Ethylene spacer-linked bis-acetamidopyridine for dicarboxylic acid recognition and polymeric new wave-like anti-perpendicular arrangement of a host–guest in the solid state

In this paper, 1,2-bis(2-acetamido-6-pyridyl)ethane, receptor 1, having an ethylene spacer is reported to recognise dicarboxylic acids. The binding study in the solution phase is carried out using ¹H NMR (1:1) and UV–vis experiments and in the solid phase by single-crystal X-ray analysis. In ¹H NMR, the downfield shifts of specific amide protons of receptor 1 in 1:1 complexes of receptor and guest diacids, and in the UV–vis experiment, the appearance of an isosbestic point as well as significant binding constants are observed, which thus unambiguously support the complexation of receptor 1 with dicarboxylic acids in solution. Receptor 2, simple 2-acetamido-6-methylpyridine, has lower binding constants than receptor 1 due to cooperative binding of two pyridine amide groups with two acid groups of diacids. In the solid phase, the ditopic receptor 1 shows a grid-like polymeric hydrogen-bonded network that changes to a polymeric wave-like 1:1 anti-perpendicular network instead of the syn–syn polymeric 1:1 (Goswami, S.; Dey, S.; Fun, H.-K.; Anjum, S.; Rahman, A.-U. Tetrahedron Lett. 2005 (a) Goswami, S., Ghosh, K. and Dasgupta, S. 2000. J. Org. Chem., 65: 1907–1914. (b) Goswami, S.; Ghosh, K.; Mukherjee, R. Tetrahedron2001, 57, 4987–4993. (c) Goswami, S.; Ghosh, K.; Halder, M. Tetrahedron Lett.1999, 40, 1735–1738. (d) Goswami, S.; Dey, S.; Fun, H.-K.; Anjum, S.; Rahman, A.-U. Tetrahedron Lett.2005, 46, 7187–7191. (e) Goswami, S.; Jana, S.; Dey, S.; Razak, I.A.; Fun, H.-K. Supramol. Chem.2006, 18, 571–574. (f) Goswami, S.; Jana, S.; Fun, H.-K. Cryst. Eng. Comm.2008, 10, 507–517. (g) Goswami, S.; Jana, S.; Dey, S.; Sen, D.; Fun, H.-K.; Chantrapromma, S. Tetrahedron2008,64, 6426–6433. (h) Goswami, S.; Dey, S.; Jana, S. Tetrahedron2008, 64, 6358–6363 [Google Scholar], 46, 7187–7191), anti–anti polymeric 1:1 (Goswami, S.; Jana, S.; Dey, S.; Razak, I.A.; Fun, H.-K. Supramol. Chem. 2006 (a) Goswami, S., Ghosh, K. and Dasgupta, S. 2000. J. Org. Chem., 65: 1907–1914. (b) Goswami, S.; Ghosh, K.; Mukherjee, R. Tetrahedron2001, 57, 4987–4993. (c) Goswami, S.; Ghosh, K.; Halder, M. Tetrahedron Lett.1999, 40, 1735–1738. (d) Goswami, S.; Dey, S.; Fun, H.-K.; Anjum, S.; Rahman, A.-U. Tetrahedron Lett.2005, 46, 7187–7191. (e) Goswami, S.; Jana, S.; Dey, S.; Razak, I.A.; Fun, H.-K. Supramol. Chem.2006, 18, 571–574. (f) Goswami, S.; Jana, S.; Fun, H.-K. Cryst. Eng. Comm.2008, 10, 507–517. (g) Goswami, S.; Jana, S.; Dey, S.; Sen, D.; Fun, H.-K.; Chantrapromma, S. Tetrahedron2008,64, 6426–6433. (h) Goswami, S.; Dey, S.; Jana, S. Tetrahedron2008, 64, 6358–6363 [Google Scholar], 18, 571–574; Goswami, S.; Jana, S.; Fun, H.-K. Cryst. Eng. Comm. 2008, 10, 507–517; Goswami, S.; Jana, S.; Dey, S.; Sen, D.; Fun, H.-K.; Chantrapromma, S. Tetrahedron 2008, 64, 6426–6433), syn–syn 2:2 (Karle, I.L.; Ranganathan, D.; Haridas, V. J. Am. Chem. Soc. 1997 (a) Garcia-Tellado, F., Goswami, S., Chang, S.K., Geib, S.J. and Hamilton, A.D. 1990. J. Am. Chem. Soc., 112: 7393–7394. (b) Geib, S.J.; Vicent, C.; Fan, E.; Hamilton, A.D. Angew. Chem. Int. Ed. Engl.1993, 32, 119–121. (c) Garcia-Tellado, F.; Geib, S.J.; Goswami, S.; Hamilton, A.D. J. Am. Chem. Soc.1991, 113, 9265–9269. (d) Karle, I.L.; Ranganathan, D.; Haridas, V. J. Am. Chem. Soc.1997, 119, 2777–2783. (e) Moore, G.; Papamicaël, C.; Levacher, V.; Bourguignon, J.; Dupas, G. Tetrahedron2004, 60, 4197–4204. (f) Korendovych, I.V.; Cho, M.; Makhlynets, O.V.; Butler, P.L.; Staples, R.J.; Rybak-Akimova, E.V. J. Org. Chem.2008, 73, 4771–4782. (g) Ghosh, K.; Masanta, G.; Fröhlich, R.; Petsalakis, I.D.; Theodorakopoulos, G. J. Phys. Chem. B2009, 113, 7800–7809 [Google Scholar], 119, 2777–2783) or top–bottom-bound 1:1 (Garcia-Tellado, F.; Goswami, S.; Chang, S.K.; Geib, S.J.; Hamilton, A.D. J. Am. Chem. Soc. 1990 (a) Goswami, S., Ghosh, K. and Dasgupta, S. 2000. J. Org. Chem., 65: 1907–1914. (b) Goswami, S.; Ghosh, K.; Mukherjee, R. Tetrahedron2001, 57, 4987–4993. (c) Goswami, S.; Ghosh, K.; Halder, M. Tetrahedron Lett.1999, 40, 1735–1738. (d) Goswami, S.; Dey, S.; Fun, H.-K.; Anjum, S.; Rahman, A.-U. Tetrahedron Lett.2005, 46, 7187–7191. (e) Goswami, S.; Jana, S.; Dey, S.; Razak, I.A.; Fun, H.-K. Supramol. Chem.2006, 18, 571–574. (f) Goswami, S.; Jana, S.; Fun, H.-K. Cryst. Eng. Comm.2008, 10, 507–517. (g) Goswami, S.; Jana, S.; Dey, S.; Sen, D.; Fun, H.-K.; Chantrapromma, S. Tetrahedron2008,64, 6426–6433. (h) Goswami, S.; Dey, S.; Jana, S. Tetrahedron2008, 64, 6358–6363 [Google Scholar], 112, 7393–7394) co-crystals.

