Steric hindrance restricts the number of asphaltenes in a nanoaggregate and a further addition of asphaltenes to the system will lead to a change in aggregate number, as opposed to size 10 , In other words, nanoaggregation kinetics are controlled by asphaltene poor solubility 49 and strong asphaltene-asphaltene interactions 10 ; in an aqueous environment micellarisation is controlled by solvent-surfactant interactions. Interfacial tension IFT is one of the most widespread measurements in analysing surfactant emulsion-stabilisation properties Obtaining a plot of surface tension versus the logarithm of surfactant concentration will illustrate a decreasing trend and the CMC.
Noteworthy, interfacial tension measurements require that the activity coefficient a s in the Gibbs adsorption equation estimating interfacial tension is constant, which is true for many aqueous systems It may be of use to understand the challenges in applying IFT to asphaltene systems. Firstly, asphaltene activity coefficient is reported to have contradictory properties, whereby some studies report an approximation of unity using Scatchard-Hildebrand solubility theory with a Flory-Huggins term 50 , and other suggest that it is not constant The surface tension of toluene is two and a half times lower than that of water, and loading high-energy asphaltenes onto toluene surface may increase surface tension On the other hand, in Langmuir-Blodgett 26 and pendant droplet 43 , 44 experiments, asphaltenes were shown to decrease surface tension.
Studies by Rane, et al.
Interestingly, the 50— ppm asphaltene concentration is consistent with our estimations of the CNR, and their nuclear magnetic resonance NMR data illustrates a linear dependence of asphaltene concentration on NMR signal breaking around a similar 80— ppm range. Contradicting previous studies 5 , 26 , Rane, et al. Despite the above differences, using micellarisation as a proxy for asphaltene nanoaggregation allows the use of sonic velocity models 40 , 53 detecting aggregation as a function of changes in solution apparent densities and compressibilities of the dispersed phase.
The full model derivation may be found in Zielinski et al. For multi-phase fluids which are well-dispersed, and ignoring the effects of sound scattering valid for sufficiently low concentration of scatterers and away from scattering resonances 55 , Eq. An extension of Eq.
In particular, the sound velocity u is related to apparent molar solution quantities following the relation. The model 2 implies that pre- and post-micellarisation, sonic velocity is related to surfactant concentration as a combination of two linear behaviours whose intersection estimates the CMC.source
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Apparent molar properties are difficult to measure in practice and we presume that an accurate determination of the CMC is found by an intersection of two linear regressions, the optimal selected by informing the coefficient of determination R 2. Zielinski et al. A study by Andreatta et al. Upon closer examination of the monomer-aggregate boundary the point of asphaltene nanoaggregation is associated with velocity fluctuations, the reasons for which we aim to investigate in more detail. Further, we consider the phenomenon of multiple micellarisation whereby a surfactant solution is prone to forming micelles of multiple sizes given the variation in surfactant molecular size 56 , 57 , The latter occurs in some pure substances and is very common in mixtures of cationic surfactants with a variation in the hydrophobic tail length 57 , 58 , Critically, as asphaltenes constitute a fraction of petroleum with a distribution of molecular weights and structures we suggest that a phenomenon similar to multiple micellarisation will also be observed.
We will attempt to illustrate the latter by performing ultrasonic characterisation of alkyltrimethylammonium bromide surfactants in single and mixed forms followed by asphaltene dispersions in toluene. The two compounds differ by only two methyl groups and their mixtures were used to test sensitivity of the ultrasonic instrument to multiple micellarisation. Asphaltenes were precipitated from four petroleum samples: E1 with E2 and E3 with E4 are from two different source rocks respectively and all are from different reservoirs. Samples were selected such that there are two biodegradation aliquots from a source rock.
Biodegradation analysis was performed on deasphalted petroleum using thin layer chromatography with short column elution to obtain aliphatic and aromatic fractions.
The latter were then analysed by gas chromatograohy-mass spectrometry and relative biomarker abundance compared with Wenger et al. Asphaltene precipitation was performed using a fold excess n -alkane addition 19 , Inference about asphltene architecture was performed using ruthenium ion catalysed oxidation RICO Glass plates were covered with 0.
The separated aliphatic and aromatic fractions were placed in short columns and eluted with petroleum ether and DCM respectively. Compound identification was based on the NIST05 mass spectral library as well as comparison to mass spectra and relative retention times reported in other studies and in-house guides.
Resoscan is a high-resolution instrument allowing a simultaneous measurement of velocity and attenuation of liquid samples. Temperature control and measurement are performed via two external units enabling a resolution of 0.
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Therefore, the precision limit of temperature corresponds to a systematic error in velocity of around 0. Asphaltene solutions in toluene were injected using a glass syringe with an aluminium needle to avoid reactivity with the toluene solvent. Aqueous surfactant solutions were injected an auto pipette.
The obtained data was analysed for extreme outliers, caused by air bubbles and other artefacts. Mean velocity values were plotted versus solute concentration to which piecewise linear regression models were fitted. The level LVL of petroleum biodegradation 1—10 was estimated using the Wenger et al.
Gas chromatograms used in biodegradation analysis are provided in the Supplementary Information Fig. S1 — S3. E1 has little signs of biodegradation Fig. S1 a—d. Therefore, E1 is classified as LVL 2 biodegraded. The biodegradation extent of E2 Fig. S1 e-h is greater than that of E1, indicated by the removal of n -decane from the n -alkanes.
