Cleaving Direct‐Laser‐Written Microstructures on Demand

Using an advanced functional photoresist we introduce direct‐laser‐written (DLW) 3D microstructures capable of complete degradation on demand. The networks consist exclusively of reversible bonds, formed by irradiation of a phenacyl sulfide linker, giving disulfide bonds in a radical‐free step‐growth polymerization via a reactive thioaldehyde. The bond formation was verified in solution by ESI‐MS. To induce cleavage, dithiothreitol causes a thiol–disulfide exchange, erasing the written structure. The mild cleavage of the disulfide network is highly orthogonal to other, for example, acrylate‐based DLW structures. To emphasize this aspect, DLW structures were prepared incorporating reversible structural elements into a non‐reversible acrylate‐based standard scaffold, confirming subsequent selective cleavage. The high lateral resolution achievable was verified by the preparation of well‐defined line gratings with line separations of down to 300 nm.     

Joint impact of nodes-mobility and channel estimation error on the performance of two-way relay systems

This paper presents the performance analysis of a two-way relay system with nodes-mobility (NM) and channel estimation errors (CEE) under time-varying Rayleigh fading. Herein, analog network coding protocol is employed at the relay node. All the participating nodes are half-duplex and equipped with single-antenna devices. Specifically, we utilize first-order Markovian model to characterize the impact of NM in the channel. Along with the NM, we also consider that the CEE may exist in practical scenarios, where nodes require to estimate the channel state information. By incorporating these factors, we first derive the instantaneous end-to-end (e2e) signal-to-noise ratios (SNRs) at two source nodes. Then, we obtain the exact closed-form expressions for distribution and density functions of the two e2e SNRs. After that, we derive the accurate expressions of various performance metrics, viz., sum-bit error rate, overall outage probability, and ergodic sum-rate. Further, to attain more insights into the considered system, we deduce the asymptotic behavior of these performance metrics. Finally, numerical and Monte-Carlo simulation results are provided to validate our theoretical analysis and to illustrate the impact of NM and CEE on the performance measures of the considered system.     

阿尔茨海默病诊断的自适应BP神经网络模型

阿尔茨海默病(AD)和轻度认知功能损伤(MCI)具有患者多、诊断难的特点,改进BP神经网络,提出自适应BP神经网络(ABP)进行100次AD和MCI诊断模拟,ABP神经网络的诊断正确率显著高于BP和RBF神经网络.采用留一法将101例正常人、200例MCI和90例AD患者的样本分为训练集和检测集,用ABP神经网络对其进行诊断模拟,总正确率达到73.91%.     

Mechanical,Thermal and Morphological Behaviors of Castor Oil Based PU/PS Interpenetrating Polymer Network-Metakaolin Composites

The meta kaolin (MK) clay particulate filler with different weight ratios viz., 0, 5, 10, 20 and 30 wt% were incorporated into castable polyurethane (PU)/polystyrene (PS) (90/10) interpenetrating polymer network (IPN). The effects of MK particulate filler loading on the mechanical and thermal properties of PU/PS (90/10) IPN composites have been studied. From the tensile behavior, it was noticed that a significant improvement in tensile strength and tensile modulus as an increase in MK filler content. Thermogravimetric analysis (TGA) data reveals the marginal improvement in thermal stability after incorporation of MK filler. TGA studies of the IPN composites have been performed in order to establish the thermal stability and their mode of thermal degradation. It was found that degradation of all composites takes place in two steps. Degradation kinetic parameters were obtained for the composites using three mathematical models. Tensile fractured composite specimens were used to analyze the morphology of the composites by scanning electron microscopic (SEM) technique.     

The Copolymerization of Alkenes with Maleic Anhydride

The investigations presented deal with the experimental results of the copolymerization of maleic anhydride (MAn) with alkenes. The course of the reaction is explained by the overall rate of the copolymerization (v _Br), which correlates with the solution viscosity of the copolymer, and the dependence of the v _Br maximum on the mole ratio of the monomers at constant total monomer concentration. The use of solvents with increasing donor power leads to increased complexing of the free MAn molecules and of the MAn radical chain ends. The results demonstrate that, for low 1-alkenes, the addition of the MAn chain radical is the rate-determining step of the copolymerization. As the substituents on the olefinic double bond become larger or the double bond shifts to the 1,2-position, the addition of MAn to the hydrocarbon radical becomes more and more the rate-determining step. On the other hand, an increase of the CT complexation of the MAn polymer radical by use of donor solvents decreases the alkene addition rate.     

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.