Bi-magnetic Mn3O4@Ni core-shell binary superparticles: Self-assembly preparation and magnetic behaviors.

Binary superparticles formed by self-assembling two different types of nanoparticles may utilize the synergistic interactions and create advanced multifunctional materials. Bi-magnetic superparticles with a core-shell structure have unique properties due to their specific spatial configurations. Herein, we built Mn3O4@Ni core-shell binary superparticles via an emulsion self-assembly technique. The superparticles are generated with a spherical morphology, and have a typical average size of about 240 nm. By altering the ratio of the two magnetic nanoparticles, the thickness of Ni shells can be adjusted. Oleic acid ligands are crucial for the formation of core-shell structure. Magnetic analysis suggests that core-shell superparticles display dual-phase magnetic interactions, contrasting with the single-phase magnetic behaviors of commonly core-shell magnetic nanoparticles. The calculation on the effective magnetic anisotropy constants indicates that the presence of Ni shell layers reduces the dipole interactions among the Mn3O4 core particles. Due to the presence of Ni nanoparticle shells, the blocking temperature of Mn3O4 is reduced, while the Curie temperature of Mn3O4 is independent on Ni content. Tunable magnetic properties can be achieved by modulating the Ni nanoparticle shell thickness. This study offers insights for the development of core-shell superparticles with varied magnetic characteristics.

Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinations.

Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.

Self-Assembled Preparation of Porous Nickel Phosphide Superparticles with Tunable Phase and Porosity for Efficient Hydrogen Evolution.

Self-assembly of colloidal nanoparticles enables the easy building of assembly units into higher-order structures and the bottom-up preparation of functional materials. Nickel phosphides represent an important group of catalysts for hydrogen evolution reaction (HER) from water splitting. In this paper, the preparation of porous nickel phosphide superparticles and their HER efficiencies are reported. Ni and Ni2P nanoparticles are self-assembled into binary superparticles via an oil-in-water emulsion method. After annealing and acid etching, the as-prepared Ni-Ni2P binary superparticles change into porous nickel phosphide superparticles. The porosity and crystalline phase of the superparticles can be tuned by adjusting the ratio of Ni and Ni2P nanoparticles. The resulting porous superparticles are effective in driving HER under acidic conditions, and the modulation of porosity and phase further optimize the electrochemical performance. The prepared Ni3P porous superparticles not only possess a significantly enhanced specific surface area compared to solid Ni-Ni2P superparticles but also exhibit an excellent HER efficiency. The calculations based on the density functional theories show that the (110) crystal facet exhibits a relatively lower Gibbs free energy of hydrogen adsorption. This work provides a self-assembly approach for the construction of porous metal phosphide nanomaterials with tunable crystalline phase and porosity.