Effect of the particle size distribution on the low shear viscosity of high‐solid‐content latexes

The production of high‐solid‐content, low‐viscosity latexes is an active field in both industry and academia. The viscosity of polymer dispersions has a clear dependence on the particle size distribution (PSD). An example is the rule of thumb that a bimodal PSD enables the reduction of the viscosity with respect to monomodal systems. Despite important progress in theoretical work, not much has been done to quantitatively predict the low shear viscosity of aqueous polymer dispersions as a function of the complex PSD. In this work, the capability of a low‐shear‐viscosity equation to quantitatively account for the influence of both the PSD and the physicochemical characteristics of the dispersions is experimentally assessed. An analysis, consistent with theoretical concepts, of the data with semiempirical correlations is proposed. Next, with values of the parameters of the viscosity equation obtained experimentally, the effect of a latex with a 70% solid content on the low shear viscosity is examined. © 2004 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 42: 3936–3946, 2004     

Vortices in the superconducting transition of the 2+1 Abelian Higgs model

We study the phase transitions to the Higgs phase of the finite temperature 2+1 dimensional Abelian Higgs model in terms of Ginzburg-Landau vortices. We find that vorticity is a good order parameter which allows us to determine the order of the transitions.     

Mössbauer characterization of iron and cobalt dinitrosyl derivatives of 1,1′-bis(diphenylphosphino)ferrocene

The interest in the synthesis and reactivity of organometallic compounds which have two or more different metals in the same molecule has grown in recent years, due to the new chemical properties that they can present. In this work, we report the Mössbauer characterization of dppfFe(CO)₃ and two novel compounds, dppfFe(NO)₂ and [dppfCo(NO)₂]⁺[SbF₆]^–, where dppf =1,1-bis(diphenylphosphino)ferrocene. The complexes were also characterized by IR and³¹P NMR. The Mössbauer parameters of the dppfFe(NO)₂ and dppfFe(CO)₃, show two iron sites in the same proportions while for [dppfCo(NO)₂ ⁺[SbF₆]^– only one site was observed.     

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20–25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.     

Prediction of intracellular storage polymers using quantitative image analysis in enhanced biological phosphorus removal systems

The present study focuses on predicting the concentration of intracellular storage polymers in enhanced biological phosphorus removal (EBPR) systems. For that purpose, quantitative image analysis techniques were developed for determining the intracellular concentrations of PHA (PHB and PHV) with Nile blue and glycogen with aniline blue staining. Partial least squares (PLS) were used to predict the standard analytical values of these polymers by the proposed methodology. Identification of the aerobic and anaerobic stages proved to be crucial for improving the assessment of PHA, PHB and PHV intracellular concentrations. Current Nile blue based methodology can be seen as a feasible starting point for further enhancement. Glycogen detection based on the developed aniline blue staining methodology combined with the image analysis data proved to be a promising technique, toward the elimination of the need for analytical off-line measurements.