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There is a great number of reports with the aim to reduce the inherent brittleness of epoxy resins through their modification with different types of reacting and non-reacting additives, in order to diversify their applications. In this work, the toughening effect of polydimethylsiloxane, diglycidyl ether (PDMS-DGE) as well as this rubber functionalized with 1,2-diaminocyclohexane (PDMS-DGE-DCH) on the bisphenol-A diglycidyl ether/1,2-diaminocyclohexane epoxy resin (DGEBA/1,2-DCH) was characterized by differential scanning calorimetry, DSC, and dynamic mechanical analysis, DMA. The results were correlated with those from energy-dispersive X-ray spectroscopy, EDS X-ray, scanning electronic microscopy, SEM, thermogravimetric analysis, and tension tests. The epoxy resin added with 10 phr (parts per hundred of resin) PDMS-DGE underwent phase separation evidenced by voids of c.a. 0.7 µm left after extraction of PDMS-DGE, while Young's modulus and glass transition temperature, Tg, did not decrease significantly. In contrast, the PDMS-DGE-DCH modifier at 10 phr yielded epoxy resins with two lower partially overlapping Tg over a broader temperature range and increased on both the tensile strength and the tensile strain at the break by 2.8- and 16.8-fold, respectively. No voids were observed on the SEM images after subjected to extraction conditions with acetone, and EDS X-ray elemental mapping revealed Si atoms uniformly distributed on all the fracture-surface, proving that PDMS-DGE-DCH took part in the cure reaction into the network of epoxy resin.

Warner’s randomized response (RR) model is used to collect sensitive information for a broad range of surveys, but it possesses several limitations such as lack of reproducibility, higher costs and it is not feasible for mail questionnaires. To overcome such difficulties, nonrandomized response (NRR) surveys have been proposed. The proposed NRR surveys are limited to simple random sampling with replacement (SRSWR) design. In this paper, NRR procedures are extended to complex survey designs in a unified setup, which is applicable to any sampling design and wider classes of estimators. Existing results for NRR can be derived from the proposed method as special cases.

© 2020 by the authors., The use of probability-based panels that collect data via online or mixed-mode surveys has increased in the last few years as an answer to the growing concern with the quality of the data obtained with traditional survey modes. However, in order to adequately represent the general population, these tools must address the same sources of bias that affect other survey-based designs: namely under coverage and non-response. In this work, we test several approaches to produce calibration estimators that are suitable for survey data affected by non response where auxiliary information exists at both the panel level and the population level. The first approach adjusts the results obtained in the cross-sectional survey to the population totals, while, in the second, the weights are the result of two-step process where different adjusts on the sample, panel, and population are done. A simulation on the properties of these estimators is performed. In light of theory and simulation results, we conclude that weighting by calibration is an effective technique for the treatment of non-response bias when the response mechanism is missing at random. These techniques have also been applied to real data from the survey Andalusian Citizen Preferences for Political Decision-Making Procedures., The work was supported by the Ministerio de Economia, Industria y Competitividad, Spain, under Grant MTM2015-63609-R.

Dual frame surveys are a device to reduce the costs derived from data collection in surveys and improve coverage for the whole target population. Since their introduction, in the 1960’s, dual frame surveys have gained much attention and several estimators have been formulated based on a number of different approaches. In this work, we propose new dual frame estimators based on the population empirical likelihood method originally proposed by Chen and Kim (2014) and using both the dual and the single frame approach. The extension of the proposed methodology to more than two frame surveys is also sketched. The performance of the proposed estimators in terms of relative bias and relative mean squared error is tested through simulation experiments. These experiments indicate that the proposed estimators yield better results than other likelihood-based estimators proposed in the literature., Ministerio de Economía y Competitividad of Spain

This paper considers the problem of estimating a poverty measure, the Head Count Index, using the auxiliary information available, which is incorporated into the estimation procedure by calibration...

The synthesis of the poly(4-vinylpyridine-co-ethylvinylbenzene) resin is investigated and its performance to remove Cr(VI) from aqueous solutions is evaluated as a function of pH using batch and fixed bed column adsorptions. The rate of Cr(VI) removal is observed to increase as the pH solution shifts to acidic conditions due to an enhanced protonation of the 4-vinylpyridine group in the polymer, which favors its electrostatic attraction with Cr(VI) oxyanions. This finding is supported with Density Functional Theory (DFT) calculations, revealing that the interaction between $${\text{CrO}}_{4}^{{2 - }}$$ (predominant species at pH 6) due to a higher charge delocalization arising in the O atoms. Experimental isotherms are approximated with the Langmuir and Radke-Prausnitz adsorption models. This former approach generates the best fitting to the data, whereby it was incorporated into a nonlinear transient model to account for the Cr(VI) adsorption in a fixed bed, and evaluating its capacity to predict experimental adsorption data. The model enables to infer that the resin presents a fast kinetic for Cr(VI) sorption, and the Cr(VI) intra-particle diffusion across the adsorbent pores is the rate-determining step for sorption.

The estimation of a finite population distribution function under a dual frame context is considered when auxiliary population information is available. Several procedures are defined and compared to various methods adapted from the literature. The asymptotic distribution of the proposed estimators is established, a brief simulation is implemented and an application to real data is included.

Summary Surveys usually include questions where individuals must select one in a series of possible options that can be sorted. On the other hand, multiple frame surveys are becoming a widely used method to decrease bias due to undercoverage of the target population. In this work, we propose statistical techniques for handling ordinal data coming from a multiple frame survey using complex sampling designs and auxiliary information. Our aim is to estimate proportions when the variable of interest has ordinal outcomes. Two estimators are constructed following model-assisted generalised regression and model calibration techniques. Theoretical properties are investigated for these estimators. Simulation studies with different sampling procedures are considered to evaluate the performance of the proposed estimators in finite size samples. An application to a real survey on opinions towards immigration is also included.

