing. The current work investigates the effects of water solvent on the Young's modulus of cellulose calculated from coarse-grained MD mechanical stress simulations. The coarse-grained model was parametrized from atomistic MD calculations of cellulose-cellulose potentials of mean force using umbrella sampling techniques under vacuum and solvated conditions. The Young's moduli of the coarse-grained cellulose assemblies parametrized from cellulose in vacuum or solvated in water were computed via mechanical stress simulations to highlight the importance of capturing solvent interactions for modeling the mechanical behavior of cellulose.
In this work, we introduce a differentiable implementation of the local natural orbital coupled cluster (LNO-CC) method within the automatic differentiation framework of the PySCFAD package. The implementation is comprehensively tuned for enhanced performance, which enables the calculation of first-order static response properties on medium-sized molecular systems using coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)]. We evaluate the accuracy of our method by benchmarking it against the canonical CCSD(T) reference for nuclear gradients, dipole moments, and geometry optimizations. In addition, we demonstrate the possibility of property calculations for chemically interesting systems through the computation of bond orders and Mössbauer spectroscopy parameters for a [NiFe]-hydrogenase active site model, along with the simulation of infrared spectra via ab initio LNO-CC molecular dynamics for a protonated water hexamer.
Relevant research results have suggested that fermentation can increase the content of sorghum amylose chains and their retrogradation value. Therefore, this study explored the effect of fermentation pretreatment on the yield, digestibility, molecular structure, and in vitro fermentation property of sorghum-resistant starch by conducting fermentation pretreatment of sorghum and extracting the resistant starch from fermented sorghum with pressure-heat compound enzyme method. The results were as follows. After fermentation pretreatment, the yield of sorghum-resistant starch increased, the digestibility of sorghum-resistant starch reduced, the laminated structure size on the surface of the particles became more uniform, and the stacking mode became more neat and denser. The sorghum-resistant starch prepared before and after fermentation did not produce new chemical groups, and its functional group peak remained unchanged. After fermentation, the weight-average molecular weight of sorghum-resistant starch was elevated, and the percentage content of high- and low-molecular substances increased and decreased, respectively, compared with that of the unfermented sorghum-resistant starch. The percentage content of short-chain branches in the branched chain increased, whereas that of the long-chain branches decreased; the crystallinity of sorghum-resistant starch after fermentation decreased, and the intensity of X-diffraction peak changed slightly before and after fermentation. According to the results of the in vitro fermentation experiments, the fermentation broth of sorghum-resistant starch had the highest content of butyric acid and short-chain fatty acid. Research results reveal that, after fermentation pretreatment, sorghum-resistant starch presented increased yield, more complex molecular structure, heavier molecular weight and more uniform surface morphology, more efficient butyric acid generation, and greater fermentation rate than unfermented sorghum-resistant starch.
The ability to control the molecular structure (e.g., degree of branching) of iso-olefins produced from oligomerizing light olefins is a valuable tool for tuning the final compositions of hydrocarbon fuels and targeting specific fuel properties. In this study, we demonstrated that the degree of branching of iso-olefins obtained from butene(s) oligomerization can be controlled by tuning process conditions (i.e., temperature, weight hourly space velocity [WHSV], and nature of butene feedstock) and by choosing the proper catalyst (i.e., Amberlyst-36 vs. Y/ZSM-22). In this study, we produced three types of iso-olefin mixtures: 1) a methyl-heptenes rich (74 wt.%) mixture, 2) dimethyl-hexenes rich (80–96 wt.%) mixtures, and 3) highly branched (i.e., more than three methyl substitutions) iso-olefins rich (>50 wt.%) mixtures. While dimethyl-hexenes are preferentially formed at lower temperatures (60–100°C) and WHSV (i.e., 2 hr-1), methyl-heptenes are favorably produced at higher temperatures (>100°C) and WHSVs (i.e., 7 hr-1) over Amberlyst-36. The use of either 1-butene or 2-butene as feedstock resulted in liquid products with similar branching because facile intramolecular isomerization occurs prior to oligomerization. However, the use of isobutylene feedstock forms a significantly more branched olefin product. The formation of highly branched iso-olefins also is favored at higher temperature (140°C) over Amberlyst-36. Using Y/ZSM-22 instead of Amberlyst-36 allowed preferential formation of less-branched methyl-heptenes. For each type of iso-olefins mixture, we determined fuel properties including research octane number (RON), motor octane number (MON), and octane sensitivity (S), which is the difference between RON and MON. We found that not only RON and MON but also S values increased with the degree of branching of these complex mixtures of iso-olefins. The highest RON of 99.7 and S value of 9.3 were obtained for a mixture of highly branched iso-olefins.