Therefore, E2 is is classed as LVL 2—3 biodegraded.
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In contrast, E3 illustrates a complete removal of n -alkanes which puts it into the heavily biodegraded range Fig. S2 a-f. E4 shows signs of early biodegradation Fig. S2 g,h and S3 with the homologous n -alkane series starting from n -C Isoprenoids, such as pristane and phytane, are detectable. Hopanes and steranes are abundant and only C 29 -norhopane has formed.
Therefore, E4 is estimated to be LVL 3 biodegraded. S4 illustrate left-skewed distributions with a mild even-odd predominance of medium molecular weight compounds. Note that the high C 16 and C 18 peaks also drive the even-odd predominance of E1 which is highest out of the four samples. In comparison, the compound distributions of E3 and E4 Fig.
S4 — S5 are more peaked with varying compound resolution. Even-odd ratios are 1. As nanoaggregation kinetics of asphaltenes are affected by steric hindrance between side-chains 5 , 11 , 21 we will test the coupling between asphaltene FAME distributions and nanoaggregation results observed in ultrasonic velocity measurements in the Discussion section. Every concentration measurement in an average of ca. Plots a,b and the corresponding R 2 values provide good evidence of the linearity between velocity and surfactant concentration, and the CMC values correspond to previous measurements 53 , 67 , Plots c,d illustrate multiple micellarisation in CTAB mixtures, with a strong indication of the primary critical micelle concentration CMC 1 ; the secondary critical micelle concentration CMC 2 may be highlighted by taking a natural log-transformation as shown in plots e,f.
Similar results were found by Ray et al. Concentration-velocity measurements of CTAB pure and mixed aqueous solutions. Points marked in black indicate data that were not included in linear regression estimation. Piecewise regression models indicated by dashed lines were fitted using a constrained optimisation scheme 36 , 37 which is detailed in the Supplementary Information. When choosing between different regression estimations we deploy this scheme as a guide to indicate where linear behaviour becomes no longer plausible by excessively penalising the R 2 measure given large outliers and changes in regression slope.
In general, the four plots suggest that at either ends of asphaltene concentration range the association with ultrasonic velocity is linear, and the CNR indicated by large outliers. Note that the change in the speed of sound is larger than the confidence intervals in the majority of cases. The larger error bars are likely to occur due to trace amounts of dissolved air.
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Concentration-velocity measurements of asphaltene-toluene mixtures. Dashed lines illustrate estimated linear models using constrained optimisation, CNR1 and CNR2 refer to the onset and decline of the critical nanoaggregation region respectively. E4 has the highest aggregated R 2 and the earliest onset of nanoaggregation at The trend below and above the CNR is significant over the measurement error bars for all samples.
These trends are similar to those observed in ultrasonic characterisation of Tween toluene mixtures and asphaltene-toluene mixtures by Andreatta, et al. In particular, the gradient change is consistent across our four samples, except E1 where both slopes are positive. We are not sure what the reason is for this discrepancy, but a few things can be noted here. The theoretical linear model underlying our measurements that relates the speed of sound to asphaltene concentration 40 , 53 is a function of apparent molar densities and compressibilities. As asphaltenes constitute a petroleum fraction with a range of molecular properties, isolating apparent molar quantities is challenging if not impossible.
Understanding the chemico-physical cause of changing gradient in asphaltene ultrasonic velocity measurement would be a very interesting task, however it goes beyond the scope of present investigation. We performed principal component analysis PCA 69 , 70 to assess the combined impact of the above variables on asphaltene nanoaggregation. It is uncertain whether the longer DFAME compounds are PAH linkages as there is no evidence in the literature that inter-aromatic bridges can be over seven carbons long 15 , Therefore, we will exclude this information from PCA. Although the latter is unfortunate, we assume that their contribution is relatively insignificant to hindrance effects as compared to the longer appendages.
This component can, therefore, be interpreted as the aggregation complexity variable, drawing a positive relation between the size of the CNR and the amount of longer asphaltene side-chains, all of which negatively affect the linear model fit. Observing the distribution in Fig. Interestingly, the contrast to long side-chains is half that of medium-sized ones, which implies that the linear model fit is more affected by the abundance of medium-length side-chains or perhaps the size variability.
In this domain, E4 and E2 have a very similar performance which is consistent with their fits to linear models Fig.
In conclusion, we have provided evidence for an asphaltene CNR. Asphaltene samples were selected to represent a range of molecular architectures and biodegradation levels of the parent oils. Ultrasound velocity characterisation was used to detect nanoaggregation in conjunction with the model by Zielinski, et al.
The analysis of asphaltene solutions in toluene illustrated a variability of aggregation behaviours, subject to asphaltene structural properties. This relationship was confirmed by PCA of asphaltene coupled structural and velocity data. We found that asphaltene structural properties, in particular longer aliphatic appendages and increased variation in size, contribute to a wider aggregation region and a decreased fit to the two-regression model.
The results call for a further investigation whereby the origin of longer DFAME compounds is established and their contribution to nanoaggregation also resolved. Ultrasonic velocity results will also be used to construct a probabilistic model of the CNR. Bridie, A. Sjoblom, J. Our current understanding of water-in-crude oil emulsions.
Colloid Interface Sci. Dicharry, C.
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