Studies in crystal engineering: Crystal packing, topological photodimerization and structure-reactivity correlations in fluoro-substituted styrylcoumarins

In continuation of our studies on the influence of fluoro substitution on the solid state photobehaviour and packing pattern of styrylcoumarins, the results obtained for 4-(3-fluorostyryl)coumarin 1, 4-styryl-6-fluorocoumarin 2 and 4-styryl-7-fluorocoumarin 3 are presented. The configuration of the dimers was established on the basis of crystal packing of 1 and 2 (alpha-packed). A rationale for the significantly lower dimer yield in the crystal for 2 is proposed. In the observed centrosymmetric arrangement of the reactants the C=O ...pi (phenyl) contacts seem to provide additional attractive interactions. C-H ... O and C-H ... F hydrogen bonding seems to provide stability in these structures.

Studies in crystal engineering: Steering ability of fluorine in styrylcoumarins

In continuation of our studies on crystal engineering using fluorine as a steering group, the photobehaviour of di and tri fluoro 4-styrylcoumarins has been examined. It is found that out of the five derivatives, four crystallize into P-packing mode producing syn-HH photodimer upon irradiation whereas the parent hydrocarbon produces an anti K-T dimer. The packing features of the photolabile crystals of 4-(4-fluorostyryl)-6-fluorocoumarin (1), 4-(2,6-difluorostyryl) 6-fluorocoumarin (2) and the photodimer (3a) of 4-(2,6-fluorostyryl)-7-fluorocoumarin (3) have been determined by single crystal X-ray diffraction studies. The stereochemistry of the photodimer of 4-(2-fluorostyryl)-6-fluorocoumarin (4) is deduced based on preliminary X-ray crystallographic data. However, 4-(2,6-difluorostyryl) coumarin (5) is photoinert. The remarkable steering ability of fluorine is established with the molecular packing in the crystal lattice leading to the formation of syn H-H dimer in the above four examples. (C) 1999 Elsevier Science Ltd. All rights reserved.

Poly(lactic-co-glycolic acid): Carbon nanofiber composites for myocardial tissue engineering applications

The objective of the present in vitro research was to investigate cardiac tissue cell functions (specifically cardiomyocytes and neurons) on poly(lactic-co-glycolic acid) (PLGA) (50:50 wt.%)-carbon nanofiber (CNF) composites to ascertain their potential for myocardial tissue engineering applications. CNF were added to biodegradable PLGA to increase the conductivity and cytocompatibility of pure PLGA. For this reason, different PLGA:CNF ratios (100:0, 75:25, 50:50,25:75, and 0:100 wt.%) were used and the conductivity as well as cytocompatibility of cardiomyocytes and neurons were assessed. Scanning electron microscopy, X-ray diffraction and Raman spectroscopy analysis characterized the microstructure, chemistry, and crystallinity of the materials of interest to this study. The results show that PLGA:CNF materials are conductive and that the conductivity increases as greater amounts of CNF are added to PLGA, from OS m(-1) for pure PLGA (100:0 wt.%) to 5.5 x 10(-3) S m(-1) for pure CNF (0:100 wt.%). The results also indicate that cardiomyocyte density increases with greater amounts of CNF in PLGA (up to 25:75 wt.% PLGA:CNF) for up to 5 days. For neurons a similar trend to cardiomyocytes was observed, indicating that these conductive materials promoted the adhesion and proliferation of two cell types important for myocardial tissue engineering applications. This study thus provides, for the first time, an alternative conductive scaffold using nanotechnology which should be further explored for cardiovascular applications. (C) 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Special Issue: Engineering applications of representations of function, Part 1

In engineering design, the end goal is the creation of an artifact, product, system, or process that fulfills some functional requirements at some desired level of performance. As such, knowledge of functionality is essential in a wide variety of tasks in engineering activities, including modeling, generation, modification, visualization, explanation, evaluation, diagnosis, and repair of these artifacts and processes. A formal representation of functionality is essential for supporting any of these activities on computers. The goal of Parts 1 and 2 of this Special Issue is to bring together the state of knowledge of representing functionality in engineering applications from both the engineering and the artificial intelligence (AI) research communities.

An Empirical Study of Synthesis of Multiple State Devices by Engineering Designers

Automated synthesis of mechanical designs is an important step towards the development of an intelligent CAD system. Research into methods for supporting conceptual design using automated synthesis has attracted much attention in the past decades. The research work presented here is based on an empirical study of the process of synthesis of multiple state mechanical devices. As a background to the work, the paper explores concepts of what mechanical device, state, single state and multiple state are, and in the context of the above observational studies, attempts to identify the outstanding issues for supporting multiple state synthesis of mechanical devices.

Shape and size mimicry in the design of ternary molecular solids: towards a robust strategy for crystal engineering

2- and 5-methylresorcinol form co-crystals with 4,4'-bipyridine in which some of the bipyridine molecules are loosely bound. These molecules can be replaced with other molecules of a similar shape and size to give a general method for the engineering of a ternary co-crystal.