     

QSAR Modeling of Large Heterogeneous Sets of Molecules

Abstract

In aquatic toxicology, QSAR models are generally designed for chemicals presenting the same mode of toxic action. Their proper use provides good simulation results. Problems arise when the mechanism of toxicity of a chemical is not clearly identified. Indeed, in that case, the inappropriate application of a specific QSAR model can lead to a dramatic error in the toxicity estimation. With the advent of powerful computers and easy access to them, and the introduction of soft modeling and artificial intelligence in SAR and QSAR, radically different models, designed from large non-congeneric sets of chemicals have been proposed. Some of these new QSAR models are reviewed and their originality, advantages, and limitations are stressed.     

冬季平原河网水体溶存甲烷和氧化亚氮浓度特征及排放通量

以长江三角洲上海地区和海河流域天津地区水网为研究对象,对冬季河网表层水体溶存甲烷（CH4）和氧化亚氮（N2O）浓度、饱和度及水-气界面排放通量进行了研究.结果表明,冬季我国平原河网水体溶存CH4和N2O的浓度值都很高,呈高度过饱和状态：CH4浓度均值为0.86mol/L（饱和度：758%）,范围在（0.043±0.001）～（25.3±9.32）μmol/L之间;N2O浓度均值为86.8nmol/L（饱和度：488%）,范围在（9.71±0.41）～（691±35.2）nmol/L之间变化.天津排污河水体CH4和N2O浓度显著高于其他河流（均值分别为38.4mol/L和88.9nmol/L）.水体溶存CH4和N2O浓度、饱和度存在很大的地区差异,上海河网的CH4和N2O浓度和饱和度均值高于天津河网.河网水体水-气界面CH4和N2O排放通量变化范围很广,CH4通量在（1.35±0.22）～（665±246）mol/m2h之间,平均值为24.1mol/m2h,N2O通量在（0.19±0.02）～（22.6±5.05）mol/m2h之间,平均值为2.28mol/m2h.相关分析发现,河网水体溶存CH4浓度与DO显著负相关,与NH4＋显著正相关;N2O浓度则与NH4＋和NO3＋NO2显著正相关.河网水-气界面CH4和N2O排放通量均呈现出市区高郊区和农村低的空间分布规律,污染严重的河流已显然成为大气CH4和N2O的潜在排放源.     

