Ionic liquids as recycling solvents for the synthesis of magnetic nanoparticles.

This work describes an easy synthesis (one pot) of MFe(2)O(4) (M = Co, Fe, Mn, and Ni) magnetic nanoparticles MNPs by the thermal decomposition of Fe(Acac)(3)/M(Acac)(2) by using BMI·NTf(2) (1-n-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide) or BMI·PF(6) (1-n-butyl-3-methylimidazolium hexafluorophosphate) ionic liquids (ILs) as recycling solvents and oleylamine as the reducing and surface modifier agent. The effects of reaction temperature and reaction time on the features of the magnetic nanomaterials (size and magnetic properties) were investigated. The growth of the MNPs is easily controlled in the IL by adjusting the reaction temperature and time, as inferred from Fe(3)O(4) MNPs obtained at 150 °C, 200 °C and 250 °C with mean diameters of 8, 10 and 15 nm, respectively. However, the thermal decomposition of Fe(Acac)(3) performed in a conventional high boiling point solvent (diphenyl ether, bp 259 °C), under a similar Fe to oleylamine molar ratio used in the IL synthesis, does not follow the same growth mechanism and rendered only smaller NPs of 5 nm mean diameter. All MNPs are covered by at least one monolayer of oleylamine making them readily dispersible in non-polar solvents. Besides the influence on the nanoparticles growth, which is important for the preparation of highly crystalline MNPs, the IL was easily recycled and has been used in at least 20 successive syntheses.

Determination of the content of fatty acid methyl esters (FAME) in biodiesel samples obtained by esterification using 1H-NMR spectroscopy.

Three different calibration curves based on (1)H-NMR spectroscopy (300 MHz) were used for quantifying the reaction yield during biodiesel synthesis by esterification of fatty acids mixtures and methanol. For this purpose, the integrated intensities of the hydrogens of the ester methoxy group (3.67 ppm) were correlated with the areas related to the various protons of the alkyl chain (olefinic hydrogens: 5.30-5.46 ppm; aliphatic: 2.67-2.78 ppm, 2.30 ppm, 1.96-2.12 ppm, 1.56-1.68 ppm, 1.22-1.42 ppm, 0.98 ppm, and 0.84-0.92 ppm). The first curve was obtained using the peaks relating the olefinic hydrogens, a second with the parafinic protons and the third curve using the integrated intensities of all the hydrogens. A total of 35 samples were examined: 25 samples to build the three different calibration curves and ten samples to serve as external validation samples. The results showed no statistical differences among the three methods, and all presented prediction errors less than 2.45% with a co-efficient of variation (CV) of 4.66%.

Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy.

In this work it has been shown that the routine ASTM methods (ASTM 4052, ASTM D 445, ASTM D 4737, ASTM D 93, and ASTM D 86) recommended by the ANP (the Brazilian National Agency for Petroleum, Natural Gas and Biofuels) to determine the quality of diesel/biodiesel blends are not suitable to prevent the adulteration of B2 or B5 blends with vegetable oils. Considering the previous and actual problems with fuel adulterations in Brazil, we have investigated the application of vibrational spectroscopy (Fourier transform (FT) near infrared spectrometry and FT-Raman) to identify adulterations of B2 and B5 blends with vegetable oils. Partial least square regression (PLS), principal component regression (PCR), and artificial neural network (ANN) calibration models were designed and their relative performances were evaluated by external validation using the F-test. The PCR, PLS, and ANN calibration models based on the Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy were designed using 120 samples. Other 62 samples were used in the validation and external validation, for a total of 182 samples. The results have shown that among the designed calibration models, the ANN/FT-Raman presented the best accuracy (0.028%, w/w) for samples used in the external validation.