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Basic research in the chemistry of hexameric resorcin[4]arenes and pyrogallol[4]arenes during the last decade is reviewed. Applications of NMR methods to determine solution structures, host guest properties and exchange dynamics are discussed. The scientific issue is the behavior of molecules in small spaces; the challenge is to translate this information to practical applications in, say, catalysis or transport.

1H- and 13C-chemical shifts of the dianion of dibenzo[a,c]naphthacene (1) have unambiguously been assigned by 2D-NMR spectroscopy. They indicate a remarkable charge distribution, as most of the negative charge is localized on the ‘anthracenic’ moiety, while the ‘phenanthrenic’ moiety is almost neutral. Association of 12− with alkali-metal counterions has only a minor effect on the chemical shifts. The charge partitioning in 12−, which is reproduced by ωβ calculations in the frame of the Huckel model, must, thus, be considered as an intrinsic property of the 4nπ-electron system of 12−. It is rationalized in terms of differing energy contents of the constituent anthracenic and phenanthrenic moieties. ESR and ENDOR studies of the radical anion 1 and the radical cation 1 show that the π-charge distribution in 12− is reflected by the π-spin distributions in the two radical ions of the alternant hydrocarbon 1.

The ligands 2,9-bis[(6-methyl-2, 2'-bipyridin-6'-yl)methyleneoxymethylenyl]-1,10-phenanthroline (6), 6' ',6' "-bis[(6-methyl-2, 2'-bipyridin-6'-yl)methyleneoxymethylenyl)]-2' ',2' "-bipyridine (2), 5,5'-bis[(6-methyl-2,2'-bipyridin-6'yl)methyleneoxymethylenyl]-2, 2'-bithiophene (7), and 6,6'-bis[(6-methyl-2, 2'-bipyridin-6'-yl)methyleneoxymethylenyl]-2,2'-biphenyl (8) and their respective homo- and heteroleptic double-stranded copper(I) complexes were prepared and characterized in order to estimate the importance of self-recognition in the self-assembly processes of double-stranded copper complexes. The homoleptic double-stranded copper complexes of 2, 6, 7, and 8 were characterized by NMR, FAB-MS, and electrochemistry. It was found that 6 and 2 each form a single double-stranded helicate having the structure of [(L)(2)Cu(3)](3+) (L = 2 or 6), 7 forms two double-stranded [(7)(2)Cu(3)](3+) complexes, and 8 results in a mixture of at least two [(8)(2)Cu(2)](2+) complexes. The potential shift, DeltaE degrees (,) of the Cu(+)/Cu(2+) redox process of these complexes reflects the binding affinity of the different binding sites to the copper cation. The electrochemical data show that the central units have a higher affinity to Cu(+) as compared to the off-center binding sites. NMR was used to determine the actual complex composition obtained from different mixtures of 2, 6, or 7 with Cu(+). Interestingly, we have found that, although 6, 2, and 7 each form homoleptic double-stranded complexes, no heteroleptic double-stranded copper complexes were formed from the mixtures of 7 with either 6 or 2. However, when mixtures of 6 and 2 are used, helicate distributions seem to follow simple statistics. These results are discussed in terms of the relative importance of self-recognition in the self-assembly of double-stranded helicates.

Quantitative structural property relations (QSPRs) for boiling points of aliphatic hydrocarbons were derived using a back-propagation neural network and a modified Fuzzy ARTMAP architecture. With the back-propagation model, the selected molecular descriptors were capable of distinguishing between diastereomers. The QSPRs were obtained from four valance molecular connectivity indices (1χv,2χv,3χv,4χv), a second-order Kappa shape index (2κ), dipole moment, and molecular weight. The inclusion of dipole moment proved to be particularly useful for distinguishing between cis and trans isomers. A back-propagation 7−4−1 architecture predicted boiling points for the test, validation, and overall data sets of alkanes with average absolute errors of 0.37% (1.65 K), 0.42% (1.73 K), and 0.37% (1.54 K), respectively. The error for the test and overall data sets decreased to 0.19% (0.81 K) and 0.31% (1.30 K), respectively, using the modified Fuzzy ARTMAP network. A back-propagation alkene model, with a 7−10−1 architectu...

A neural network based quantitative structure−property relationship (QSPR) was developed for the vapor pressure−temperature behavior of hydrocarbons based on a data set for 274 compounds. The optimal QSPR model was developed based on a 7-29-1 back-propagation neural network architecture using valance molecular connectivity indices (1χv, 3χv, 4χv), molecular weight, and temperature as input parameters. The average absolute errors in vapor pressure predictions for the test, validation, and overall data sets were 8.2% (0.036 log P units or 23.2 kPA), 9.2% (0.039 log P units or 26.8 kPA), and 10.7% (0.046 log P units or 31.1 kPA), respectively. The performance of the QSPR for temperature-dependent vapor pressure, which was developed from a simple set of molecular descriptors, displayed accuracy of better than or well within the range of other available estimation methods.

