Salinity and pH effects on water-oil-calcite interfaces by using molecular dynamics

Smart water injection is a technology that allows changing the wettability of the oil rock by injecting water at different salinities, in a cheap and environmentally friendly way compared to other traditional methods. In this study, the individual effect of some typical salts on the wettability of the (104) surface of calcite toward non-polar and polar crude oil models was explored by molecular dynamics as a function of the salinity and pH. The results obtained show that the electrical double layer plays a principal role in the detachment of crude oil models. The divalent ion salts, i.e., CaCl2, CaSO4, MgCl2, and MgSO4, do not form the electrical double layer on calcite, but salts of NaCl and Na2SO4 form it. Moreover, the surface affinity of calcite to the non-polar crude oil is not affected by the salinity. However, the affinity of the calcite surface toward polar crude is affected by salinity and pH conditions. This research provides new insights into the action mechanisms that could help optimize its uses in enhanced oil recovery.

Discriminator for Cutaneous Leishmaniasis Using MALDI-MSI in a Murine Model

Cutaneous leishmaniasis is a skin disease caused by flagellate protozoa of the genus Leishmania and transmitted by sandflies of the genus Lutzomyia. Around 1 million new cases occur in the world annually, with a total of 12 million people affected, mainly in rural areas with low access to health services and adequate treatments. In the area of the Americas, Colombia has one of the highest infection rates after Brazil. Topical treatments with pentamidine isethionate (PMD) present an attractive alternative due to their ease of application and low costs. However, cutaneous leishmaniasis lesions present nodules with seropurulent exudate that, when drying, form hyperkeratotic lesions, hindering the effective penetration of drugs for their treatment. The use of molecular histology techniques, such as MALDI-MSI, allow in situ evaluation of the penetration of the treatment to the sections of the dermis where the disease-causing parasite resides. However, the large volume of information generated makes it impossible to process it manually. Machine learning techniques allow the unsupervised processing of large amounts of information, generating prediction models for the classification of new information. This work proposes a low-cost method to generate cutaneous leishmaniasis detection and classification models using MALDI-MSI images taken from murine models. The proposed models allow a 95% efficiency when separating healthy samples from infected samples and an effectiveness of 67% when separating effectively treated samples from unsuccessfully treated samples.

Wettability of graphene oxide functionalized with N-alkylamines: a molecular dynamics study

The wettability of graphene oxide functionalized with N-alkylamines was studied by molecular dynamics simulations. Six different N-alkylamines and two functionalization degrees were reviewed. The nucleophilic ring-opening reaction mechanism between the N-alkylamines and epoxy functional groups of graphene oxide was considered to generate the atomistic models. Water contact angles increased with both the alkyl chain length and substitution degree. The Wenzel model was used to access the effect of both the surface roughness and alkyl chain length on wettability. The results indicated that functionalization introduces an important increase of surface roughness but its effect on wettability is countered by the alkyl chain length.

New Synthetic Peptides Conjugated to Gold Nanoclusters: Antibiotic Activity Against Escherichia coli O157:H7 and Methicillin-Resistant Staphylococcus aureus (MRSA)

Gold nanoclusters protected with bovine serum albumin (AuNC) can be used in multiple biomedical applications through functionalization with two new and bioactive peptides. Both cationic peptides sequences of 17 amino acids in length and the cysteine residue at its C-terminus were designed and synthesized. Peptides were obtained by solid phase synthesis using the Fmoc strategy. Peptides may be coupled via disulfide bonds to AuNC with hydrodynamic size ~ 2 nm ± 0.3 determined by dynamic light scattering and it had a zeta potential value equal to - 42 mV. Peptides named NBC2253 and NBC2254 were attached to the AuNC using N-succinimidyl-3-(2-pyridyl-dithiol) propionate as crosslinker agent. AuNC@NBC2253 was more active against methicillin-resistant Staphylococcus aureus (MIC50 6.5 µM) and AuNC@NBC2254 exhibited higher antimicrobial activity than the free peptides on Escherichia coli O157:H7 (MIC50 3.5 µM). No hemolysis was detected for any of the peptides. It is evidenced that these antimicrobial peptides conjugated to AuNC serve as promising agents to combat some multi-resistant bacterial strains and that the specific binding of these antimicrobial peptides to gold nanoclusters improves the interaction of these nanostructured systems with the biological target.

Pushing the analytical limits: new insights into complex mixtures using mass spectra segments of constant ultrahigh resolving power

A new strategy has been developed for characterization of the most challenging complex mixtures to date, using a combination of custom-designed experiments and a new data pre-processing algorithm. In contrast to traditional methods, the approach enables operation of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) with constant ultrahigh resolution at hitherto inaccessible levels (approximately 3 million FWHM, independent of m/z). The approach, referred to as OCULAR, makes it possible to analyze samples that were previously too complex, even for high field FT-ICR MS instrumentation. Previous FT-ICR MS studies have typically spanned a broad mass range with decreasing resolving power (inversely proportional to m/z) or have used a single, very narrow m/z range to produce data of enhanced resolving power; both methods are of limited effectiveness for complex mixtures spanning a broad mass range, however. To illustrate the enhanced performance due to OCULAR, we show how a record number of unique molecular formulae (244 779 elemental compositions) can be assigned in a single, non-distillable petroleum fraction without the aid of chromatography or dissociation (MS/MS) experiments. The method is equally applicable to other areas of research, can be used with both high field and low field FT-ICR MS instruments to enhance their performance, and represents a step-change in the ability to analyze highly complex samples.

Manufacturing of Polymeric Substrates with Copper Nanofillers through Laser Stereolithography Technique

This study presents the additive manufacture of objects using mass-functionalized photo-resins, which are additively photopolymerized using the laser stereolithography technique. The mass functionalization is based on the incorporation of copper nanowires used as fillers at different concentrations. Cylindrical and tensile test probes are designed and manufactured in a layer-by-layer approach using a low-cost laser stereolithography system working with a layer thickness of 100 µm. The morphological, mechanical, thermal and chemical results help to show the viability and potential that this combination of mass-functionalized resins and technological processes may have in the near future, once key challenges are solved. Finally, some potential applications are also discussed.

Construction of a predictive model for concentration of nickel and vanadium in vacuum residues of crude oils using artificial neural networks and LIBS

A predictive model to determine the concentration of nickel and vanadium in vacuum residues of Colombian crude oils using laser-induced breakdown spectroscopy (LIBS) and artificial neural networks (ANNs) with nodes distributed in multiple layers (multilayer perceptron) is presented. ANN inputs are intensity values in the vicinity of the emission lines 300.248, 301.200 and 305.081 nm of the Ni(I), and 309.310, 310.229, and 311.070 nm of the V(II). The effects of varying number of nodes and the initial weights and biases in the ANNs were systematically explored. Average relative error of calibration/prediction (REC/REP) and average relative standard deviation (RSD) metrics were used to evaluate the performance of the ANN in the prediction of concentrations of two elements studied here.