Viscosity index (VI) and shear stability index (SSI) are standard methods used in the lubricant industry to determine temperature-viscosity dependency and resistance to product degradation, respectively. A variety of ...Viscosity index (VI) and shear stability index (SSI) are standard methods used in the lubricant industry to determine temperature-viscosity dependency and resistance to product degradation, respectively. A variety of oil-soluble polymers, including poly(alkyl methacrylates) (PAMAs) are routinely used to control these properties in fully-formulated liquid lubricants. In this report, we use reversible addition-fragmentation chain transfer (RAFT) polymerization to precisely target identical degrees of polymerization in a family of PAMAs with varying lauryl, hexyl, butyl, ethyl, and methyl groups. Then, expanding on previous methodology reported in the literature, we establish structure property relationships for these PAMAs, specifically looking at how intrinsic viscosity [η] and Martin interaction parameters K<sub>M</sub> relate to VI and SSI characteristics. While the intrinsic viscosity [η] is associated with the volume of macromolecules at infinite dilution, the parameter K<sub>M</sub> reflects the hydrodynamic interactions of polymer chains at actual polymer concentrations in lubricating oils. In this paper, we show that the dependence of VI on the non-dimensional concentration c/c* (or c[η]) can be presented in a form of master curve with shift factors proportional to K<sub>M</sub> that decreases with increasing size of alkyl groups. This finding implies that even in the dilute regime, the coil-expansion theory used to explain the effect of macromolecules on VI should be complemented with the idea of hydrodynamic interactions between polymer molecules that can be controlled by the choice of alkyl chains in the family of PAMAs.展开更多
During storage,olive oil may suffer degradation leading to an inferior quality level when purchased and consumed.Oxidative stability is one of the most important parameters for maintaining the quality of olive oil,whi...During storage,olive oil may suffer degradation leading to an inferior quality level when purchased and consumed.Oxidative stability is one of the most important parameters for maintaining the quality of olive oil,which affects its acceptability and market value.The current methods of predicting the oxidative stability of edible oils are costly and time-consuming.The aim of the present research is to demonstrate the use of dielectric spectroscopy integrated with computer vision for determining the oxidative stability index(OSI)of olive oil.The most effective features were selected from the extracted dielectric and visual features for each olive oil sample.Three machine learning techniques were employed to process the raw data to develop an oxidative stability prediction algorithm,including artificial neural network(ANN),support vector machine(SVM)and multiple linear regression(MLR).The predictive models showed a great agreement with the results obtained by the Rancimat instrument that was used as a reference method.The best result for modelling the oxidative stability of olive oil was obtained using SVM technique with the R-value of 0.979.It can be concluded that this new approach may be utilized as a perfect replacement for quicker and cheaper assessment of olive oil oxidation.展开更多
文摘Viscosity index (VI) and shear stability index (SSI) are standard methods used in the lubricant industry to determine temperature-viscosity dependency and resistance to product degradation, respectively. A variety of oil-soluble polymers, including poly(alkyl methacrylates) (PAMAs) are routinely used to control these properties in fully-formulated liquid lubricants. In this report, we use reversible addition-fragmentation chain transfer (RAFT) polymerization to precisely target identical degrees of polymerization in a family of PAMAs with varying lauryl, hexyl, butyl, ethyl, and methyl groups. Then, expanding on previous methodology reported in the literature, we establish structure property relationships for these PAMAs, specifically looking at how intrinsic viscosity [η] and Martin interaction parameters K<sub>M</sub> relate to VI and SSI characteristics. While the intrinsic viscosity [η] is associated with the volume of macromolecules at infinite dilution, the parameter K<sub>M</sub> reflects the hydrodynamic interactions of polymer chains at actual polymer concentrations in lubricating oils. In this paper, we show that the dependence of VI on the non-dimensional concentration c/c* (or c[η]) can be presented in a form of master curve with shift factors proportional to K<sub>M</sub> that decreases with increasing size of alkyl groups. This finding implies that even in the dilute regime, the coil-expansion theory used to explain the effect of macromolecules on VI should be complemented with the idea of hydrodynamic interactions between polymer molecules that can be controlled by the choice of alkyl chains in the family of PAMAs.
文摘During storage,olive oil may suffer degradation leading to an inferior quality level when purchased and consumed.Oxidative stability is one of the most important parameters for maintaining the quality of olive oil,which affects its acceptability and market value.The current methods of predicting the oxidative stability of edible oils are costly and time-consuming.The aim of the present research is to demonstrate the use of dielectric spectroscopy integrated with computer vision for determining the oxidative stability index(OSI)of olive oil.The most effective features were selected from the extracted dielectric and visual features for each olive oil sample.Three machine learning techniques were employed to process the raw data to develop an oxidative stability prediction algorithm,including artificial neural network(ANN),support vector machine(SVM)and multiple linear regression(MLR).The predictive models showed a great agreement with the results obtained by the Rancimat instrument that was used as a reference method.The best result for modelling the oxidative stability of olive oil was obtained using SVM technique with the R-value of 0.979.It can be concluded that this new approach may be utilized as a perfect replacement for quicker and cheaper assessment of olive oil oxidation.
