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This chapter presents results related to the interaction of fibers. In networks, fibers form crosslinks and develop contacts. The mechanical behavior of the crosslinks between athermal fibers and between thermal filaments is discussed, with emphasis on crosslink failure and strength. Contacts between fibers form either along the fiber length, within bundles of parallel fibers, or at specific sites where fibers cross at an angle. The mechanics of elastic contacts with and without cohesive interactions between fibers is reviewed. Sliding of fibers in contact is often encountered in networks and friction has strong effects on network mechanics. Several results related to friction at elastic contacts are reviewed.
Damage accumulation and rupture of network materials are discussed in this chapter. The chapter is divided into two parts addressing the rupture of networks without and with pre-existing defects. The relation between the network structure and the strength and ductility of materials without pre-existing cracks is defined, and examples from gels, cellulose networks, and nonwovens are provided. The effect of the fiber tortuosity, fiber aspect ratio, fiber preferential alignment, and the variability of fiber and crosslink properties on network strength is discussed. As in other materials, the size effect on strength is important in Network materials and a section is dedicated to statistical aspects of the strength. Failure under multiaxial loading conditions is compared with failure in uniaxial tension. The second part of the chapter presents an analysis of the propagation of cracks in networks. Pronounced notch insensitivity is observed in many materials and numerous examples are presented. Special attention is given to toughness and guidelines are provided to assist the toughening of quasi-brittle Network materials. A subsection is dedicated to the strength, toughness, and fatigue resistance of elastomers and gels.
This chapter presents a comprehensive overview of the mechanical behavior of Network materials, with emphasis on the structure–properties relation. Crosslinked and non-crosslinked Network materials are discussed in separate sections. The behavior of crosslinked networks in tension, shear, compression, and multiaxial loading is described. The effects of fiber tortuosity, fiber alignment, crosslink compliance, network connectivity, and variability of fiber properties on network stiffness and nonlinear behavior are discussed in detail. The size effect on linear and nonlinear material properties is evaluated in relation with network parameters. Three types of nonlinear behavior are identified, corresponding to networks that stiffen or soften continuously during deformation, and networks with an approximately linear response. Numerous examples of each type are presented, including collagen networks, fibrin and actin gels, elastomers, paper, and nonwovens. The response of non-crosslinked athermal networks, such as fiber wads, is studied in compression and tension. The effect of entanglements in athermal networks is analyzed and a parallel drawn with the mechanics of thermoplastics.
In many networks, fibers interact though surface interactions such as cohesion and capillarity, which cause fiber bundling. In adequate conditions, the same process leads to the formation of percolated networks of fiber bundles. These have a specific structure and their mechanical properties are quite different from those of regular networks of fibers and molecular filaments. Separate sections are dedicated to crosslinked and non-crosslinked networks with surface interactions. Surface interactions perturb weakly the structure of crosslinked networks, but have a significant effect on their mechanics. If the network is not crosslinked, surface interactions reorganize the network and define the resulting structure. The properties of networks of fiber bundles embedded in solvents (colloidal suspensions) and in the dry state (buckypaper) are discussed in separate sections.
We have previously shown that the geographic routing’s greedy packet forwarding distance (PFD), in dissimilarity values of its average measures, characterizes a mobile ad hoc network’s (MANET) topology by node size. In this article, we demonstrate a distribution-based analysis of the PFD measures that were generated by two representative greedy algorithms, namely GREEDY and ELLIPSOID. The result shows the potential of the distribution-based dissimilarity learning of the PFD in topology characterizing. Characterizing dynamic MANET topology supports context-aware performance optimization in position-based or geographic packet routing.
The crystal structure of nequinate has been solved and refined using synchrotron X-ray powder diffraction data and optimized using density functional techniques. Nequinate crystallizes in the space group P21/c (#14) with a = 18.35662(20), b = 11.68784(6), c = 9.06122(4) Å, β = 99.3314(5)°, V = 1918.352(13) Å3, and Z = 4. The crystal structure is dominated by the stacking of the approximately planar molecules. N–H⋯O hydrogen bonds link adjacent molecules into chains parallel to the b-axis. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).
Reducing negative attitudes toward older adults is an urgent issue. A previous study has conducted “stereotype embodiment theory”-based interventions (SET interventions) that present participants with the contents of SET and related empirical findings. I focus on the subjective time to become older (the perception of how long people feel it will be before they become old) as a mechanism for the effect of SET interventions. I make the SET intervention group and the control group in which the participants are presented with an irrelevant vignette. The data from 641 participants (M = 31.97 years) were analyzed. Consequently, the SET intervention shortened the subjective time to become older and reduced negative attitudes toward older adults. When considering SET interventions, it would be useful to focus not only on the self-interested motives to avoid age discrimination but also on the subjective time to become older.
