The use of ionic liquids (ILs) as demulsifiers of water-in-crude oil emulsions represents a new field of study. The main purpose of this work is to investigate the effect of five ILs, [C 4 and a set of operation parameters on the demulsification process, including the heating type (conventional and microwave), IL concentration (0.74−8.9 μmol/g), effect of alkyl chain length, and effect of cation and anion type on demulsification efficiency. The results indicated that the demulsification was favored when more hydrophobic ILs and longer cation alkyl chains were employed, such as [C 12 mim] + [NTf 2 ] − , reaching values close to 92% of water removal. Moreover, the joint use of microwaves and hydrophobic ILs allowed us to maximize the demulsification efficiency.

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Thermochemical treatments are traditionally employed to perform the separation between the oil and water phases in the petroleum industry. The chemical agents have the function of reducing the barrier rigidity formed by natural surfactants present in the emulsion, thus favoring destabilization. The main objective of this study was to analyze the viscoelastic properties of samples composed of water and a crude oil with the addition of distinct ionic liquids. The methodology used to obtain the interfacial properties was the pedant drop technique. The results showed that the addition of the ionic liquids induced a reduction in the interfacial elasticity and an increase in the compressibility of interfacial films. It was also observed that an enhancement in the alkyl chain length has a positive effect on changing the interfacial properties. The ionic liquid with the highest alkyl chain length investigated ([C 12 min] + [NTf 2 ] − ) showed the ability to produce the more elastic films for the crude oil investigated.

The present work proposed a low-cost portable device as an enabling technology for agriculture using multispectral imaging and machine learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed a good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences.