Methyl Methacrylate–Ethyleneglycol Dimethacrylate–Acrylic Acid Surface as Stationary Phase for Stir Bar Sorptive Extraction

A novel poly(methyl methacrylate–ethyleneglycol dimethacrylate–acrylic acid) film has been introduced for coating on stir bars to sorptive extraction (SBSE). The effects of different contents of porogen, monomer, cross-linker, modifier and initiator during the solvent or bulk polymerization step on the mechanical property and solvent resistant of prepared sorptive stir bars were investigated. The evaluation of results were caused to obtain the four prepared sorptive stir bars with good mechanical stability and excellent resistance to organic solvents. The extraction efficiency of these prepared sorptive stir bars were investigated by liquid desorption-liquid chromatography-ultraviolet detection (LD-LC-UV) using N-Nitrosodiphenylamine as target analyte. SBSE assays showed the sorptive stir bar that prepared using solution polymerization method had the better recovery for N-Nitrosodiphenylamine in water samples. This sorptive stir bar showed good linearity and acceptable recoveries, as well as advantages such as sensitivity, simplicity, low cost and high feasibility. Based on atomic force microscopy (AFM) results, the average pore size of optimum prepared stir bar using solvent polymerization method was obtained ~9 nm. The thermal gravity (TG) and differential scanning calorimetric (DSC) results showed this polyacrylate film has high thermal stability.     

Improved equation of state for ionic liquids using surface tension

The major objective of this work was the development of a reliable model to describe volumetric properties of ionic liquids (ILs). In this regard, we have applied the Ihm–Song–Mason equation of state (EOS) to some phosphonium- and imidazolium-based ILs. Three temperature-dependent parameters in the equation of state have been scaled based on the surface tension and the liquid density at room temperature. In order to improve the predictive power of the mentioned EOS for ILs, we have proposed using a simple modification. We have taken 1,228 experimental points to show the reliability of the improved EOS. The comparison of predicted densities with literature data over a broad range of temperature, 293–472 K, and pressures up to 200 MPa led to encouraging results. The average absolute deviation of calculated densities from literature values was found to be 0.75%.     

Rapid production of monodisperse cross-linked red blood corpuscle-like particles via scalable one-pot dispersion polymerization

Cross-linked polystyrene (PS) particles having red blood corpuscle (RBC)-like shape were synthesized by one-pot dispersion polymerization of styrene with ethanol/water mixture and ethylene glycol dimethacrylate (EGDMA) as the reaction medium and cross-linker, respectively. Monitoring of the reaction showed that RBC-like shape forms due to asymmetric shrinkage of a cross-linked network during the phase separation. In addition, three dimensional phase diagram was generated based on the yielded data that showed that the formation of such unique shape extremely depends on the polarity of the medium and injection time of the cross-linker. In situ synthesis of RBC-like particles, as promising biomaterials in targeted drug delivery and a model for the understanding of the cell behavior, via such fast and high solid content approach makes it to be conducive to subsequent scale up, i.e. potential commercial adoption.     

On nonparametric classification with missing covariates

General procedures are proposed for nonparametric classification in the presence of missing covariates. Both kernel-based imputation as well as Horvitz-Thompson-type inverse weighting approaches are employed to handle the presence of missing covariates. In the case of imputation, it is a certain regression function which is being imputed (and not the missing values). Using the theory of empirical processes, the performance of the resulting classifiers is assessed by obtaining exponential bounds on the deviations of their conditional errors from that of the Bayes classifier. These bounds, in conjunction with the Borel-Cantelli lemma, immediately provide various strong consistency results.