Ideal for entry-level and experienced researchers working in materials science and engineering, this unique book introduces a new subfield of materials science and mechanics of materials: network materials. A comprehensive review of their mechanical behaviours allows readers to understand, design, and enhance the performance of these material systems, across a range of materials including cytoskeletons, connective tissue, and thermoset polymers. By introducing simple models, supported by experimental data, the book provides the necessary fundamental knowledge to assist readers to design and develop their own material systems. By presenting each of these previously disparate material systems within a unified theoretical framework, this book provides a consolidated presentation of the mechanics of networks and their interactions, introducing parameters that define the stochastic structure of the network, and discussing their mechanical behaviour. It is an ideal text for those new to this fast-growing field, and for experienced researchers looking to consolidate their knowledge.
The previously unreported crystal structure of (S)-Dapoxetine hydrochloride (DAPHCl), the only active pharmaceutical ingredient specially developed for the treatment of premature ejaculation in men, has been determined from laboratory X-ray powder diffraction data with DASH and refined by the Rietveld method with TOPAS-Academic. The structure was evaluated and optimized by dispersion-corrected DFT calculations. This compound crystallizes in an orthorhombic cell, space group P212121, with unit-cell parameters a= 6.3208(3) Å, b = 10.6681(5) Å, c = 28.1754(10) Å, V = 1899.89(14) Å3, Z = 4. The refinement converged to Rp= 0.0442, Rwp= 0.0577, and GoF = 2.440. The crystal structure is a complex 3D arrangement of DAPHCl moieties held together by hydrogen bonds, π⋯π, and C–H⋯π interactions. The chloride ions form layers parallel to the ab plane and are connected by dapoxetinium moieties through N–H⋯Cl and C–H⋯Cl hydrogen bonds. These layers stack along the c-axis, which are connected by C–H⋯π interactions. Hirshfeld surface analysis and fingerprint plot calculations have been performed.
Sexual propagation of Agave plants is an incipient cultivation method, these plants withstand drought and adverse growing conditions; therefore, research on Agave’s diversity, seed processing, and storage could support its cultivation on marginal lands. The aim of this work was to evaluate seed morphology, germination, and seedling genetic diversity of six seed origins (species × provenance) of Agave plants collected in five provenances from Mexico. Seed longevity was evaluated in two seed origins after a 10-year storage period. Seed morphology and seedling genetic variation results demonstrated intra- and interspecific variation within Agave salmiana and with the other seed origins, respectively. After a 10-year storage period seed germination of two A. salmiana seed origins remained relatively stable, storage conditions, and seed variables of this work can serve as reference parameters for future analyses. To the best authors’ knowledge, this is the first report of Agave’s seed longevity evaluation after a 10-year storage period.
The bootComb R package allows researchers to derive confidence intervals with correct target coverage for arbitrary combinations of arbitrary numbers of independently estimated parameters. Previous versions (<1.1.0) of bootComb used independent bootstrap sampling and required that the parameters themselves are independent—an unrealistic assumption in some real-world applications.
Findings
Using Gaussian copulas to define the dependence between parameters, the bootComb package has been extended to allow for dependent parameters.
Implications
The updated bootComb package can now handle cases of dependent parameters, with users specifying a correlation matrix defining the dependence structure. While in practice it may be difficult to know the exact dependence structure between parameters, bootComb allows running sensitivity analyses to assess the impact of parameter dependence on the resulting confidence interval for the combined parameter.
The form and function of human-made objects has borrowed extensively from the natural world. Living systems can serve as the departure point for biomimicry, or as part of the experience in the case of biophilic design. However, bio-inspired design approaches often focus on an idealised and perfected representation of biology. As we design as we integrate biology into products, infrastructure, our conventional thinking on aesthetics may need to change. Real biological systems are subject to imperfections and may be visceral in ways that are not compatible with common conceptions of taste and beauty.
The crystal structure of altrenogest has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional techniques. Altrenogest crystallizes in space group P212121 (#19) with a = 7.286 916(16), b = 10.580 333(19), c = 22.266 08(7) Å, V = 1716.671(6) Å3, and Z = 4 at 295 K. Thermal expansion between 113 and 295 K is anisotropic. An O–H⋯O hydrogen bond links the molecules into chains along the c-axis. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).
The crystal structure of aminopentamide hydrogen sulfate has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional techniques. Aminopentamide hydrogen sulfate crystallizes in space group P21/c (#14) with a = 17.62255(14), b = 6.35534(4), c = 17.82499(10) Å, β = 96.4005(6)°, V = 1983.906(14) Å3, and Z = 4. The structure consists of layers parallel to the bc-plane with hydrogen sulfate anions at the core and aminopentamide cations on the outside. There is a strong charge-assisted O49–H53⋯O52 hydrogen bond between the hydrogen sulfate anions. This hydrogen bond links the anions in a chain parallel to the b-axis. The cation forms a discrete N–H⋯O hydrogen bond to the anion. The amide group also forms two weaker discrete hydrogen bonds to the anion. The three N–H⋯O hydrogen bonds link the cations and anions into columns parallel to the b-axis. This commercial material from USP contained an unidentified impurity, the powder pattern of which could be indexed on a monoclinic unit cell. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).
