Shape-memory polymers can revert to their original shape when they are reheated. The stress generated by shape recovery is a growing function of the energy absorbed during deformation at a high temperature; thus, high energy to failure is a necessary condition for strong shape-memory materials. We report on the properties of composite nanotube fibers that exhibit this particular feature. We observed that these composites can generate a stress upon shape recovery up to two orders of magnitude greater than that generated by conventional polymers. In addition, the nanoparticles induce a broadening of the glass transition and a temperature memory with a peak of recovery stress at the temperature of their initial deformation.
The range of energies observed is quite narrow at only 6.8-14.4 kJ/mol above that of quartz, and these data are consistent with and extend the earlier findings of Petrovic et al. 1 The enthalpy variations are correlated with the following structural parameters: framework density, nonbonded distance between Si atoms, and framework loop configurations. A strong linear correlation between enthalpy and framework density is observed, implying that it is the overall packing quality that determines the relative enthalpies of zeolite frameworks. The presence of internal silanol groups is shown to result in a slight (e2.4 kJ/mol) destabilization of the calcined molecular sieves by comparing calorimetric data for MFI and BEA samples synthesized in hydroxide (containing internal silanol groups) and fluoride (low internal silanol group density) media.
ABSTRACT:Solvents crucially alter the rates and selectivity of liquid-phase organic reactions, often hindering the development of new synthetic routes or, if chosen wisely, facilitating routes by improving rates and selectivities. To address this challenge, a systematic methodology is proposed that quickly identifies improved reaction solvents by combining quantum mechanical computations of the reaction rate constant in a few solvents with a computer-aided molecular design (CAMD) procedure. The approach allows the identification of a high-performance solvent within a very large set of possible molecules. The validity of our CAMD approach is demonstrated through application to a classical nucleophilic-substitution reaction for the study of solvent effects, the Menschutkin reaction. The results are successfully validated via in-situ kinetic experiments. A space of 1341 solvents is explored in silico, but requiring quantum mechanical calculations of the rate constant in only 9 solvents, and uncovering a solvent that increases the rate constant by 40%.What is the best solvent for a given chemical reaction? Given that the rate and selectivity of chemical reactions can vary by several orders of magnitude in different solvents, 1,2 this question has important ramifications for the exploration of novel reaction routes and the development of industrial processes. 3,4 When investigating new liquid-phase reactions, it is essential to find a solvent that promotes the desired reaction without excessive catalyst deactivation, side-product formation, or solubility limitations. Indeed, a poor solvent choice can result in a missed opportunity to investigate novel chemistry or catalysts. At the process-development level, the problem of solvent choice is 2 compounded by the numerous safety, environmental and process constraints that must be satisfied.Yet, few tools exist to support this decision, especially when it affects reaction kinetics, and researchers are often left to choose on the basis of qualitative chemical knowledge and/or extensive and costly experimental investigations.Advances in the understanding of liquid-phase reactions remain a topic of intense academic interest 5 and practical relevance, as illustrated by identification of the development of solvent selection techniques as a key priority area by the ACS Green Chemistry roundtable. 6 A very promising avenue of research in this direction is the development of Computer-Aided Molecular Design (CAMD) techniques. CAMD offers systematic methodologies, typically in the form of algorithms, to identify chemical species/molecular structures that perform a chosen function best (e.g., maximize the rate of a given reaction). The resulting molecular designs can be used to guide experiments in an otherwise huge space of possibilities. CAMD techniques have been widely applied in the context of solvent design for separations, and have had a significant impact on academic and industrial practice. 7 In a batch extractive distillation for fine chemicals processing, for example, ...
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