Lewis acidic mesoporous Fe-TUD-1 as catalysts for synthesis of Hantzsch 1,4-dihydropyridine derivatives

Lewis acidic mesoporous material (TUD-1) containing Fe was investigated as stable and reusable catalyst for the synthesis of Hantzsch 1,4-dihydropyridine (DHP) derivatives. Yields between 76 and 91 % of DHP derivatives were obtained in shorter reaction times of 3 h depending on the nature of substituents in benzaldehyde.     

Influence of polyhydric solvents on the catalytic &; adsorption properties of self-oriented mesoporous SBA-15 silica

Mesoporous materials (2–50 nm) have generated much interest due to its multi-faceted applications as catalysts, adsorbents and drug delivery systems. This study is the first of its kind to systematically investigate the effect of polyhydric solvents on the morphology, catalytic and adsorption properties of self-oriented mesoporous silica. Three different mesoporous SBA-15 silica materials were synthesized using Water (SW), Glycerol (SG) and Ethylene Glycol (SEG) as solvent. They were characterized using FE-SEM, HR-TEM, small angle XRD, FT-IR, BET and solid state NMR. Morphological studies such as pore characteristics, surface area and the functionalization were carried out by comparing their catalytic and adsorption properties. Each mesoporous sample was used to catalyze biodegradable aliphatic polyester synthesis namely poly (butylene succinate), poly (butylene pimelate) and poly (butylene sebacate) and compared with a conventional homogeneous catalyst SnCl₂·2H₂O. The results offered higher purity and yield of polyesters and they took the order as SW > SG > SEG. The adsorption efficiency of each mesoporous sample was compared using the fluorescent dye Rhodamine-B and it took a reverse order (SEG > SG > SW) to that of the catalytic efficiency. This difference may be attributed to the difference in free active sites, ordered morphology of pores and surface area. The synthesized polyesters were characterized using FT-IR, ¹H NMR, XRD, GPC, DSC and the adsorption studies, using UV–Visible spectrophotometer.     

The Prediction of Yarn Tensile Properties by Using Artificial Neural Networks

This paper presents an artificial-neural-net model for predicting yarn tensile properties. A single hidden-layer neural network trained by using the back-propagation algorithm performs a functional mapping between material properties, process variables, and the resulting yarn tensile properties. The material and process variables, namely, yarn count, blend, and front- and back-nozzle pressures on an air-jet spinning machine, arc correlated with the experimentally determined yarn properties: breaking load and breaking elongation. The neural net was trained and then used to predict the tensile properties of yarns. The errors of prediction were low despite the availability of only a relatively small data set for training, and in each ease the prediction error was less than the standard deviation of experimentation. Use of the cross-validation technique ensured that the neural net obtained a generalized mapping of the inputs and outputs.     

Comparative performance of anaerobic reactors for treatment of sago industry wastewater

For a country like India where energy continues to be precious, with oil prices continuing to rise unlike in the West, anaerobic digestion has far greater relevance than it has to many other regions of the world. The cassava starch production in our country is mainly concentrated in small to medium scale factories, which generates 30,000–40,000 l of effluent per ton of sago produced. The effluent is acidic and highly organic in nature having chemical oxygen demand (COD) of 5,000–7,000 mg l^?1 during the season and 1,000–5,000 during the off-season. These effluents pose a serious threat to the environment and quality of life in the rural area. Since the treatment of cassava starch factory effluents through the normal biogas plants with 30–55 days retention period is very costly, attempts have been made to treat them through high-rate hybrid reactor with several hours of retention period. In Random-Packed Anaerobic Filter, the maximum COD reduction was observed (84.4 %) at 10 h hydraulic retention time (HRT). At 4 h HRT only 46.3 % COD was removed. Even though higher COD removal was achieved at 20 h, the better HRT was at 10 h as the difference between the 20 and 10 h HRT in only 0.2 %. In Up-flow Anaerobic Sludge Blanket reactor, the maximum COD removal (90 %) and total solid (TS) removal (82 %) were observed in a HRT of 30 h, whereas low COD (67 %) and TSs (64 %) removal was observed at 5 h HRT. The treatment of sago industry effluent in a hybrid reactor was studied and the HRT employed was 10, 24, 32, and 40 h. The COD removal rates were 86, 93, 94, and 95 %, and the TSs removal was 79, 85, 86, and 89 %. When the results of all these three reactors were compared, the hybrid reactor seems to be better with an optimum HRT range of 10–20 h. Hence, the anaerobic digestion has proved to be an effective method of treating the sago industry wastewater with simultaneous production of energy in the form of methane.     

