Polyfunctional macromonomers obtained from 2,2‐bis(hydroxymethyl) propanoic acid and tall oil fatty acids

Macromonomers (MMs) were obtained with different proportions of dimethylolpropionic acid (DMPA) and tall oil fatty acid (TOFA). The MMs were characterized by their acid value (AV), their hydroxyl value (OHV), IR spectroscopy, NMR, electrospray ionization–mass spectrometry, UV spectroscopy, rheological, differential scanning calorimetry, thermogravimetric analysis, gloss, flexibility, and adherence analyses. The presence of hydroxyl and ester groups and double bonds were confirmed by IR and NMR analyses. IR analysis showed that the intensity of the OH groups decreased with the addition of TOFA. In all cases, the AVs of the MMs were lower than those of DMPA and TOFA. By electrospray ionization–mass spectrometry, the formation of the DMPA homopolymers and structures with molecular weights higher than that of DMPA were observed. All of the MMs were amorphous. The viscosity of the MMs decreased with the addition of TOFA. The gloss values of the MMs at 85° were higher than 100, and the flexibility was good. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42029.     

The Role of α1-Adrenoceptor Antagonists in the Treatment of Prostate and Other Cancers

This review evaluates the role of α-adrenoceptor antagonists as a potential treatment of prostate cancer (PCa). Cochrane, Google Scholar and Pubmed were accessed to retrieve sixty-two articles for analysis. In vitro studies demonstrate that doxazosin, prazosin and terazosin (quinazoline α-antagonists) induce apoptosis, decrease cell growth, and proliferation in PC-3, LNCaP and DU-145 cell lines. Similarly, the piperazine based naftopidil induced cell cycle arrest and death in LNCaP-E9 cell lines. In contrast, sulphonamide based tamsulosin did not exhibit these effects. In vivo data was consistent with in vitro findings as the quinazoline based α-antagonists prevented angiogenesis and decreased tumour mass in mice models of PCa. Mechanistically the cytotoxic and antitumor effects of the α-antagonists appear largely independent of α 1-blockade. The proposed targets include: VEGF, EGFR, HER2/Neu, caspase 8/3, topoisomerase 1 and other mitochondrial apoptotic inducing factors. These cytotoxic effects could not be evaluated in human studies as prospective trial data is lacking. However, retrospective studies show a decreased incidence of PCa in males exposed to α-antagonists. As human data evaluating the use of α-antagonists as treatments are lacking; well designed, prospective clinical trials are needed to conclusively demonstrate the anticancer properties of quinazoline based α-antagonists in PCa and other cancers.     

Slope reliability and back analysis of failure with geotechnical parameters estimated using Bayesian inference

A Bayesian approach is proposed for the inference of the geotechnical parameters used in slope design. The methodology involves the construction of posterior probability distributions that combine prior information on the parameter values with typical data from laboratory tests and site investigations used in design. The posterior distributions are often complex, multidimensional functions whose analysis requires the use of Markov chain Monte Carlo (MCMC) methods. These procedures are used to draw representative samples of the parameters investigated, providing information on their best estimate values, variability and correlations. The paper describes the methodology to define the posterior distributions of the input parameters for slope design and the use of these results for evaluation of the reliability of a slope with the first order reliability method (FORM). The reliability analysis corresponds to a forward stability analysis of the slope where the factor of safety (FS) is calculated with a surrogate model from the more likely values of the input parameters. The Bayesian model is also used to update the estimation of the input parameters based on the back analysis of slope failure. In this case, the condition FS = 1 is treated as a data point that is compared with the model prediction of FS. The analysis requires a sufficient number of observations of failure to outbalance the effect of the initial input parameters. The parameters are updated according to their uncertainty, which is determined by the amount of data supporting them. The methodology is illustrated with an example of a rock slope characterised with a Hoek-Brown rock mass strength. The example is used to highlight the advantages of using Bayesian methods for the slope reliability analysis and to show the effects of data support on the results of the updating process from back analysis of failure.     

Waterborne star-shaped styrene-alkyd resins

Waterborne star-shaped styrene-alkyd resins (SSARs) were synthesized from a branched alkyd resin (AR) and styrene (St) by miniemulsion polymerization. SSARs are an environmentally friendly material. The ratio of AR to St for obtaining SSARs was as follows: 50:50 (SSAR1), 60:40 (SSAR2), 70:30 (SSAR3), and 80:20 (SSAR4). The conversion percentage was directly proportional to St used, and was higher than 94.0 %. Infrared analysis and protonic nuclear magnetic resonance revealed the reaction between AR and St. The synthesis process also leads to the formation of polystyrene and its concentration increases with the concentration of St. The values of the reacted double-bond fractions were higher than 17.80%. The SSARs drop size was bigger than the particle size. The miniemulsion colloidal stability was good at room temperature. The SSARs zeta potential was between −55 and −90 mV. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48386.     

MoO2 Diffusion in Aluminosilicate Glass

A diffusion couple of an oxidized molybdenum disk and a glass cylinder was used to measure the solubiiity and effective binary diffusion coefficient of MoO₂ in a non-alkali aluminosilicate glass. At 1400°C, the solubility limit was 8.4 mol%; the value of the diffusion coefficient (4.1 × 10⁻¹⁶ m²/s) was significantly lower than that estimated from the Stokes-Einstein relation.