Laboratory antimicrobial activity of cinnamaldehyde and pomegranate‐based polycaprolactone films

Polycaprolactone (PCL) was incorporated separately with cinnamaldehyde (CNMA), pomegranate methanolic extract (PME), freeze dried arils of pomegranate (FDAP), and seed flour of pomegranate (SF) to form antimicrobial films to be used for active food packaging. PCL–CNMA films completely inactivated growth of the artificially inoculated Escherichia coli and Staphylococcus aureus, whether at 5% or 10% concentrations (wt/wt of polymer), at all studied temperatures (4, 20, and 37 °C). PCL–PME films (10% wt/wt of polymer) delayed the growth of E. coli and S. aureus for up to 7 and 6 days, respectively, at 37 °C. Two‐day delays in the growth of both bacteria were achieved with FDAP and SF films (10% wt/wt of polymer) at 20 °C. The release of CNMA was slower than the release of PME during film processing, as measured by Fourier transform infrared spectroscopy. Thus, CNMA and pomegranate‐based films perhaps should be planned further for use in controlled release food packaging. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45347.     

A data-based framework for fault detection and diagnostics of non-linear systems with partial state measurement

A novel framework based on the use of dynamic neural networks for data-based process monitoring, fault detection and diagnostics of non-linear systems with partial state measurement is presented in this paper. The proposed framework considers the presence of three kinds of states in a generic system model: states that can easily be measured in real time and in-situ, states that are difficult to measure online but can be measured offline to generate training data, and states that cannot be measured at all. The motivation for such a categorization of state variables comes from a wide class of problems in the manufacturing and chemical industries, wherein certain states are not measurable without expensive equipments or offline analysis while some other states may not be accessible at all. The framework makes use of a recurrent neural network for modeling the hidden dynamics of the system from available measurements and uses this model along with a non-linear observer to augment the information provided by the measured variables. The performance of the proposed method is verified on a synthetic problem as well as a benchmark simulation problem.     

Improvement of dissolution behavior of poorly water soluble drugs by biodegradable polymeric submicron carriers containing sparingly methylated β-cyclodextrin

The objective of this study was to develop submicron carriers of two drugs that are practically insoluble in water, i.e. meloxicam and aceclofenac, to improve their dissolution behavior. The phase solubility of the drugs was studied using different concentrations of sparingly methylated β-cyclodextrin, Kleptose^® Crysmeβ (Crysmeb), in the presence and absence of 0.2 % w/v water-soluble chitosan. Drug-loaded submicron particles (SMPs) were prepared using chitosan chlorhydrate and Crysmeb by the ionotropic gelation method. The SMPs were characterized in terms of powder X-ray diffraction, Fourier transforms infrared spectroscopy, size determination, process yield, drug loading, encapsulation efficiency, surface morphology and in vitro release. The drug loading in the SMPs was enhanced in the presence of Crysmeb. The in vitro drug release was found to be enhanced with SMPs prepared using higher concentrations of Crysmeb. These results indicate that SMPs formed from chitosan chlorhydrate and Crysmeb are promising submicron carriers for enhancing the dissolution of meloxicam and aceclofenac.     

Frying of potato chips in a blend of canola oil and palm olein: changes in levels of individual fatty acids and tocols

The changes occurring in the levels of nutritionally relevant oil components were assessed during repeated frying of potato chips in a blend of palm olein and canola oil (1:1 w/w). The blend suffered minimal reductions in omega‐3 and omega‐6 polyunsaturated fatty acids. There was no significant difference between the fatty acid composition of the oil extracted from the product and that of the frying medium, in all three cases. The blend also contained a significant amount of tocols which add a nutritional value to the oil. The concentration of the tocols was satisfactorily retained over the period of oil usage, in contrast to the significant loses observed in the case of the individual oils. The blend also performed well when assessed by changes in total polar compounds, free fatty acids, p‐anisidine value. When fried in used oil, the product oil content increased progressively with oil usage time. This study shows that blended frying oils can combine good stability and nutritional quality.     

Performance studies on solar collector with grooved absorber tube configuration using aqueous ZnO–ethylene glycol nanofluids

Solar flat plate collector (SFPC) is regarded as one of the best renewable energy devices to acquire hot water for domestic usage. Though its theoretical efficiency is projected at higher scale, its conversion efficiency is observed to be low due to various collector losses. Hence, to achieve improved performance of collector different techniques for heat transfer augmentation in circular pipes can be adopted. Among passive and active techniques available, passive is preferred over active due to economics and saving in exergy. Hence, in the present study absorber tube configuration is modified with internal grooves in the collector setup to enhance the rate of heat transfer. Also, fluids dispersed with metal oxide particles provide an increase in thermal conductivity such that ZnO-based aqueous EG mixture nanofluid is used as working medium to analyze the performance of collector setup. Also, the change in heat transfer rate and temperature profile of grooved tubes under forced laminar conditions for different working fluids are compared and reported.     

