Surface Plasmon Aided Ethanol Dehydrogenation Using Ag–Ni Binary Nanoparticles

Plasmonic metal nanoparticles absorb light energy and release the energy through radiative or nonradiative channels. Surface catalytic reactions take advantage of the nonradiative energy relaxation of plasmons with enhanced activity. Particularly, binary nanoparticles are interesting because diverse integration is possible, consisting of a plasmonic part and a catalytic part. Herein, we demonstrated ethanol dehydrogenation under light irradiation using Ag–Ni binary nanoparticles with different shapes, snowman and core–shell, as plasmonic catalysts. The surface plasmon formed in the Ag part enhanced the surface catalytic reaction that occurred at the Ni part, and the shape of the nanoparticles affected the extent of the enhancement. The surface plasmon compensated the thermal energy required to trigger the catalytic reaction. The absorbed light energy was transferred to the catalytic part by the surface plasmon through the nonradiative hot electrons. The effective energy barrier was greatly reduced from 41.6 kJ/mol for the Ni catalyst to 25.5 kJ/mol for the core–shell nanoparticles and 22.3 kJ/mol for the snowman-shaped nanoparticles. These findings can be helpful in designing effective plasmonic catalysts for other thermally driven surface reactions

Morphological Evolution of Block Copolymer Particles: Effect of Solvent Evaporation Rate on Particle Shape and Morphology

Shape and morphology of polymeric particles are of great importance in controlling their optical properties or self-assembly into unusual superstructures. Confinement of block copolymers (BCPs) in evaporative emulsions affords particles with diverse structures, including prolate ellipsoids, onion-like spheres, oblate ellipsoids, and others. Herein, we report that the evaporation rate of solvent from emulsions encapsulating symmetric polystyrene-<i>b</i>-polybutadiene (PS-<i>b</i>-PB) determines the shape and internal nanostructure of micron-sized BCP particles. A distinct morphological transition from the ellipsoids with striped lamellae to the onion-like spheres was observed with decreasing evaporation rate. Experiments and dissipative particle dynamics (DPD) simulations showed that the evaporation rate affected the organization of BCPs at the particle surface, which determined the final shape and internal nanostructure of the particles. Differences in the solvent diffusion rates in PS and PB at rapid evaporation rates induced alignment of both domains perpendicular to the particle surface, resulting in ellipsoids with axial lamellar stripes. Slower evaporation rates provided sufficient time for BCP organization into onion-like structures with PB as the outermost layer, owing to the preferential interaction of PB with the surroundings. BCP molecular weight was found to influence the critical evaporation rate corresponding to the morphological transition from ellipsoid to onion-like particles, as well as the ellipsoid aspect ratio. DPD simulations produced morphologies similar to those obtained from experiments and thus elucidated the mechanism and driving forces responsible for the evaporation-induced assembly of BCPs into particles with well-defined shapes and morphologies

Designing Tripodal and Triangular Gadolinium Oxide Nanoplates and Self-Assembled Nanofibrils as Potential Multimodal Bioimaging Probes

Here, we report the shape-controlled synthesis of tripodal and triangular gadolinium oxide (Gd₂O₃) nanoplates. In the presence of lithium ions, the shape of the nanocrystals is readily controlled by tailoring reaction parameters such as temperature and time. We observe that the morphology transforms from an initial tripodal shape to a triangular shape with increasing reaction time or elevated temperatures. Highly uniform Gd₂O₃ nanoplates are self-assembled into nanofibril-like liquid-crystalline superlattices with long-range orientational and positional order. In addition, shape-directed self-assemblies are investigated by tailoring the aspect ratio of the arms of the Gd₂O₃ nanoplates. Due to a strong paramagnetic response, Gd₂O₃ nanocrystals are excellent candidates for MRI contrast agents and also can be doped with rare-earth ions to form nanophosphors, pointing to their potential in multimodal imaging. In this work, we investigate the MR relaxometry at high magnetic fields (9.4 and 14.1 T) and the optical properties including near-IR to visible upconversion luminescence and X-ray excited optical luminescence of doped Gd₂O₃ nanoplates. The complex shape of Gd₂O₃ nanoplates, coupled with their magnetic properties and their ability to phosphoresce under NIR or X-ray excitation which penetrate deep into tissue, makes these nanoplates a promising platform for multimodal imaging in biomedical applications

Synthesis and Size-Selective Precipitation of Monodisperse Nonstoichiometric M_<i>x</i>Fe_3–<i>x</i>O₄ (M = Mn, Co) Nanocrystals and Their DC and AC Magnetic Properties

Spinel ferrite nanocrystals (NCs) have shown great promise for a wide variety of electromagnetic and medical applications. In this work, the AC magnetic properties of nonstoichiometric manganese and cobalt ferrites (M_<i>x</i>Fe_3–<i>x</i>O₄, M = Mn, Co) NCs are systematically studied as a function of composition. Samples of very similar average size and shape, but different Mn to Fe and Co to Fe ratios are prepared to study the effect of composition. Conventional syntheses are combined with a size-selective precipitation method using oleic acid as an antisolvent yielding nearly monodisperse samples. DC and AC magnetic measurements shows that introducing Co to the ferrite crystal increases the blocking temperatures and magnetic anisotropies of the nanocrystals with corresponding shifts in AC magnetic susceptibilities, while manganese ferrites are relatively insensitive to the variation in compositions as size and shape dominate over crystal anisotropy. The systematic AC-magnetic characterizations of superparamagnetic Mn_<i>x</i>Fe_3–<i>x</i>O₄ and Co_<i>x</i>Fe_3–<i>x</i>O₄ NCs raise the importance of controlling chemical composition of ferrite NCs for AC magnetic applications

Additional file 1 of Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment

Additional file 1: Supplementary methods

Additional file 2 of Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment

Additional file 2: Fig S1. Gene optimization. Fig S2. Histogram of venetoclax IC50 values from the BeatAML dataset. Fig S3. Optimal rank selection for NMF. Fig S4. Association between the BCL2 family and venetoclax response. Fig S5. Contribution of the BCL2 family to signatures. Fig S6. Identification of BCL2 family-based subtypes in other hematologic malignancies. Fig S7. Mutation status of BCL2 family-based subtypes. Fig S8. Comparison of BCL2 family-based acute myeloid leukaemia (AML) subtypes based on drug response. Fig S9. Assignment of BFSig subtypes in Tavor Dataset. Fig S10. Batch effect correction. Fig S11. External validation of BCL2 family signature-based classifier. Fig S12. Profile of BCL2 family signatures in acute myeloid leukaemia (AML) cell lines. Fig S13. Prediction response to BCL2 family inhibitors in cell lines. Fig S14. Improvement of Prediction Power via Gene Optimization and Rank Selection. Fig S15. Prediction Power using Optimized Genes in External Datasets. Fig S16. Venetoclax Response in NanoString Samples. Fig S17. Relationship between BFL1/MCL1 signature and FAB classification of AML. Fig S18. Correlation between BCL2 family signatures and monocyte signatures. Fig S19. Scheme of gene optimization algorithm. Fig S20. Performance Measurements in Imputation of BCL2 family Profiles

Additional file 4 of Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment

Additional file 4: Table S3. NMF Matrices

Additional file 6 of Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment

Additional file 6: Table S5. Prediction Performance in Cell Line Dataset. Table S6. NanoString Codeset. Table S7. Normalized Counts of NanoString Samples. Table S8. Pre-Collected Genes

Additional file 3 of Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment

Additional file 3: Table S1. Samples Used in Classifier. Table S2. Optimized Genes

Additional file 1 of Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data

Additional file 1: Figures S1-S9