Viscoelastic properties of polyvinyl acetates - mechanical loss and molecular weight distribution
Thin films composed of molecular mixtures of metal and dielectric are being considered for use as solar selective coatings for a variety of space power applications. By controlling the degree of molecular mixing, the solar selective coatings can be tailored to have the combined properties of high solar absorptance, , and low infrared emittance, . On orbit, these combined properties would simultaneously maximize the amount of solar energy captured by the coating and minimize the amount of thermal energy radiated. Mini-satellites equipped with solar collectors coated with these cermet coatings may utilize the captured heat energy to power a heat engine to generate electricity, or to power a thermal bus that directs heat to remote regions of the spacecraft. Early work in this area identified the theoretical boundary conditions needed to operate a Carnot cycle in space, including the need for a solar concentrator, a solar selective coating at the heat inlet of the engine, and a radiator.1 A solar concentrator that can concentrate sunlight by a factor of 100 is ideal. At lower values, the temperature of the solar absorbing surface becomes too low for efficient heat engine operation, and at higher values, cavity type heat receivers become attractive. In designing the solar selective coating, the wavelength region yielding high solar absorptance must be separated from the wavelength region yielding low infrared emittance by establishing a sharp transition in optical properties. In particular, a sharp transition in reflectance is desired in the infrared to achieve the desired optical performance. For a heat engine operating at 450 C, a sharp transition at 1.8 micrometers is desired.2 The radiator completes the heat flow through the Carnot cycle.
Future civilian aircraft will require the use of advanced adhesive systems with high temperature capabilities. One such material has been developed at the NASA Langley Research Center, a phenylethynyl-terminated polyimide given the designation LARC(sup TM)-PETI-5. Recent work has shown the advantages of similar phenylethynyl-terminated polyimides as films, moldings, adhesives, and composite matrix resins. Phenylethynyl-terminated oligomers provide greater processing windows than materials which incorporate simple ethynyl endcaps. Since these low molecular weight, low melt viscosity oligomers thermally cure without the evolution of volatile by-products, they provide an excellent means of producing polymers with high glass transition temperatures, excellent solvent resistance, and high mechanical properties. Three different versions of LARC(sup TM)-PETI-5 with theoretical number average molecular weights (M(sub n)s) of 250, 5000, and 10000 g/mol were synthesized in this work. Differential Scanning Calorimetry (DSC) measurements were performed on the dry powder form of these materials to establish cure conditions which result in high glass transition temperatures (T(sub g)s). Lap shear specimens were prepared from adhesive tape made from each material and with the thermal cure conditions determined from the DSC data. The tensile shear data established processing conditions which provided the best adhesive strengths. Further testing was performed to establish the properties of LARC(sup TM)-PETI-5 as an adhesive material and to determine its solvent resistance.
When reducing the size of materials towards the nanoscale, magnetic properties can emerge due to structural variations. Here, we show the reverse effect, where the structure of nanomaterials is controlled by magnetic manipulations. Using the break-junction technique, we find that the interatomic distance in platinum atomic wires is shorter or longer by up to ~20%, when a magnetic field is applied parallel or perpendicular to the wires during their formation, respectively. The magnetic field direction also affects the wire length, where longer (shorter) wires are formed under a parallel (perpendicular) field. Our experimental analysis, supported by calculations, indicates that the direction of the applied magnetic field promotes the formation of suspended atomic wires with a specific magnetization orientation associated with typical orbital characteristics, interatomic distance, and stability. A similar effect is found for various metal and metal-oxide atomic wires, demonstrating that magnetic fields can control the atomistic structure of different nanomaterials when applied during their formation stage.