A Bayesian machine learning method for sensor selection and fusion with application to on-board fault diagnostics

In applications like feature-level sensor fusion, the problem of selecting an optimal number of sensors can lead to reduced maintenance costs and the creation of compact online databases for future use. This problem of sensor selection can be reduced to the problem of selecting an optimal set of groups of features during model selection. This is a more complex problem than the problem of feature selection, which has been recognized as a key aspect of statistical model identification. This work proposes a new algorithm based on the use of a Bayesian framework for the purpose of selecting groups of features during regression and classification. The hierarchical Bayesian formulation introduces grouping for the parameters of a generalized linear model and the model hyper-parameters are estimated using an empirical Bayes procedure. A novel aspect of the algorithm is its ability to simultaneously perform feature selection within groups to reduce over-fitting of the data. Further, the parameters obtained from this algorithm can be used to obtain a rank order among the selected sensors. The performance of the algorithm is first tested on a synthetic regression example. Finally, it is applied to the problem of fault detection in diesel engines (30,000 data records from 43 sensors, 8 classes) and used to compare the misclassification rates with a varying number of sensors.     

Charge transport and ammonia sensing properties of flexible polypyrrole nanosheets grown at air–liquid interface

Flexible polypyrrole nanosheets (thickness ∼150 nm) grown at the air–liquid interface have been investigated for charge transport and NH₃ sensing application. Polypyrrole nanosheets films exhibited a uniform and dense morphology. Temperature dependent charge transport measurements revealed that the PPy films obey Mott's 3-D variable range hopping mechanism. The mobility values calculated using temperature dependent current voltage characteristics indicated them to obey Arrhenius behaviour. These films exhibited a reversible response towards NH₃ at room temperature. The sensor exhibited a sensitivity of ∼12% with a typical response and recovery times of 240 s and 50 min, respectively towards 50 ppm of NH₃. Raman studies indicated that there is an increase in the antisymmetrical C–N stretching upon exposure to higher concentration of NH₃ (500 ppm) and could be assigned to the interaction of NH₃ with the carbon backbone of PPy film. Our results clearly emphasize that these flexible PPy films could be used to realize flexible sensors.     

Thermal and mechanical characterization of poly(methyl methacrylate) nanocomposites filled with TiO2 nanorods

Thick films of nanocomposites made of poly(methyl methacrylate) matrix and colloidal anatase TiO₂ nanorods fillers were prepared by solvent mixing and solution drop casting. Different concentrations of nanorods were tested in order to examine the influence of the nanoscale fillers on the composites material properties and structure. The thermal properties of the samples were investigated through thermogravimetric analysis, which showed an increase in thermal stability of the nanocomposites on increasing nanorods concentration, for the range of concentrations used. The viscoelastic properties were investigated through dynamic mechanical analysis, which showed an increase in both the storage and loss modulus on increasing nanorods concentration. The in-depth distribution of the TiO₂ nanorods in the matrix was evaluated through cross-sectional transmission electron microscopy, which pointed out a uniform dispersion of mesoscale nanorods agglomerates with increasing diameter of 100–200 nm range on increasing nanorods concentration.     

Improving injection efficiency of gate modulation ROIC interface for HgCdTe IR photodiodes

Microsystem Technologies - A new interface circuit for readout of HgCdTe Infrared photodiodes is proposed that can be used for efficient transfer of photocurrent from the photodiodes to the...     

Renewable resource modified polyol derived aliphatic hyperbranched polyurethane as a biodegradable and UV‐resistant smart material

Renewable resource tailored tough, elastomeric, biodegradable, smart aliphatic hyperbranched polyurethanes were synthesized using castor oil modified polyol containing fatty amide triol, glycerol, diethanolamine and monoglyceride of sunflower oil via an A_x + B_y (x , y ≥ 2) approach. To the best of our knowledge, this is the first report of the synthesis of solely aliphatic hyperbranched polyurethanes by employing renewable resources. The synthesized polyurethanes were characterized by Fourier transform infrared, NMR and XRD techniques. The hyperbranched polyurethanes exhibited good mechanical properties, especially elongation at break (668%), toughness (32.16 MJ m^?3) and impact resistance (19.02 kJ m^?1); also high thermal stability (above 300 °C) and good chemical resistance. Also, the hyperbranched polyurethanes were found to show adequate biodegradability and significant UV light resistance. Moreover, they demonstrated excellent multi‐stimuli‐driven shape recovery ability (up to 97%) under direct sunlight (10⁵ lux), thermal energy (50 °C) and microwave irradiation (450 W). The performance of the hyperbranched polyurethanes was compared with renewable resource based and synthetic linear polyurethane to judge the superiority of the hyperbranched architecture. Therefore, these new aliphatic macromolecules hold significant promise as smart materials for advanced applications. © 2017 Society of Chemical Industry