Phenylboronic acids in crystal engineering: Utility of the energetically unfavorable syn,syn-conformation in co-crystal design

Phenylboronic acids can exist, in principle, in three different conformers (syn,syn; syn,anti and anti,anti) with distinct energy profiles. In their native state, these compounds prefer the energetically favored syn, anti-conformation. In molecular complexes, however, the functionality exhibits conformational diversity. In this paper we report a series of co-crystals, with N-donor compounds, prepared by a design strategy involving the synthons based on the syn, syn-conformation of the boronic acid functionality. For this purpose, we employed compounds with the 1,2-diazo fragment (alprazolam, 1H-tetrazole, acetazolamide and benzotriazole), 1,10-phenanthroline and 2,2'-bipyridine for the co-crystallization experiments. However, our study shows that the mere presence of the 1,2-diazo fragment in the coformer does not guarantee the successful formation of co-crystals with a syn, syn-conformation of the boronic acid. [GRAPHICS] The -B(OH)(2) fragment makes unsymmetrical O-H center dot center dot center dot N heterosynthons with alprazolam (ALP) and 1,10-phenanthroline (PHEN). In the co-crystals of phenylboronic acids with 1H-tetrazole (TETR) and 2,2'-bipyridine (BPY), the symmetrical boronic acid dimer is the major synthon. In the BPY complex, boronic acid forms linear chains and the pyridine compound interacts with the lateral OH of boronic acid dimers that acts as a connector, thus forming a ladder structure. In the TETR complex, each heterocycle interacts with three boronic acids. While two boronic acids interact using the phenolic group, the third molecule generates O-H center dot center dot center dot N hydrogen bonds using the extra OH group, of -B(OH)(2) fragment, left after the dimer formation. Thus, although molecules were selected retrosynthetically with the 1,2-diazo fragment or with nearby hetero-atoms to induce co-crystal formation using the syn,syn-orientation of the -B(OH)(2) functionality, co-crystal formation is in fact selective and is probably driven by energy factors. Acetazolamide (ACET) contains self-complementary functional groups and hence creates stable homosynthons. Phenylboronic acids being weak competitors fail to perturb the homosynthons and hence the components crystallize separately. Therefore, besides the availability of possible hydrogen bond acceptors in the required position and orientation, the ability of the phenyl-boronic acid to perturb the existing interactions is also a prerequisite to form co-crystals. This is illustrated in the table below. In the case of ALP, PHEN and BPY, the native structures are stabilized by weak interactions and may be influenced by the boronic acid fragment. Thus phenylboronic acids can attain co-crystals with those compounds, wherein the cyclic O-H center dot center dot center dot N hydrogen bonds are stronger than the individual homo-interactions. This can lower the lattice energy of the molecular complex as compared with the individual crystals. [GRAPHICS] Phenylboronic acids show some selectivity in the formation of co-crystals with N-heterocycles. The differences in solubility of the components fall short to provide a possible reason for the selective formation of co-crystals only with certain compounds. These compounds, being weak acids, do not follow the Delta pK(a) analysis and hence fail to provide any conclusive observation. Theoretical results show that of the three conformers possible, the syn,anti conformer is the most stable. The relative stabilities of the three conformers syn,anti,syn,syn and anti,anti are 0.0, 2.18 and 3.14 kcal/mol, respectively. The theoretical calculations corroborate the fact that only energetically favorable synthons can induce the formation of heterosynthons, as in ALP and PHEN complexes. From a theoretical and structural analysis it is seen that phenylboronic acids will form interactions with those molecules wherein the heterocyclic and acidic fragments can interrupt the homosynthons. However, the energy profile is shallow and can be perturbed easily by the presence of competing functional groups (such as OH and COOH) in the vicinity. [GRAPHICS] .

Error, Confidence, and Cost in Engineering Computations

The questions that one should answer in engineering computations - deterministic, probabilistic/randomized, as well as heuristic - are (i) how good the computed results/outputs are and (ii) how much the cost in terms of amount of computation and the amount of storage utilized in getting the outputs is. The absolutely errorfree quantities as well as the completely errorless computations done in a natural process can never be captured by any means that we have at our disposal. While the computations including the input real quantities in nature/natural processes are exact, all the computations that we do using a digital computer or are carried out in an embedded form are never exact. The input data for such computations are also never exact because any measuring instrument has inherent error of a fixed order associated with it and this error, as a matter of hypothesis and not as a matter of assumption, is not less than 0.005 per cent. Here by error we imply relative error bounds. The fact that exact error is never known under any circumstances and any context implies that the term error is nothing but error-bounds. Further, in engineering computations, it is the relative error or, equivalently, the relative error-bounds (and not the absolute error) which is supremely important in providing us the information regarding the quality of the results/outputs. Another important fact is that inconsistency and/or near-consistency in nature, i.e., in problems created from nature is completely nonexistent while in our modelling of the natural problems we may introduce inconsistency or near-inconsistency due to human error or due to inherent non-removable error associated with any measuring device or due to assumptions introduced to make the problem solvable or more easily solvable in practice. Thus if we discover any inconsistency or possibly any near-inconsistency in a mathematical model, it is certainly due to any or all of the three foregoing factors. We do, however, go ahead to solve such inconsistent/near-consistent problems and do get results that could be useful in real-world situations. The talk considers several deterministic, probabilistic, and heuristic algorithms in numerical optimisation, other numerical and statistical computations, and in PAC (probably approximately correct) learning models. It highlights the quality of the results/outputs through specifying relative error-bounds along with the associated confidence level, and the cost, viz., amount of computations and that of storage through complexity. It points out the limitation in error-free computations (wherever possible, i.e., where the number of arithmetic operations is finite and is known a priori) as well as in the usage of interval arithmetic. Further, the interdependence among the error, the confidence, and the cost is discussed.