Dramatic influence of fragrance alcohols and phenols on the phase inversion temperature of the Brij30/n-octane/water system

Fast Prediction of the Equivalent Alkane Carbon Number Using Graph Machines and Neural Networks

The hydrophobicity of oils is a key parameter to design surfactant/oil/water (SOW) macro-, micro-, or nano-dispersed systems with the desired features. This essential physicochemical characteristic is quantitatively expressed by the equivalent alkane carbon number (EACN) whose experimental determination is tedious since it requires knowledge of the phase behavior of the SOW systems at different temperatures and for different surfactant concentrations. In this work, two mathematical models are proposed for the rapid prediction of the EACN of oils. They have been designed using artificial intelligence (machine-learning) methods, namely, neural networks (NN) and graph machines (GM). While the GM model is implemented from the SMILES codes of a 111-molecule training set of known EACN values, the NN model is fed with some σ-moment descriptors computed with the COSMOtherm software for the 111-molecule set. In a preliminary step, the leave-one-out algorithm is used to select, given the available data, the appropriate complexity of the two models. A comparison of the EACNs of liquids of a fresh set of 10 complex cosmetic and perfumery molecules shows that the two approaches provide comparable results in terms of accuracy and reliability. Finally, the NN and GM models are applied to nine series of homologous compounds, for which the GM model results are in better agreement with the experimental EACN trends than the NN model predictions. The results obtained by the GMs and by the NN based on σ-moments can be duplicated with the demonstration tool available for download as detailed in the Supporting Information.

In2O3/oxygen doped g-C3N4 towards photocatalytic BPA degradation: Balance of oxygen between metal oxides and doped g-C3N4

Hybrid semi-conductor heterojunction appears to be a promising technology for pollutant removal and wastewater treatment. However, the interface modification of the heterojunction and the working mechanisms remain elusive, thus impeding the development of highly efficient photocatalysis. In this work, we highlighted the key role played by the 3D/2D In₂O₃/oxygen doped graphitic carbon nitrides (g-C₃N₄) heterojunction, named In₂O₃/OGCN, on the photocatalytic performance. The characteristic results showed a balance of oxygen between In₂O₃ and OGCN, which enabled a stable interaction in the heterojunction to specifically tune the oxidation power, and this strategy can be applied to rationally control the photocatalytic activity of organic pollutants. The optimized In₂O₃/OGCN heterojunction demonstrated a notable photocatalytic degradation capability for bisphenol A (BPA), which was better than that of pristine In₂O₃ and OGCN, respectively. This photocatalyst had a great physical stability and can be recycled for further use. Meanwhile, the exceptional photodegradation capacity was attributed to spatially separated charge carriers, fast-charge transportation characteristics, and the special band gap structure of In₂O₃/OGCN heterojunction. In addition, the covalent bond between In-O significantly improved oxygen stability in the lattice, thereby increasing the reliability of the material. This research presents a new opportunity to fabricate metal oxides/OGCN heterojunction photocatalysts which have potential application in wastewater treatment by adjusting the oxygen between the two compounds in heterojunction.

Use of the normalized hydrophilic-lipophilic-deviation (HLDN) equation for determining the equivalent alkane carbon number (EACN) of oils and the preferred alkane carbon number (PACN) of nonionic surfactants by the fish-tail method (FTM)

The standard HLD (Hydrophilic-Lipophilic-Deviation) equation expressing quantitatively the deviation from the "optimum formulation" of Surfactant/Oil/Water systems is normalized and simplified into a relation including only the three more meaningful formulation variables, namely (i) the "Preferred Alkane Carbon Number" PACN which expresses the amphiphilicity of the surfactant, (ii) the "Equivalent Alkane Carbon Number" EACN which accurately reflects the hydrophobicity of the oil and (iii) the temperature which has a strong influence on ethoxylated surfactants and is thus selected as an effective, continuous and reversible scanning variable. The PACN and EACN values, as well as the "temperature-sensitivity-coefficient"τ of surfactants are determined by reviewing available data in the literature for 17 nonionic n-alkyl polyglycol ether (C_iE_j) surfactants and 125 well-defined oils. The key information used is the so-called "fish-tail-temperature" T* which is a unique data point in true ternary C_iE_j/Oil/Water fish diagrams. The PACNs of C_iE_j surfactants are compared with other descriptors of their amphiphilicity, namely, the cloud point, the HLB number and the PIT-slope value. The EACNs of oils are rationalized by the Effective-Packing-Parameter concept and modelled thanks to the COSMO-RS theory.

Preparation of photochromic liquid core nanocapsules based on theoretical design

Photochromic materials have attracted considerable attention for their practical applications in optoelectronic devices. In this study, we developed the photochromic liquid core nanocapsules by polymerization of oil-in-water (O/W) nanoemulsion monomer droplets on the basis of Hansen solubility parameters. The thermoresponsive amphiphilic block copolymers (POEGMA_m-b-PSt_n and POEGMA_m-b-PMMA_n) were synthesized by sequential reversible addition fragmentation chain transfer (RAFT) polymerization of hydrophilic oligo(ethylene glycol) methyl ether methacrylate and hydrophobic styrene or MMA. The O/W nanoemulsion methyl methacrylate (MMA) droplets, dissolving dipropylene glycol methyl-n-propyl ether (DPMNP) and (E)-3-(adamantan-2-ylidene)-4-[1-(2,5-dimethyl-3-furyl) ethylidene]dihydro-2,5furandione (Aberchrome 670) as a core liquid and a photochromic dye, respectively, were obtained through the phase inversion temperature emulsification technique using POEGMA_m-b-PSt_n as a surfactant. As theoretically predicted in terms of the spreading coefficients, the DPMNP solution of Aberchrome 670 was successfully encapsulated by coacervation of the crosslinked PMMA condensed phase. Aberchrome 670 dissolved in a liquid core was found to photoisomerize twice as fast as that dispersed in the solid polymer matrices.