Initiated chemical vapor deposition of pH responsive poly(2-diisopropylamino)ethyl methacrylate thin films

Poly(2-(diisopropylamino)ethyl methacrylate) (PDPAEMA) thin films were deposited on low temperature substrates by initiated chemical vapor deposition (iCVD) method using tertbutyl peroxide as an initiator. Very high deposition rates up to 38 nm/min were observed at low filament temperatures due to the use of the initiator. Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy show the formation of PDPAEMA films with high retention of tertiary amine functionality which is responsible for pH induced changes in the wetting behavior of the surfaces. As-deposited PDPAEMA thin films on flat Si surface showed a reversible switching of water contact angle values between 87° and 28°; after successive treatments of high and low pH water solutions, respectively. Conformal and non-damaging nature of iCVD allowed to functionalize fragile and rough electrospun poly(methyl methacrylate) fiber mat surfaces by PDPAEMA, which creates a surface with a switching behavior between superhydrophobic and approaching superhydrophilic with contact angle values of 155 ± 3°and 22 ± 5°, respectively.     

Assessment of deformation modulus of weak rock masses from pressuremeter tests and seismic surveys

The deformation modulus of intact rock can be determined through standardized laboratory tests for heavily jointed rock masses but this is very difficult, while in situ tests are time-consuming and expensive. In this study, the deformation modulus of selected heavily jointed, sheared and/or blocky, weathered, weak greywacke, andesite and claystone were assessed, based on pressuremeter tests, geo-engineering characterization and seismic surveys. Empirical equations based on GSI and RMR values are proposed to indirectly estimate the deformation modulus of the greywackes. For the andesites, the spacing of the discontinuities is greater than the length of the pressuremeter probe hence the intact rather than rock mass deformation modulus is obtained. The pressuremeter test results from the claystones could not be correlated with the field data; the relationship between the ratio of rock mass modulus to intact rock modulus and RQD appears to give a better estimation of the deformation modulus.      

Strength and Frictional Properties of Popcorn Kernel as Affected by Moisture Content

Strength properties and dynamic coefficient of friction of popcorn kernels were evaluated as a function of moisture content in the range of 7.78–16.72 g/100 g dry solids. In order to determine strength properties of the kernels that are in terms of deformation at rupture point, rupture force, energy absorbed, hardness, and toughness of the popcorn kernels were quasi-statically loaded between two parallel plates. To determine frictional properties, friction tests were performed on aluminum, steel, and fiberglass surfaces. The values related to the strength properties, in general, decreased as the moisture content increased. On the other hand, the dynamic coefficient of friction of the popcorn kernels increased with increasing moisture content.     

Acetylation of Wood with Various Catalysts

Abstract

The purpose of this study was to compare two different acetylation mechanisms using acetic anhydride (AA) or vinyl acetate (VA) modification with using various catalysts. Acetylation of Scots pine wood flour with acetic anhydride could be significantly improved in the presence of potassium acetate, potassium carbonate, and sodium carbonate at 100°C. Sodium carbonate had low effect on VA acetylation, potassium acetate was found to be more effective, and potassium carbonate was better for vinyl acetate modification of wood flour. The two modification methods and the effect of different catalysts on AA or VA modification were characterized by infrared and NMR spectra and analyzed in detail. The acetylation of Scots pine flour with VA and AA showed almost the same WPG values for catalysts when based on long reaction times.     

Construction of consistent neural network empirical physical formulas for detector counts in neutron exit channel selection

Proper selection of neutron exit channels following heavy-ion reactions is important in nuclear structure physics. A knowledge of detector counts versus number of neutron interaction points per event can be useful in this selection. In this paper, we constructed layered feedforward neural networks (LFNNs) consistent empirical physical formulas (EPFs) to estimate the detector counts versus number of neutron interaction points per event. The LFNN-EPFs are of explicit mathematical functional form. Therefore, by various suitable operations of mathematical analysis, these LFNN-EPFs can be used to derivate further physical functions which might be potentially relevant to neutron exit channel selection.     

A hybrid breast cancer detection system via neural network and feature selection based on SBS, SFS and PCA

Two hybrid feature selection methods (SFSP and SBSP) which are composed by combining the sequential forward selection and the sequential backward selection together with the principal component analysis developed by utilizing quadratic discriminant analysis classification algorithmic criteria so as to utilize in the diagnosis of breast cancer fast and effectively are presented in this study. The tenfold cross-validation method has been applied in the algorithm, which is utilized as criteria during the selection of the features. The dimension of the feature space for input has been decreased from 9 to 4 thanks to the selection of these two hybrid features. The Artificial Neural Networks have been used as classifier. The cross-validation method has been preferred also in the phase of this classification as in the case of the selection of the feature in order to increase the reliability of the result. The Wisconsin Breast Cancer Database obtained from the UCI has been utilized so as to determine the correctness of the system suggested. The values of the average correctness of the classification obtained by utilizing a tenfold cross-validation of the two hybrid systems developed earlier are found, respectively, as follows: for SFSP + NN, 97.57 % and for SBSP + NN, 98.57 %. SBSP + NN system has been observed that, among the studies carried out by implementing the cross-validation method for the breast cancer, the result appears to be very promising. The acquired results have revealed that this hybrid system applied by means of reducing dimension is an utilizable system in order to diagnose the diseases faster and more successfully.     

Asphalt concrete stability estimation from non-destructive test methods with artificial neural networks

The core drilling method has often been used to determine the current status of asphalt concretes. However, this method is destructive so causes damage to the asphalt concretes. In addition, this method causes localized points of weakness in the asphalt concretes and is time consuming. In recent years, non-destructive testing methods have been used for pavement thickness estimation, determination of elasticity modulus, and density and moisture measurements. In this study, the above-mentioned non-destructive and destructive tests with data obtained by applying the Marshall stability to the same asphalt concretes were estimated using the artificial neural networks approach.     

Determination of Melatonin in Cow’s Milk by Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS)

The aim of the present study is the development of a rapid and accurate analytical method to determine melatonin in milk. In this method, melatonin extracted from milk with ethyl acetate, after application of reversed phase liquid chromatographic separation melatonin, was detected and quantified with tandem mass spectrometry. Performance of method was tested by conducting method validation study wherein limit of detection, limit of quantification, linearity recovery, and precision parameters were determined. Limit of detection and limit of quantification were calculated as 0.016 and 0.054 μg/kg, respectively. Recovery of the method was checked at two different concentration namely 0.5 and 2.5 μg/kg and determined as 106 and 100%, respectively. Precision of the method was determined as interday and intraday precision. Intraday precision calculated as 3.92 and 2.15% for 0.5 and 2.5 μg/kg spike levels. In this study, we describe an analytical method for determination of melatonin in milk with LC-MS/MS. This method offers an efficient, rapid, and easy sample preparation procedure with high selectivity and good sensitivity for the determination of melatonin in milk.