Search

The present work deals with the chemical properties of vegetable oils commonly used for deep‐frying, for example, sunflower, corn, cotton, canola, and olive oils. Ten deep‐frying cycles were carried out for 20 min each. Oil degradation was measured by peroxide values (PVs) and was highly correlated with the presence of conjugated‐diene and conjugated‐triene structures. Frying cycles caused two alternative reactions, which both impacted the PV. In one reaction, there was an increase in PV with the oxidation of the unsaturated bonds, and in the second stage, there was a decrease in the cleavage of the double‐bond oxide. Additionally, the point of returns (PRs) and activation energy (Ea) were determined for the two stages. The PRs were eight cycles for olive and corn, five cycles for canola, four cycles for sunflower, and three cycles for cotton oil. In terms of ER, the olive and cotton oils had the highest ER and were most resistant to decomposition, and the associated activation energy formation of peroxide decomposition products. The corn oil was the least resistant to decomposition. In addition, this work demonstrated a novel analytical technique that showed the correlation between the total value of conjugated diene‐triene structures and PV, using a titration method. The correlation was high confidence for the regression, recovery, and U95. This significant correlation is useful for measuring the PV of oils subjected to other processes. [ABSTRACT FROM AUTHOR]

Graphite demand is increasing rapidly due to the popularity of electric vehicles (EVs) and mobile devices. Lithium-ion batteries (LIBs) are the power source of EVs and mobile devices and the anodes of LIBs are mostly made of graphite, which is currently produced in an unsustainable and costly manner. For the first time, battery-grade biographite is produced from a renewable and sustainable precursor, biomass-derived pyrolysis oil, using iron as the graphitizing catalyst. Considering battery-grade graphite production at scale, pyrolysis oil offers several advantages as feedstock over biomass in terms of higher carbon density, low inorganics, easy handling, and low transportation cost. Catalytic graphitization of pyrolysis oil can be induced at low temperature (∼1100 °C). Herein, catalytic graphitization using iron powder was carried out under relatively moderate conditions (1500 °C) to reduce costs and environmental impact as compared to incumbent technologies. After graphitization, the residual catalyst was removed by refluxing the biographite + iron solid mixture with hydrochloric acid. The efficiency of iron removal was studied using the X-ray fluorescence (XRF) technique. The structural and morphological properties of biographites were assessed via X-ray diffraction (XRD), Raman spectroscopy, and high-resolution transmission electron microscopy (HR-TEM). The in situ XRD experiments provided a mechanistic understanding of the iron-catalyzed graphitization process. In addition, the effect of pyrolysis oil aging on the biographite crystalline structure was elucidated. The optimal biographite sample demonstrated impressive electrochemical performance with a reversible capacity of 350 mA h g−1, an initial coulombic efficiency of 90.2%, and minimal capacity loss over 100 cycles when applied as an LIB anode, thereby making pyrolysis oil-derived biographite potentially competitive with commercially available battery-grade graphite. [ABSTRACT FROM AUTHOR]

Olive oil is a very valuable product with its unique flavor, taste, aroma and bioactive components. It is an oil obtained by squeezing the olive with its pit and can be consumed in its natural form without any chemical treatment. It is well valuable thanks to its features such as helping healthy nutrition, contributing to economic and agricultural areas and being environmentally friendly. It is one of the most adulterated products today. So oil quality and content have to be determined properly. The aim of this study is to examine the machine learning of chemometrically fried oils in virgin olive oil and eight times used olive oil compared using Fourier Transform Infrared spectroscopy. Deep-Frying Oils (DFO) was carried out 8 times for 20 minutes. Because of chemical quality of oils, Cis, Trans, Ester, Methyl, Carbonyl, peroxide, unsaturated peroxide and ether groups were used in These results were evaluated by classification and regression using machine learning methods. For these evaluations, firstly classification and regression were made using all properties of these index. In classification models, Linear Discriminant Analysis, Naïve Bayesian and Ensemble Tree were used. The evaluation was carried out in two stages. In the first stage, half of the dataset was used for training and the other half for testing. In the second stage, all data was used for training and testing using cross validation method. The success results obtained using the all data set was 100% with naïve Bayesian method. According to chemometric strategy, differences between virgin olive oils and DFO were found by high accuracy in this study. This phenomenon also could be possible for other oil types and degrees of purity. Results illustrated that the method is very suitable and exact for detection deteriotion of olive oil. [ABSTRACT FROM AUTHOR]