Metal-Organic Framework-Stabilized High Internal Phase Pickering Emulsions Based on Computer Simulation for Curcumin Encapsulation: Comprehensive Characterization and Stability Mechanism

A review of high internal phase Pickering emulsions: Stabilization, rheology, and 3D printing application

High internal phase Pickering emulsion (HIPPE) is renowned for its exceptionally high-volume fraction of internal phase, leading to flocculated yet deformed emulsion droplets and unique rheological behaviors such as shear-thinning property, viscoelasticity, and thixotropic recovery. Alongside the inherent features of regular emulsion systems, such as large interfacial area and well-mixture of two immiscible liquids, the HIPPEs have been emerging as building blocks to construct three-dimensional (3D) scaffolds with customized structures and programmable functions using an extrusion-based 3D printing technique, making 3D-printed HIPPE-based scaffolds attract widespread interest from various fields such as food science, biotechnology, environmental science, and energy transfer. Herein, the recent advances in preparing suitable HIPPEs as 3D printing inks for various applied fields are reviewed. This work begins with the stabilization mechanism of HIPPEs, followed by introducing the origin of their distinctive rheological behaviors and strategies to adjust the rheological behaviors to prepare more eligible HIPPEs as printing inks. Then, the compatibility between extrusion-based 3D printing and HIPPEs as building blocks was discussed, followed by a summary of the potential applications using 3D-printed HIPPE-based scaffolds. Finally, limitations and future perspectives on preparing HIPPE-based materials using extrusion-based 3D printing were presented.

Encapsulation of ferulic acid in high internal phase Pickering emulsions stabilized using nonenyl succinic anhydride (NSA) and octenyl succinic anhydride (OSA) modified quinoa and maize starch nanoparticles

High internal phase Pickering emulsions (HIPPEs) stabilized using modified starch nanoparticles (SNPs) were studied as a delivery system for ferulic acid (FA). The quinoa (Q, 153 nm) and maize (M, 221 nm) SNPs were prepared by sono-precipitation and modified with nonenyl succinic anhydride (NSA) and octenyl succinic acid (OSA). The FA-encapsulated HIPPEs obtained showed neither coalescence nor Ostwald ripening, as reflected by emulsion index and droplet size measurements. Confocal laser scanning microscopy revealed FA entrapped droplets surrounded by the SNPs layer. The rheological measurements confirmed strong network formation and long-term stability. In vitro studies (pH 7.4, 96 h) showed sustained release of FA from the gel network. After 15 days, the encapsulation efficiencies for HIPPEs stabilized with both NSA and OSA modified QSNPs and MSNPs were close to 99%. The results showed that FA could be feasibly encapsulated in HIPPEs stabilized using modified SNPs.

Engineering linker defects in functionalized UiO-66 MOF nanoparticles for oil-in-water Pickering emulsion stabilization

Designing metal-organic framework (MOF)-based solid nanoparticles to stabilize Pickering emulsions by fine-tuning their hydrophobicity and lipophobicity is vital for essential applications and fundamental understanding. We demonstrate in situ grafting of palmitic acid in UiO-66 MOF through its linker defects. Our designed and activated nanoparticles (denoted as UP') stabilized the Pickering emulsions of n-heptane-in-water. Furthermore, we showed how UP' stabilized emulsion droplets disperse in media by covering each tiny droplet with a nanoscale layer made of UP'. To support our claim, we carried out the freeze-drying process to remove the liquid part from the emulsion, leaving behind the solid shell-like microstructures that we further characterized through several microscopic techniques. The stable n-heptane-in-water emulsion was confirmed by dilution (drop test), conductivity, zeta potential, and theoretical surface electrostatic potential measurements. Rheological studies indicate that the Pickering emulsions of n-heptane-in-water stabilized by UP' are much more resistant to deformation and flow imparting higher (mechanical) stability and shelf-life. Pickering emulsions stabilized by UP' emerged as a versatile way to design smart functional materials of UiO-66 through engineering linker defects that may have potential applications in interfacial catalysis, dye or contaminant separation, etc.

Development of Stable Pickering Emulsions with TEMPO-Oxidized Chitin Nanocrystals for Encapsulation of Quercetin

Pickering emulsions stabilized by TEMPO-oxidized chitin nanocrystals (T-ChNCs) were developed for quercetin delivery. T-ChNCs were synthesized by TEMPO oxidation chitin and systematically characterized in terms of their physicochemical properties. T-ChNCs were rod-like with a length of 279.7 ± 11.5 nm and zeta potential around -56.1 ± 1.6 mV. The Pickering emulsions were analyzed through an optical microscope and CLSM. The results showed that the emulsion had a small droplet size (972.9 ± 86.0 to 1322.3 ± 447.7 nm), a high absolute zeta potential value (-48.2 ± 0.8 to -52.9 ± 1.9 mV) and a high encapsulation efficiency (quercetin: 79.6%). The emulsion stability was measured at different levels of T-ChNCs and pH values. The droplet size and zeta potential decreased with longer storage periods. The emulsions formed by T-ChNCs retarded the release of quercetin at half rate of that of the quercetin ethanol solution. These findings indicated that T-ChNCs are a promising candidate for effectively stabilizing Pickering emulsions and controlling release of quercetin.

Neural network in food analytics

Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.