Numerical estimators of differential entropy and mutual information can be slow to converge as sample size increases. The offset Kozachenko–Leonenko (KLo) method described here implements an offset version of the Kozachenko–Leonenko estimator that can markedly improve convergence. Its use is illustrated in applications to the comparison of trivariate data from successive scene color images and the comparison of univariate data from stereophonic music tracks. Publicly available code for KLo estimation of both differential entropy and mutual information is provided for R, Python, and MATLAB computing environments at https://github.com/imarinfr/klo.
The crystal structure of anhydrous alfuzosin hydrochloride has been solved and refined using laboratory X-ray powder diffraction data and optimized using density functional theory techniques. Anhydrous alfuzosin hydrochloride crystallizes in space group P-1 with a = 9.3214(16), b = 9.3997(29), c = 12.6172(64) Å, α = 107.993(11), β = 100.386(9), γ = 90.229(6)°, V = 1032.1(10) Å3, and Z = 2 at ambient conditions. Thermal expansion is anisotropic, being 8× larger in the c-direction than in the other two. The crystal structure is characterized by a stack of planar fused rings along the b-axis, and layers of the more-corrugated portion of the molecule parallel to the ab-plane. There are two strong N–H⋯Cl hydrogen bonds, as well as seven C-H⋯Cl hydrogen bonds. The powder patterns have been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).
This paper aims to explore the influence of solvent effects on the crystal habit of venlafaxine hydrochloride using the modified attachment energy (MAE) model by molecular dynamics (MD) simulation. Solvent effects were investigated based on the different morphologies of venlafaxine hydrochloride acquired by simulation and experimental technology from the solvents of isopropanol, dimethyl sulfoxide, and acetonitrile. Firstly, morphologically dominant crystal faces were obtained through the prediction of crystal habit in vacuum by the attachment energy (AE) model. Subsequently, the MAEs were calculated by the MD simulation to modify the crystal shapes in a real solvent environment, and the simulation results were in agreement with the experimental ones. Meanwhile, in order to have a better understanding of the solvent effects, the surface structure was introduced to analyze the solvent adsorption behaviors. The results show that the crystal habits of venlafaxine hydrochloride are affected by the combination of the AE and surface structures. Finally, the flowability of the obtained crystal powders from different solvents was investigated by measurement and analysis of the angle of repose and compressibility. The above results verify that the physical properties are closely related to the morphologies of the crystals.
X-ray powder diffraction data, unit-cell parameters, and space group for the topiroxostat form II, C13H8N6, are reported [a = 7.344(9) Å, b = 12.946(7) Å, c = 12.133(5) Å, β = 96.99(3)°, V = 1145.2(4) Å3, Z = 4, and space group P21/c]. The topiroxostat monohydrate, C13H8N6·H2O, crystallized in a triclinic system and unit-cell parameters are also reported [a = 7.422(9) Å, b = 8.552(1) Å, c = 11.193(5) Å, α = 74.85(1)°, β = 81.17(1)°, γ = 66.29(1)°, V = 627.0(6) Å3, Z = 2, and space group P-1]. In each case, all measured lines were indexed and are consistent with the corresponding space group. The single-crystal data of two solid-state forms of topiroxostat are also reported, respectively [a = 7.346(2) Å, b = 12.955(2) Å, c = 12.130(7) Å, β = 96.91(6)°, V = 1146.1(3) Å3, Z = 4, and space group P21/c] and [a = 7.418(6) Å, b = 8.532(8) Å, c = 11.183(9) Å, α = 74.807(1) °, β = 81.13(1)°, γ = 66.32(1) °, V = 624.7(6) Å3, Z = 2, and space group P-1]. The experimental powder diffraction pattern has been well matched with the simulated pattern derived from the single-crystal data.
Biological manufacturing platforms open exciting opportunities to generate new materials, replace extractive processes, and perform ecosystem services through the deployment of metabolic pathways that are both found in nature and engineered. Further possibilities are generated through inter-kingdom collaborations and consortia-based pathways for manufacturing or biodegradation. In tandem, bioreactor technologies to support biocatalysis or bioconversion through novel immobilisation techniques enable flexibility in deployment. The intersection of physical, chemical and biological parameters within a novel bioreactor design will influence performance and stability in contexts beyond the sterility of the production facility. The nature and scale of new applications may invite unconventional production systems, or even consider in situ manufacturing as a potential way of disrupting centralised manufacturing and distribution processes. We may also consider how new technologies underpinning this approach could help us move beyond linear supply chains towards an embodiment of industrial ecology principles. We invite contributions that go beyond optimisation of a single pathway for product formation under conventional homogeneous conditions. Responses to this question will explicitly challenge how we currently design bioreactors through aspects of spatial distribution, connected systems or facilitating novel metabolic assemblages for multi-functional biosynthetic outputs.