Thermal Stability of Polymer Additives: Comparison of Decomposition Models Including Oxidative Pyrolysis

The thermo‐oxidative stability of widely used polymer additives has been investigated. A comparative analytical approach with classic and innovative decomposition models for polymer additives was conducted and the results supported using quantum‐chemical calculations. Unique pyrolysis products of the analytes were compiled utilizing pyrolysis online coupled to gas chromatography followed by mass spectrometric detection (Pyr‐GC–MS). The pyrolysis was either performed under inert conditions or in an oxygen‐containing atmosphere. Squalane was applied as polymer‐mimicking liquid next to low density polyethylene (LDPE) and polyamide 6 (PA 6) as matrices for 10 selected additives. The additives included in this study range from antioxidants and plasticizers to processing aids. These were selected to address a range of application in consumer products and to cover different chemical classes. The toxicological relevance of additives and potential breakdown products was considered. Consequently, degradation of sterically hindered antioxidants, diarylamines, and a trimellitic acid derivative was investigated. The findings were used to predict the behavior of consumer products made of polymeric materials entailing additives. The level of Antioxidant 2246 [2‐tert‐butyl‐6‐[(3‐tert‐butyl‐2‐hydroxy‐5‐methylphenyl)methyl]‐4‐methylphenol] and one of its predicted decomposition products was determined in baby bottle nipples made of natural rubber [2‐tert‐butyl‐4‐methylphenol] utilizing the complementary technique of gas chromatography coupled to tandem mass spectrometry (GC–MS/MS). This study provides a comprehensive characterization of important polymer additives and enables the prioritization of degradation products for further risk assessment. J. VINYL ADDIT. TECHNOL., 25:E12–E27, 2019. © 2018 The Authors. Journal of Vinyl and Additive Technology published by Wiley Periodicals, Inc. on behalf of Society of Plastics Engineers     

Cultivation and downstream processing of microalgae and cyanobacteria to generate protein-based technofunctional food ingredients

Abstract

Microalgae are unicellular microorganisms that can be rich in proteins and are therefore a valuable ingredient in different foods. So far microalgae are mainly utilized in foods in low concentrations as a whole-cell ingredient even though it is known that proteins extracted from microalgae can possibly posess various technofunctional properties, such as high protein solubility, emulsification, foaming, and gelation properties. The widespread usage of protein-rich ingredients obtained from microalgae is for the most part prevented by the high price of the biomass, the lack of efficient downstream processes, and the adverse taste. The aim of this review is to give insights into the fundamental properties of the growth and processing of microalgae, highlight the advantages of microalgae ingredients and show potential applications based on the technofunctional, nutritional and sensory properties that were reported. Moreover, the existing challenges and knowledge gaps that hinder the application of microalgal proteins in foods are discussed.     

Privacy-preserving iris authentication using fully homomorphic encryption

Multimedia Tools and Applications - Rapid advancement in technology has led to the use of biometric authentication in every field. In particular, from the past few years, iris recognition systems...     

An optimization method based on the evolutionary and topology approaches to reduce the mass of composite wind turbine blades

In optimization of transient problems, a robust, stable, and efficient numerical scheme for time integration is of much importance. Recently, the mixed Lag     

Calcium phosphates grown on bacterial cellulose template

Bacterial cellulose membranes were employed as templates for calcium phosphates deposition by successive immersion in solutions of Ca(NO₃)₂·4H₂O and (NH₄)₂HPO₄, under ultrasonication. During the wet chemical reaction, mineral phases were loaded on bacterial cellulose fibrils, leading to precursor hybrid composites. These were subjected to a lyophilisation procedure in order to preserve the 3D porous aspect and afterwards to a thermal treatment with the aim of removing the polymeric phase and generating well crystallized structures. Different types of morphologies were achieved by varying the heating rate, as well as the calcination temperature and period. The as-prepared samples and the final ones were investigated from compositional and structural point of view through X-ray diffraction and Fourier-transform infrared spectroscopy and morphologically concerning by scanning electron microscopy. The magnetic properties were also evaluated in order to demonstrate the suitability of the obtained materials for the development of magnetic scaffolds dedicated to hard tissue applications.     

Sol-Gel Preparation and Spectroscopic Properties of Modified Sodium Silicate /Tartrazine Dye Nanocomposite

Silicon - Thin film nanocomposites of sodium silicate (80 SiO2 –20 Na2O)/ tartrazine dye (E102) prepared using sol gel process in acidic system. The change in the physicochemical properties...     

Bibliometric analysis of the evolution of biochar research trends and scientific production

Clean Technologies and Environmental Policy - Biochar has caught great attention over the last decade, yielding a large number of publications in a broad range of disciplines. This scientometric...     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.