Prediction of extraction efficiency in supported liquid membrane with a stagnant acceptor phase by means of artificial neural network

An artificial neural network model of supported liquid membrane extraction process with a stagnant acceptor phase is proposed. Triazine herbicides and phenolic compounds were used as model compounds. The model is able to predict the compound extraction efficiency within the same family based on the octanol–water partition coefficient, water solubility, molecular mass and ionisation constant of the compound. The network uses the back‐propagation algorithm for evaluating the connection strengths representing the correlations between inputs (octanol–water partition coefficients logP, acid dissociation constant pK_a, water solubility and molecular weight) and outputs (extraction efficiency in dihexyl ether and undecane as organic solvents). The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be smaller than ±3%. Moreover, standard statistical methods were applied for exploration of relationships between studied parameters.     

Automatic Electronic Tongue for On-Line Detection and Quantification of Organophosphorus and Carbamate Pesticides Using Enzymatic Screen Printed Biosensors

This work presents an automatic system, based on an electronic tongue, for resolution of mixtures of three pesticides. Inhibition detections were performed during the steady state of biosensors response. Three biosensors were built using two enzymes, electric eel (EE), genetically-modified Drosophila melanogaster (B131), and electric eel co-immobilized with drosophila melanogaster (BH). Calibrations curves for paraoxon, dichorlvos, and carbofuran were performed in the ranges 0.4–50.4 µM, 0.01–1.01 µM, 0.01–0.41 µM with LOD of 3.91 × 10^?8, 6.30 × 10^?11, and 5.84 × 10^?10, respectively. An artificial neural network (ANN) was used to model the combined response of three pesticides. A set of 19 mixtures were prepared in order to train the artificial neural network, the modeling was validated with a set of 6 spiked samples of river water. The error and recovery yields were found in consistent with expected values.     

Formula Optimization of Emulsifiers for Preparation of Multiple Emulsions Based on Artificial Neural Networks

Formulation optimization of emulsifiers for preparing multiple emulsions was performed in respect of stability by using artificial neural network (ANN) technique. Stability of multiple emulsions was expressed by the percentage of reserved emulsion volume of freshly prepared sample after centrifugation. Individual properties of multiple emulsions such as droplet size, δ, viscosity of the primary and the multiple emulsions were also considered. A back‐propagation (BP) network was well trained with experimental data pairs and then used as an interpolating function to estimate the stability of emulsions of different formulations. It is found that using mixtures of Span 80 and Tween 80 with different mass ratio as both lipophilic and hydrophilic emulsifiers, multiple W/O/W emulsions can be prepared and the stability is sensitive to the mixed HLB numbers and concentration of the emulsifiers. By feeding ANN with 39 pairs of experimental data, the ANN is well trained and can predict the influences of several formulation variables to the immediate emulsions stability. The validation examination indicated that the immediate stability of the emulsions predicted by the ANN is in good agreement with measured values. ANN therefore could be a powerful tool for rapid screening emulsifier formulation. However, the long‐term stability of the emulsions is not good, possibly due to the variation of the HLB number of the mixed monolayers by diffusion of emulsifier molecules, but can be greatly improved by using a polymer surfactant Arlacel P135 to replace the lipophilic emulsifier.     

Copper-containing dendromesogens: the influence of the metal on the mesomorphism

The synthesis and liquid crystalline behaviour of the first and second generations of a dendrimeric structure based on poly(propyleneimine)(DAB-dendr(NH₂)_x) are reported. 4-(4-n-Alkoxybenzoyloxy)salicylaldehydes are used as mesogenic moieties attached at the peripheral amino groups of the dendrimers giving rise to dendromesogens with four and eight mesogenic branches. From these dendromesogens, considered as organic ligands, were prepared six metal-containing dendrimers which incorporate two or four copper atoms in their structures. All the dendrimeric ligands and three of the metal-containing dendrimers exhibit liquid crystalline properties which were studied by optical microscopy, DSC, X-ray diffraction and EPR spectroscopy.     

Assessment of mobility of heavy metals in two soil types by use of column leaching experiments and chemometric evaluation of elution curves

The objectives of this study were to evaluate the mobility of heavy metals (HMs) in two types of soils (acidic forest soil and neutral agricultural soil) by leaching with calcium chloride solution in column experiments. The screening properties of neutral agricultural soil towards pollution by heavy metals (Ni, Cu, Zn and Cd) are approximately 10 times higher than those of acid forest soil. The neutral agricultural soil, polluted artificially by one pore volume (PV) of an HMs solution of concentration 200 mg L^?1, can screen the leaching of these metals over several hundreds of years. The higher apparent desorption rate and per cent desorption of HMs (especially Cd) in acid forest soil indicated a higher potential of intensive migration of the metals across the profile and indicated potential risk of Cd pollution for this type of soil. The latest approach of artificial neural networks to describe transport of HMs in soil has been also evaluated. Using a simple three-layer perceptron topology with three hidden neurons, the experimental data could be simulated. The results suggested that the pH of soil is a major factor controlling the retention of the heavy metals in the soils.