Quantitative structure−property relationships (QSPRs) for estimating aqueous solubility of organic compounds at 25 °C were developed based on a fuzzy ARTMAP and a back-propagation neural networks using a heterogeneous set of 515 organic compounds. A set of molecular descriptors, developed from PM3 semiempirical MO-theory and topological descriptors (first-, second-, third-, and fourth-order molecular connectivity indices), were used as input parameters to the neural networks. Quantum chemical input descriptors included average polarizability, dipole moment, resonance energy, exchange energy, electron−nuclear attraction energy, and nuclear−nuclear (core−core) repulsion energy. The fuzzy ARTMAP/QSPR correlated aqueous solubility (S, mol/L) for a range of −11.62 to 4.31 logS with average absolute errors of 0.02 and 0.14 logS units for the overall and validation data sets, respectively. The optimal 11−13−1 back-propagation/QSPR model was less accurate, for the same solubility range, and exhibited larger avera...

Quantitative structure−property relationships (QSPRs) for estimating the logarithm octanol/water partition coefficients, logKow, at 25 °C were developed based on fuzzy ARTMAP and back-propagation neural networks using a heterogeneous set of 442 organic compounds. The set of molecular descriptors were derived from molecular connectivity indices and quantum chemical descriptors calculated from PM3 semiempirical MO-theory. Quantum chemical input descriptors include average polarizability, dipole moments, exchange energy, total electrostatic interaction energy, total two-center energy, and ionization potential. The fuzzy ARTMAP/QSPR performed, for a logKow range of −1.6 to 7.9, with average absolute errors of 0.03 and 0.14 logKow for the overall data and test sets, respectively. The optimal 12−11−1 back-propagation/QSPR model, for the same range of logKow, exhibited larger average absolute errors of 0.23 and 0.27 logKow for the test and validation data sets, respectively, over the same range of logKow values....

Homoditopic building blocks 1, featuring two π-rich cone-like calix[5]arene moieties connected at their narrow rims by a rigid o-, m-, or p-xylyl spacer in a centrosymmetric divergent arrangement, show a remarkable tendency to spontaneously and reversibly self-assemble with the complementary homoditopic α,ω-alkanediyldiammonium dipicrate guest salts C8−C12·2Pic through iterative intermolecular inclusion events, forming supramolecular assemblies whose composition and dynamics strongly depend upon the length of the connector, the geometry of the spacer, as well as the concentration and/or molar ratios between the two components. 1H NMR spectroscopy and ESI-MS studies of 1/Cn·2Pic modular homoditopic pairs support the formation of discrete (bis)-endo-cavity assemblies with the shorter C8 and C9 connectors, and/or (poly)capsular assemblies with the longer C10−C12 components under appropriate concentrations and molar ratios (50 mM equimolar solutions). 1H NMR titration experiments and diffusion NMR studies pro...

Quantitative structure-property relationships (QSPRs) for estimating a dimensionless Henry's Law constant of organic compounds at 25 degrees C were developed based on a fuzzy ARTMAP and back-propagation neural networks using a heterogeneous set of 495 organic compounds. A set of molecular descriptors developed from PM3 semiempirical MO-theory and topological descriptors (second-order molecular connectivity index) were used as input parameters to the neural networks. Quantum chemical input descriptors included average molecular polarizability, dipole moments (total point charge, total hybridization, and total sum), ionization potential, and heat of formation. The fuzzy ARTMAP/QSPR correlated Henry's Law constant for -6.72/= logH/= 2.87 with average absolute errors of 0.03 and 0.13 logH units for the overall data and the test set, respectively. The optimal 7-17-1 back-propagation/QSPR model was less accurate and exhibited larger average absolute errors of 0.28 and 0.27 logH units for the validation (recall) and test sets, respectively. The fuzzy ARTMAP-based QSPR was superior to the back-propagation and multiple linear regression/QSPR models for Henry's Law constant of organic compounds.

NMR studies (proton and carbon) afford an experimental tool for the evaluation of the patterns of charge delocalization and charge distribution in π-conjugated polycyclic dianions. In several cases a partitioning of charge could be observed (as well as a segregation of M.O.s), a situation unprecedented in π-conjugated systems. This charge partitioning is rationalized by the relative paratropicities of the charged and uncharged components

Description de l'application du concept d'alternance de charge a la distribution de charge dans les diions de molecules mono- et polycycliques. Determination de la densite de charge par etude theorique et experimentale (RMN 13 C)