UV-Visible Spectrophotometer for Distinguishing Oxidation Time of Engine Oil

Optimization of Melanin Production by Streptomyces antibioticus NRRL B-1701 Using Arthrospira (Spirulina) platensis Residues Hydrolysates as Low-Cost L-tyrosine Supplement

Melanin is a functional pigment that is used in various products. It can be produced by Streptomyces antibioticus NRRL B-1701 when supplemented with L-tyrosine. Arthrospira (Spirulina) platensis is a cyanobacterium with high protein content, including the protein phycocyanin (PC). During PC's extraction, biomass residues are generated, and these residues still contain various amino acids, especially L-tyrosine, which can be used as a low-cost supplement for melanin production. Thus, this study employed a hydrolysate of A. platensis biomass residue for L-tyrosine substitution. The effects of two drying methods, namely, lyophilization and dying via a hot air oven, on the proximate composition and content of L-tyrosine in the biomass residue were evaluated. The highest L-tyrosine (0.268 g L-tyrosine/100 g dried biomass) concentration was obtained from a hot-air-oven-dried biomass residue hydrolysate (HAO-DBRH). The HAO-DBRH was then used as a low-cost L-tyrosine supplement for maximizing melanin production, which was optimized by the response surface methodology (RSM) through central composite design (CCD). Using the RSM-CCD, the maximum level of melanin production achieved was 0.24 g/L, which is approximately four times higher than it was before optimization. This result suggests that A. platensis residue hydrolysate could be an economically feasible and low-cost alternative source of L-tyrosine for the production of melanin.

Reflecting the Quality Degradation of Engine Oil by the Thermal Diffusivity: Radiative and Nonradiative Analyses

Ageing of engine oil is an important issue determining the engine life and performance. The present work attempts to delineate the ageing-induced changes in engine oil through the mode-mismatched dual-beam thermal lens (MMDBTL) technique and other conventional spectroscopic techniques. For the analyses, engine oil samples were collected after every 200 km of runtime. As the thermal diffusivity is related to the nonradiative deexcitation upon optical absorption, comprehensive radiative and nonradiative analyses were carried out. The Ultraviolet-Visible, Fourier transform infrared, and Nuclear magnetic resonance spectroscopic analyses point to the structural modification as a result of the breaking of the long-chain hydrocarbons into ketones, aldehydes, esters, and other compounds. This modifies the absorption pattern, which can also be understood from the nonlinear refractive index study using the Z-scan technique. The compositional variations associated with the degradation upon ageing, the length of the hydrocarbon chain, and the formation of newer molecules account for the enhancement of the thermal diffusivity revealed through the MMBDTL techniques. The complementary nature of the radiative and nonradiative emission is understood from the fluorescence study. Thus, the study reveals the possibility of thermal diffusivity measurement as an effective tool for the quality monitoring of engine oil.

Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants

In this study, a total of 302 molecular structures of phenylnaphthylamine antioxidants based on N-phenyl-1-naphthylamine and N-phenyl-2-naphthylamine skeletons with various substituents were modeled by exhaustive methods. Antioxidant parameters, including the hydrogen dissociation energy, solubility parameter, and binding energy, were calculated through molecular simulations. Then, a group decomposition scheme was determined to decompose 302 antioxidants. The antioxidant parameters and decomposition results constituted machine-learning data sets. Using an artificial neural network model, a correlation coefficient between the predicted and true values above 0.88 and an average relative error within 6% were achieved. Random forest models were used to analyze the factors affecting antioxidant activity from chemical and physical perspectives; the results showed that amino and alkyl groups were conducive to improving antioxidant performance. Moreover, substituent positions 1, 7, and 10 of N-phenyl-1-naphthylamine and 3, 7, and 10 of N-phenyl-2-naphthylamine were found to be the optimal positions for modifications to improve antioxidant activity. Two potentially efficient phenylnaphthylamine antioxidant structures were proposed and their antioxidant parameters were also calculated; the hydrogen dissociation energy and solubility parameter decreased by more than 9% and 7%, respectively, whereas the binding energy increased by more than 16% compared with the benchmark of N-phenyl-1-naphthylamine. These results indicate that molecular simulation and machine learning could provide alternative tools for the molecular design of new antioxidants.

Prediction of the Total Base Number (TBN) of Engine Oil by Means of FTIR Spectroscopy

The objective of this study is to develop a statistical model to accurately estimate the total base number (TBN) value of diesel engine oils on the basis of the Fourier transform infrared spectroscopy (FTIR) analysis. The research sample consisted of oils used in the course of 14,820 km. The samples were collected after each 1000 km and both FTIR and TBN measurements were performed. By applying the measured absorbance values, five statistical models aimed at predicting TBN values were elaborated with the use of the following information: aggregated values of measured absorbance in defined spectral ranges, extremes at wavenumbers, or the surface area of spectral bands related to the vibrations of specific molecular structures. The obtained models may be considered a continuation and an extension of previous studies of this type described in the literature on the subject. The results of the study and the analysis of the obtained data have led to the development of two models with high predictive capabilities (R2 > 0.98, RMSE < 0.5). Another model, which had the smallest number of variables in comparison to other models, had markedly lower R2 value (0.9496) and the highest RMSE (0.5596). Yet another model, where the dimensionality of the pre-processed full spectra was reduced to four aggregates through averaging, turned out to be slightly worse than the best one (R2 = 0.9728). The study contributes to a more in-depth understanding of the FTIR-based TBN prediction tools that may be readily available to all interested parties.