Seven sour substances enhancing characteristics and stability of whey protein isolate emulsion and its heat-induced emulsion gel under the non-acid condition.

Protein emulsion gels, as potential novel application ingredients in the food industry, are very unstable in their formation. However, the incorporation of sour substances (phosphoric acid, lactic acid, acetic acid, malic acid, glutamic acid, tartaric acid and citric acid) would potentially contribute to the stable formation of whey protein isolate (WPI) emulsion as well as its gel. Thus, in this work, physical stability of seven acid-treated WPI emulsions, and microstructures, rheological properties, water distribution of its emulsion gels were characterized and compared. Initially, the absolute zeta-potential, interfacial protein adsorption, and emulsifying characteristics of acid-induced WPI emulsions were higher in contrast to acid-untreated WPI emulsions. Moreover, acid-induced WPI emulsions were thermally induced (95 ℃, 30 min) to form its emulsion gel networks via disulfide bonds as the main force (acid-untreated WPI emulsions were unable to form gels). High-resolution microscopic observation revealed that acid-induced WPI in emulsion gel network showed the morphology of aggregates. Dynamic oscillatory rheology results indicated that acid-induced emulsion gel exhibited highly elastic behavior and its viscoelasticity was associated with the generation of protein gel networks and aggregates. In addition, PCA and heatmap results further illustrated that malic acid-induced WPI emulsion gels had the best water holding capacity and gel characteristics. Therefore, this study could provide an effective way for the foodstuffs industry to open up new texture and healthy emulsion gels as fat replaces and loading systems of bioactive substances.

Exploring the role of γ-Oryzanol on stabilization mechanism of Pickering emulsion gels loaded by α-Lactalbumin or ß-Lactoglobulin via multiscale approaches.

The research explored the role of γ-oryzanol (γs) on stabilization behavior of Pickering emulsion gels (PEGs) loaded by α-lactalbumin (α-LA) or ß-lactoglobulin (ß-LG), being analyzed by experimental and computer methods (molecular dynamic simulation, MD). Primarily, the average particle size of ß-LG-γS was expressed 100.07% decrease over that of α-LA-γS. In addition, γs decreased the dynamic interfacial tension of two proteins with the order of ß-LG < α-LA. Meanwhile, quartz crystal microbalance with dissipation proved that ß-LG-γS exhibited higher adsorption mass and denser rigid interface layer than α-LA-γS. Moreover, the hydrophobic group of γS had electrostatic repulsion with polar water molecules in the aqueous phase, which spread to the oil phase. ß-LG-γS had lower RMSD/Rg value and narrower fluctuation compared with α-LA-γS. This work strength the exploration of interfacial stabilization mechanism of whey protein-based PEGs, which enriched its theoretical research for industrial-scale production as the replacement of trans fat and cholesterol.

Structural deep multi-view clustering with integrated abstraction and detail.

Deep multi-view clustering, which can obtain complementary information from different views, has received considerable attention in recent years. Although some efforts have been made and achieve decent performances, most of them overlook the structural information and are susceptible to poor quality views, which may seriously restrict the capacity for clustering. To this end, we propose Structural deep Multi-View Clustering with integrated abstraction and detail (SMVC). Specifically, multi-layer perceptrons are used to extract features from specific views, which are then concatenated to form the global features. Besides, a global target distribution is constructed and guides the soft cluster assignments of specific views. In addition to the exploitation of the top-level abstraction, we also design the mining of the underlying details. We construct instance-level contrastive learning using high-order adjacency matrices, which has an equivalent effect to graph attention network and reduces feature redundancy. By integrating the top-level abstraction and underlying detail into a unified framework, our model can jointly optimize the cluster assignments and feature embeddings. Extensive experiments on four benchmark datasets have demonstrated that the proposed SMVC consistently outperforms the state-of-the-art methods.

Electronic Structure Regulated Nickel-Cobalt Bimetal Phosphide Nanoneedles for Efficient Overall Water Splitting.

Transition metal phosphides (TMPs) have been widely studied for water decomposition for their monocatalytic property for anodic or cathodic reactions. However, their bifunctional catalytic activity still remains a major challenge. Herein, hexagonal nickel-cobalt bimetallic phosphide nanoneedles with 1-3 µm length and 15-30 nm diameter supported on NF (NixCo2-xP NDs/NF) with adjusted electron structure have been successfully prepared. The overall alkaline water electrolyzer composed of the optimal anode (Ni0.67Co1.33P NDs/NF) and cathode (Ni1.01Co0.99P NDs/NF) provide 100 mA cm-2 at 1.62 V. Gibbs Free Energy for reaction paths proves that the active site in the hydrogen evolution reaction (HER) is Ni and the oxygen evolution reaction (OER) is Co in NixCo2-xP, respectively. In the HER process, Co-doping can result in an apparent accumulation of charge around Ni active sites in favor of promoting HER activity of Ni sites, and ΔGH* of 0.19 eV is achieved. In the OER process, the abundant electron transfer around Co-active sites results in the excellent ability to adsorb and desorb *O and *OOH intermediates and an effectively reduced ∆GRDS of 0.37 eV. This research explains the regulation of electronic structure change on the active sites of bimetallic materials and provides an effective way to design a stable and effective electrocatalytic decomposition of alkaline water.