Monitoring the Early Stages of Formation of Oil–Water Emulsions Using Flow Cytometry

Hammami, Mohamed Amen; Kanj, Mazen Y.; Giannelis, Emmanuel P.

doi:10.1021/acs.langmuir.1c01847

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…The assembly of nanoparticles at the oil–water interface has received widespread attention and has been extensively studied by numerous investigators. These studies include but are not limited to food processing and production, − drug encapsulation, , environmental remediation, − and enhanced oil recovery. − The interest stems from the fact that particle-laden interfaces offer a route to controlled interfacial properties, including the ultimate stability of an emulsion. − Oil droplets surrounded by particles result in improved viscoelastic properties of the interface. Hence, emulsion breakup, coalescence, or coarsening can be circumvented or slowed. ,− …”

Section: Introductionmentioning

confidence: 99%

Probing the Interfacial Properties of Oil–Water Interfaces Decorated with Ionizable, pH Responsive Silica Nanoparticles

et al. 2023

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Particle-stabilized emulsions (Pickering emulsions) have recently attracted significant attention in scientific studies and for technological applications. The interest stems from the ease of directly assembling the particles at interfaces and modulating the interfacial properties. In this paper, we demonstrate the formation of stable, practical emulsions leveraging the assembly of ionizable, pH responsive silica nanoparticles, surface-functionalized by a mixture of silanes containing amine/ammonium groups, which renders them positively charged. Using pH as the trigger, the assembly and the behavior of the emulsion are controlled by modulating the charges of the functional groups of the nanoparticle and the oil (crude oil). In addition to their tunable charge, the particular combination of silane coupling agents leads to stable particle dispersions, which is critical for practical applications. Atomic force microscopy and interfacial tension (IFT) measurements are used to monitor the assembly, which is controlled by both the electrostatic interactions between the particles and oil and the interparticle interactions, both of which are modulated by pH. Under acidic conditions, when the surfaces of the oil and the nanoparticles (NPs) are positively charged, the NPs are not attracted at the interface and there is no significant reduction in the IFT. In contrast, under basic conditions in which the oil carries a high negative charge and the amine groups on the silica are deprotonated while still positively charged because of the ammonium groups, the NPs assemble at the interface in a closely packed configuration yielding a jammed state with a high dilatational modulus. As a result, two oil droplets do not coalesce even when pushed against each other and the emulsion stability improves significantly. The study provides new insights into the directed assembly of nanoparticles at fluid interfaces relevant to several applications, including environmental remediation, catalysis, drug delivery, food technology, and oil recovery.

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Section: Introductionmentioning

confidence: 99%

Probing the Interfacial Properties of Oil–Water Interfaces Decorated with Ionizable, pH Responsive Silica Nanoparticles

et al. 2023

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“…Manipulating the properties of liquid–liquid interfaces and specifically oil–water interfaces using nanoparticles (so-called Pickering emulsions) has attracted widespread attention due to both scientific interest as well as use in practical applications. − Assembly of nanoparticles of various chemistries and dimensionalities at the oil–water interfaces stabilizes the emulsion and offers the possibility to synthesize materials with controlled behavior by fine-tuning interfacial properties. ,, By varying the chemistry and dimensionality of the nanoparticles, the adsorption energy and thus the interfacial properties can be modulated and controlled. , …”

Section: Introductionmentioning

confidence: 99%

Fine-Tuning the Surface and Interfacial Chemistry of Silica Nanoparticles to Control Stability in High Ionic Strength Electrolytes and Modulate Assembly at Oil–Water Interfaces

Alsmaeil,

Mohammed,

Aldakkan

et al. 2023

Chem. Mater.