A nanostructured, highly flexible, and optically transparent epoxy nanocomposite has been formulated reacting diglycidyl ether of bisphenol-A with amine-functionalized silyl-diglycidyl ether-terminated poly(dimethylsiloxane). The concentration of poly(dimethylsiloxane) varied up to 10 wt% and curing was performed at 90°C. Transmission electron microscopy revealed poly(dimethylsiloxane) droplets up to 180 nm in diameter well dispersed in the epoxy. Dynamic mechanical analysis exhibited a decrease in elastic modulus E′ and glass transition temperature Tg, and an increase in the α relaxation strength suggesting increased local molecular motions. Strikingly, poly(dimethylsiloxane) induced a threefold increase in strain at fracture and toughness. Furthermore, hydrophobic behavior was induced by poly(dimethylsiloxane) as the water contact angle increased from 72° (neat epoxy) up to 110° at 5 wt% poly(dimethylsiloxane) content. Fractured surfaces exhibited plastic deformation, contrary to brittleness in ...

Waterborne acrylate/SiO 2 nanocomposites were synthesized by in situ emulsion polymerization using batch process. Polymethyl methacrylate (PMMA), acrylic acid (AA) and butyl acrylate (BA) were copolymerized in the presence of nanosilica particles of 7 nm size. Nanosilica was added up to 3% g/g and the emulsions thus obtained were stable for at least 6 months in storage at room temperature. The results showed that when the concentration of nanosilica was increased, the degree of conversion decreased, and the percent of coagulum slightly increased. Interestingly, in the presence of nanosilica the latex particle size continually decreased as the polymerization reaction progressed until reaching a nearly constant value of ∼150 nm. Conversely, the neat acrylic latex particles exhibited the typical continuous increase of particle size as polymerization time increased. Steady shear rheology showed that the viscosity of the nanostructured emulsions was shear thinning, exhibiting a power-law behavior. As cast coatings were optically transparent and no evidence of aggregation was detected by SEM and AFM, and EDS analysis confirmed the presence of silica throughout the coatings. The nanosilica induced significantly higher thermal stability, as decomposition temperatures, T dec , of the nanostructured acrylics increased by as much as 40 °C relative to the neat acrylic. The glass transition temperature T g was increased up to 10 °C at only 1 wt% SiO 2 nanoparticle concentration, but at higher concentrations it was slightly reduced. As cast films exhibited higher shear modulus and shift of maximum of tan δ denoting a modification of macromolecular dynamics by the SiO 2 nanoparticles. The results are contrasted with those obtained on acrylate-SiO 2 coatings with equal formulation but polymerized by semi-batch process (Romo-Uribe et al., 2016).

Acrylate–SiO2 nanocomposites were synthesized by in situ emulsion semi-batch polymerization in aqueous dispersion utilizing untreated nanoparticles. Butyl acrylate (BA), methyl methacrylate (MMA) and acrylic acid (AA) with composition 56:42:2 were copolymerized at 50% solids in the presence of SiO2 nanoparticles of 7 nm size. SiO2 nanoparticles were added up to 3 wt% and the emulsions thus obtained were stable for at least 6 months in storage at room temperature. The semi-batch polymerization was carried out for 2.5 h. and yielded conversions as high as 92% and not lower than 84%, superior to batch polymerization of the same system. The synthesized emulsion hybrids were characterized by Fourier transform infrared spectroscopy, thermogravimetric analysis, differential scanning calorimetry, scanning and transmission electron microscopy and atomic force microscopy. As cast films about 1 mm thick were optically transparent suggesting good nanoparticle dispersion. However, dispersed SiO2 nano aggregates were detected by SEM, TEM and AFM, and the identity of the nanoclusters was confirmed by EDS and EFTEM. Thus, the morphology of cast films consisted of SiO2 nanoclusters dispersed throughout the acrylate matrix. Strikingly, the untreated SiO2 nanoparticles induced significantly higher thermal decomposition temperatures, Tdec increased by as much as 20 °C relative to the neat acrylate. There was also an increase of glass transition temperature Tg and tensile modulus increased nearly 100% relative to the neat acrylate suggesting modification of macromolecular dynamics by the presence of the SiO2 nanoparticles, and this was confirmed by shear rheometry of the molten coatings where a rubber-like plateau was clearly defined. The results are contrasted for the same system but using batch polymerization.

Randomized response (RR) techniques may be used to compile more reliable data, to protect the respondent’s confidentiality, and to avoid an unacceptable rate of nonresponse when the information requested is sensitive (e.g., concerning racism, drug use, abortion, delinquency, AIDS, or academic cheating). Standard RR methods are used primarily in surveys that require a binary response to a sensitive question, and seek to estimate the proportion of people presenting a given (sensitive) characteristic. Nevertheless, some studies have addressed situations in which the response to a sensitive question results in a quantitative variable. RR methods are usually developed assuming that the sample is obtained using simple random sampling. However, in practice, most surveys are complex and involve stratification, clustering, and an unequal probability of selection of the sample. Data from complex survey designs require special consideration with regard to the estimation of finite population parameters and to the corresponding variance estimation procedures, due to the reality of significant departures from the simple random sampling assumption. In such a complex survey design, unbiased variance estimation is not easy to calculate, because of clustering and the involvement of (generally complex) second-order inclusion probabilities. In view of these considerations, a new computer program has been developed to provide a method for estimating the parameters of sensitive characteristics under a variety of complex sampling designs.