Polymer electrolytes show promise as alternatives to conventional electrolytes in energy storage and conversion devices but have been limited due to their inverse correlation between ionic conductivity and modulus. In this study, we examine surface morphology, linear viscoelastic, dielectric and diffusive properties of molecular ionic composites (MICs), materials produced through the combination of a rigid and charged double helical polymer, known as poly(2,2’)-disulfonyl-4,4’benzidine terepthalamide (PBDT), with ionic liquids (ILs). To probe temperature extremes, we incorporate a non-crystallizable IL to allow measurements from to -90 to 200°C. As we increase PBDT weight percentage (wt%), shear moduli increase and do not decay up to 200°C while maintaining room temperature ionic conductivity within a factor of two of the neat IL. We connect diffusion coefficients of IL ions with ionic conductivity through the Haven ratio across a wide temperature range and analyze trends in ion transport based on a relatively high and composition-dependent static dielectric constant. This behavior may result from collective rearrangement of IL ions in these networks. We propose that these properties are driven by a two-phase system in MICs corresponding to IL-rich “puddles” and PBDT-IL associated “bundles” where IL ions form alternating sheaths of cations and anions around each PBDT rod. Furthermore, these polymer-based MIC electrolytes show great promise for use in electrochemical devices that require fast ion transport, high modulus, and a broad thermal window.
Supramolecular self-assemblies of hydrophilic macromolecules functionalized with hydrophobic, structure-directing components have long been used for drug delivery. In these systems, loading of poorly soluble compounds is typically achieved through physical encapsulation during or after formation of the supramolecular assembly, resulting in low encapsulation efficiencies and limited control over release kinetics, which are predominately governed by diffusion and carrier degradation. To overcome these limitations, amphiphilic prodrugs that leverage a hydrophobic drug as both the therapeutic and structure-directing component can be used to create supramolecular materials with higher loading and controlled-release kinetics using biodegradable or enzymatically cleavable linkers. Here, we report the design, synthesis, and characterization of a library of supramolecular polymer prodrugs based on poly(ethylene glycol) (PEG) and the proregenerative drug 1,4-dihydrophenonthrolin-4-one-3-carboxylic acid (DPCA). Structure–property relationships were elucidated through experimental characterization of prodrug behavior in both the wet and dry states using scattering techniques and electron microscopy and corroborated by coarse-grained modeling. Molecular architecture and the hydrophobic-to-hydrophilic ratio of PEG–DPCA conjugates strongly influenced their physical state in water, ranging from fully soluble to supramolecular spherical assemblies and nanofibers. Molecular design and supramolecular structure, in turn, were shown to dramatically alter hydrolytic and enzymatic release and cellular transport of DPCA. In addition to potentially expanding therapeutic options for DPCA through control of supramolecular assemblies, the design principles elaborated here may inform the development of other supramolecular prodrugs based on hydrophobic small-molecule compounds.
The objective of this research is to define the methodology for the preparation and characterization of new carbon-based molecular sieves with composite structures. Carbon molecular sieves have found increasing application in the field of separation and purification of gases. These materials are relatively easy to prepare and their surfaces can be modified to some extent. It is expected that by combining inorganic oxides with the carbonaceous structure one can begin to design composite materials with a wider range of possible chemical and physical properties. In this way, the IOM-CMS materials may confer distinct advantages over pure carbon molecular sieves, not just for separation, but also for catalysis. The most recent results in the design and characterization of these IOM-CMS materials are reviewed and summarized. Directions for further research are also presented.
Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. Here, we highlight the application and suitability of different representations, from expert-guided “engineered” descriptors to automatically “learned” features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data. The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used. Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as “hand-crafted” molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure–property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors. Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule’s weakest bond are used in simple physical models of site-selectivity and reactivity.