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Controlling the interfacial chemistry is critical to the behavior of several material systems. In this report, we demonstrate how subtle changes in the surface chemistry of silica nanoparticles have a profound effect on their colloidal stability, as well as their ability to assemble at oil−water interfaces. The ability to direct the assembly by fine-tuning the surface chemistry of nanoparticles, NPs, allows for controlling the interfacial properties and the overall behavior of the resulting Pickering emulsions. We start by showing that the colloidal stability of silica nanoparticles functionalized by a mixture of silanes is dramatically improved compared to that of silica functionalized with either one. For example, NP suspensions synthesized using a 50:50 mixture of N1-(3-trimethoxysilylpropyl)diethylenetriamine and N-trimethoxysilylpropyl-N,N,Ntrimethylammonium in a complex brine containing a mixture of salts remain suspended when subjected to acceleration of 500 × g⃗ and temperatures as high as 60 °C. In contrast, particles functionalized with either one of the silanes are far less stable under the same conditions. The colloidal stability of the particles is correlated to the silane-grafted layers and surface roughness obtained by atomic force microscopy (AFM). We next studied the mechanism of the particles' assembly at oil−water interfaces and characterized the interfacial properties under various conditions. In all cases, the assembly is driven by electrostatic interactions between the positively charged particles and the negatively charged oil surface. Ultrasmall-/small-angle X-ray scattering measurements confirm the assembly of nanoparticles at the interface, and time-dependent scattering measurements reveal the presence of two steps in the assembly in brine, consistent with the interfacial tension dynamics. The assembled particles at the interface lead to a solid-like behavior or jamming, with the interface behaving like an elastic membrane with high dilatational and storage moduli. This study provides fundamental insights into the surface and interfacial properties of silane-grafted NPs and ways to fine-tune their assembly at oil−water interfaces while improving their colloidal stability under harsh environmental conditions.

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“…More discussion on the concepts about the perovskite degradation process, , water additives to suppress the recombination and improve performance, − aqueous synthesis of the halide perovskite nanocrystals, molecular passivating agents, and perovskite encapsulation technologies for water repellence such as carbon/silver paint and graphite for solar fuel generation can be referred to the literature. In addition, aqueous dye-sensitized solar cells (DSSCs) are emerging as a promising alternative to enhance both the lifetime and the environment-friendliness of the traditional DSSCs; − the water inclusion is extensively researched to replace the volatile and poisonous electrolyte in DSSCs …”

Section: Introductionmentioning

confidence: 99%

Photoelectrochemical Properties, Machine Learning, and Symbolic Regression for Molecularly Engineered Halide Perovskite Materials in Water

Pan

Zhou

Zhang

2022

ACS Appl. Mater. Interfaces

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The machine learning techniques are capable of predicting virtual material design space and optimizing material fabrication parameters. In this article, we construct machine learning models to describe the photoelectrochemical properties of molecularly engineered halide perovskite materials based on CH 3 NH 3 PbI 3 in an aqueous solution and predict a complex multidimensional design space for the halide perovskite materials. The machine learning models are trained and tested based on an experimental photocurrent data set consisting of 360 data points with varying experimental conditions and dye structures. Machine learning algorithms including support vector machine (SVM), random forest, k-nearest neighbors, Rpart, Xgboost, and Kriging algorithms are compared, with the Kriging algorithm achieving the best accuracies (r = 0.99 and R 2 = 0.98) and SVM achieving the second best. A total of 50,905 data points representing the complex multidimensional design space are predicted via the machine-learned models to benefit the future perovskite studies. In addition, the symbolic regression based on the genetic algorithms effectively and automatically designs hybrid descriptors that outperform the individual descriptors. This article highlights the machine learning and symbolic regression methods for designing stable and highperformance halide perovskite materials and serves as a platform for further experimental optimization of halide perovskite materials.

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Monitoring the Early Stages of Formation of Oil–Water Emulsions Using Flow Cytometry

Cited by 4 publications

References 44 publications

Probing the Interfacial Properties of Oil–Water Interfaces Decorated with Ionizable, pH Responsive Silica Nanoparticles

Probing the Interfacial Properties of Oil–Water Interfaces Decorated with Ionizable, pH Responsive Silica Nanoparticles

Fine-Tuning the Surface and Interfacial Chemistry of Silica Nanoparticles to Control Stability in High Ionic Strength Electrolytes and Modulate Assembly at Oil–Water Interfaces

Photoelectrochemical Properties, Machine Learning, and Symbolic Regression for Molecularly Engineered Halide Perovskite Materials in Water

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