Free radical polymerization of ethyl methacrylate and ethyl α-hydroxy methacrylate: A computational approach to the propagation kinetics

The propagation kinetics of ethyl methacrylate (EMA) and ethyl α-hydroxy methacrylate (EHMA) has been subject to a computational study to understand their free radical polymerization (FRP) behavior in bulk and in solution using Density Functional Theory (DFT). The propagation of EHMA is studied in ethanol and toluene to assess the effect of hydrogen-bonding solvents on FRP of monomers with α-hydroxy functionality. Although EMA and EHMA resemble each other in structure, EHMA propagates faster in bulk due to the presence of intermolecular hydrogen-bonds, which tend to facilitate the approach of the propagating species. This falls in contrast with the experimentally observed lower propagation rates of EHMA in ethanol compared to toluene. Calculations show that the 2.28 rate acceleration in toluene is governed by the ratio of the pre-exponential factors, which reflect the entropies of activation, in both media. The polar protic solvent ethanol has a disruptive effect via hydrogen-bonding on the 6-membered ring shape of EHMA monomers thus decreasing the entropy of activation of the reaction. In the case of toluene, there are no special interactions with the hydrophobic solvent, the entropy of activation is higher than in ethanol.     

First RAFT polymerization of captodative 2-acetamidoacrylic acid (AAA) monomer: An experimental and theoretical study

A capto-dative monomer, 2-acetamidoacrylic acid (AAA), was homopolymerized through RAFT polymerization method using 2-(2-cyanopropanyl dithiobenzoate) (CPDB) as a chain transfer agent and AIBN free radical initiator in DMF at 70 °C. DFT calculations were performed in the selection of the CTA for this unique monomer as well as to elucidate the influence of cd-stabilized growing radical on the kinetic parameters in comparison to methacrylic acid (MAA) and N-(prop-1-en-2-yl)acetamide (NPAA), which represent the captive and dative groups of AAA, respectively. K_eq for these three monomers is in the order of AAA < MAA < NPAA. While k_β > k_−add for NPAA and MAA, for AAA k_−add is about four orders of magnitude larger than k_β. This is the major disadvantage in the RAFT process of AAA using CPDB. Yet, poly(AAA) could be achieved with PDI as low as 1.49. Molecular weight of the polymer can be tuned by the monomer/AIBN ratio. First block copolymers of AAA with MAA and MMA using poly(AAA) as a macro-CTA were also synthesized, indicating the presence of active chain ends.     

Controlling the tacticity in the polymerization of N-isopropylacrylamide: A computational study

In this study, the effect of alcohols as solvents on the kinetics and the tacticity of poly(N-Isopropylacrylamide) (PNIPAM) is investigated with a combined static and molecular dynamics set of computational tools. Classical molecular dynamics calculations have been carried out to determine the location of the solvent molecules in the proximity of the monomer and the dimer. A combined implicit/explicit solvent model was used for the evaluation of the kinetics of the dimeric polymer chains. Rate constants are calculated with the B3LYP/6-311 + G(d,p)//B3LYP/6-31 + G(d), BMK/6-311 + G(d,p)//B3LYP/6-31 + G(d), and MPWB1K/6-311 + G(d,p)//B3LYP/6-31 + G(d) methodologies via the standard transition state theory. We show that due to the proximity of the -NH and carbonyl groups on the syndiotactic propagating dimeric and trimeric chains, the alcohol can stabilize the corresponding transition states by forming a bridge between these functionalities and accelerate this path more than its isotactic counterpart. In agreement with experiment, the increase in the syndiotactic PNIPAM and the acceleration of the reaction in the presence of t-BuOH is predicted with all the DFT functionals utilized in this study.     

Identification of Intermittent Electrical and Mechanical Faults in Permanent-Magnet AC Drives Based on Time–Frequency Analysis

The detection of noncatastrophic faults in conjunction with other factors can be used to determine the remaining life of an electric drive. As the frequency and severity of these faults increase, the working life of the drive decreases, leading to eventual failure. In this paper, methods are presented to identify developing electrical and mechanical faults based on both the short-time Fourier transform and wavelet analysis of the field-oriented currents in permanent-magnet ac drives. The different fault types are classified by developing a linear discriminant classifier based on the transform coefficients.     

A centrosymmetric kernel decomposition for time-frequency distribution computation

Time-frequency distributions (TFDs) are bilinear transforms of the signal and, as such, suffer from a high computational complexity. Previous work has shown that one can decompose any TFD in Cohen's class into a weighted sum of spectrograms. This is accomplished by decomposing the kernel of the distribution in terms of an orthogonal set of windows. In this paper, we introduce a mathematical framework for kernel decomposition such that the windows in the decomposition algorithm are not arbitrary and that the resulting decomposition provides a fast algorithm to compute TFDs. Using the centrosymmetric structure of the time-frequency kernels, we introduce a decomposition algorithm such that any TFD associated with a bounded kernel can be written as a weighted sum of cross-spectrograms. The decomposition for several different discrete-time kernels are given, and the performance of the approximation algorithm is illustrated for different types of signals.     

Wavelet feature selection for image classification

Energy distribution over wavelet subbands is a widely used feature for wavelet packet based texture classification. Due to the overcomplete nature of the wavelet packet decomposition, feature selection is usually applied for a better classification accuracy and a compact feature representation. The majority of wavelet feature selection algorithms conduct feature selection based on the evaluation of each subband separately, which implicitly assumes that the wavelet features from different subbands are independent. In this paper, the dependence between features from different subbands is investigated theoretically and simulated for a given image model. Based on the analysis and simulation, a wavelet feature selection algorithm based on statistical dependence is proposed. This algorithm is further improved by combining the dependence between wavelet feature and the evaluation of individual feature component. Experimental results show the effectiveness of the proposed algorithms in incorporating dependence into wavelet feature selection.     

Characterization of event related potentials using information theoretic distance measures

Analysis of event-related potentials (ERPs) using signal processing tools has become extremely widespread in recent years. Nonstationary signal processing tools such as wavelets and time-frequency distributions have proven to be especially effective in characterizing the transient phenomena encountered in event-related potentials. In this paper, we focus on the analysis of event-related potentials collected during a psychological experiment where two groups of subjects, spider phobics and snake phobics, are shown the same set of stimulus: A blank stimulus, a neutral stimulus and a spider stimulus. We introduce a new approach, based on time-frequency distributions, for analyzing the ERPs. The difference in brain activity before and after a stimulus is presented is quantified using distance measures as adapted to the time-frequency plane. Three different distance measures, including a new information theoretic distance measure, are applied on the time-frequency plane to discriminate between the responses of the two groups of subjects. The results illustrate the effectiveness of using distance measures combined with time-frequency distributions in differentiating between the two classes of subjects and the different regions of the brain.