Unique photoelectrochemical behavior of TiO2 nanorods wrapped with novel titanium Oxy-Nitride (TiOxNy) nanoparticles

In this work, we developed novel titanium oxynitride (TiO_xN_y) nanoparticles with diameter of 25 ± 2 nm and crystalline size of ～15 nm on hydrothermally grown one-dimensional (1D) TiO₂ nanorod (TNR) arrays. Herein, the TiO_xN_y nanoparticles were synthesized by facile nitridation using TiO₂ powder at 100% NH₃ gas atmosphere. Titanium oxynitride composed of potentially energetic metal-nitrogen bonds (TiN), compared to the weaker TiO bond, becomes chemically stable in the alkaline environment, and is considered as a suitable material for photoelectrochemical (PEC) system. The PEC performance of TiO_xN_y decorated TNR (abbreviated as TiO_xN_y @TNR) films was evaluated in 0.1 M KOH solution under solar illumination condition, and achieved the potentially high photocurrent density (J) of 2.1 mA/cm² at 1.23 V versus reversible hydrogen electrode (RHE) (abbreviated as V_RHE) in the TiO_xN_y@TNR arrays, in comparison with the poor photoresponse (0.7 mA/cm² at 1.23 V_RHE) of the pristine TNR arrays. A nearly three-fold enhancement was attained in the TiO_xN_y decorated TNR arrays, attributed to the high visible light absorption and fast carrier separation, due to the hybridization with the visible active TiO_xN_y nanoparticles in the cascading band alignment between the TiO_xN_y and TNR materials. Furthermore, the introduction of TiO_xN_y layer on the TNR surface quite reduces the interfacial resistance in the solid-liquid interface region, and further, the TiO_xN_y layer contributes to the passivation of the surface states (e.g., defect, trap sites etc.) where the charge recombination reaction frequently happens, leading to the improvement of PEC performance.     

Properties of lignocellulose tamarind fruit fibers

Untreated and alkali‐treated fibers from tamarind fruits were analyzed with Fourier transform infrared, chemical, X‐ray, and thermogravimetric methods. The morphology of the fibers before and after the alkali treatment was studied with scanning electron microscopy. The tensile properties of these fibers before and after the alkali treatment were also studied. The Fourier transform infrared and chemical analyses indicated lowering of the hemicellulose content by the alkali treatment of the fibers. The tensile modulus increased with the alkali treatment. X‐ray diffraction revealed an increase in the crystallinity of the fibers with the alkali treatment. The thermal stability of the fibers increased slightly with the alkali treatment. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Functionalized chitosan with super paramagnetic hybrid nanocarrier for targeted drug delivery of curcumin

Recently, hydrophobically functionalized polymers have been deployed as carriers to improve the encapsulation of hydrophobic drugs. The metal nanocomposites are extensively used to improve the biocompatibility of the formulation and target the drug to the specialized site. In our current study, naphthalene acetate (NAA) was incorporated into the amine group of chitosan to form a hydrophobically functionalized chitosan–NAA drug delivery carrier. The calcium ferrite nanoparticles (CFNP) were embedded in the chitosan–NAA matrix to form a super paramagnetic hybrid nanocarrier for controlled curcumin drug delivery. Various analytical techniques were performed to ensure the functional group modifications, thermal stability, surface nature and morphological behavior of synthesized hybrid carriers. The maximum encapsulation efficiency of 93.6% was obtained under the optimized conditions of drug to chitosan–NAA at 0.1, CFNP to chitosan–NAA at 0.75 and TPP to chitosan–NAA at 1.0 (w/w) ratios, respectively, by adapting Taguchi method. Drug release studies were conducted to determine the effect of pH, drug loading concentrations and magnetic field responses. The drug release data were fitted to various kinetic release models to understand the drug release mechanism. The biocompatibility of the hybrid material was tested using L929 mouse fibroblast cells. The cytotoxicity test against breast cancer cells (MCF-7) was also performed to study the anticancer property of the hybrid paramagnetic material. The prepared curcumin-loaded chitosan–NAA/CFNP was very active against cancer cells in comparison to the normal cells. The results confirmed the applicability of the hybrid nanocarriers in cancer cell-targeted drug delivery.     

Cluster-based context-aware route service management for smart intelligent autonomous vehicles with industrial transport system

Industrial transport system refers to the movement of goods, raw materials, and finished products within and between industrial facilities such as factories, warehouses, and distribution centers. It includes a variety of modes of transport such as trucks, forklifts, conveyor belts, and automated guided vehicles (AGVs). Context-aware route service management refers to system that can dynamically manage and optimize the routes taken by vehicles in transportation system based on contextual information. Our proposed framework for industrial transport systems and smart intelligent autonomous vehicles utilizes cluster-based context-aware routing (CCAR), which includes several significant contributions. Firstly, modified elephant herding optimization (MEHO) algorithm is used to efficiently group autonomous vehicles for routing. Secondly, an improved gravitational search (IGS) algorithm is used for cluster head (CH) selection responsible for transferring context information between vehicles (V2V). Thirdly, a deep hybrid multi-graph neural network (DHMG-NN) is designed for optimal neighboring roadside unit (RSU) node selection through different design constraints to ensure data dissemination between vehicles and infrastructure (V2I). Finally, we validate the effectiveness of our CCAR framework using various simulation scenarios. Our framework achieves a reduction of up to 78% in computation time and an improvement of up to 25% in customer satisfaction ratio and 15% higher than the overall performance ratio compared to the existing frameworks.