Convex Cube-Shaped Pt34 Fe5 Ni20 Cu31 Mo9 Ru High Entropy Alloy Catalysts toward High-Performance Multifunctional Electrocatalysis.

High entropy alloy (HEA) catalysts exhibit excellent multifunctional catalytic performance due to the synergistic effect of multi-metal components. However, shape-controlled synthesis of such kinds of HEA catalysts, especially those with high index facets, still faces great challenges, limiting further enhancement of their catalytic performance. Herein, one-pot synthesis of convex cube-shaped Pt34 Fe5 Ni20 Cu31 Mo9 Ru HEA catalysts which possess rich (310) facets and a diagonal crystalline size of 38.5 nm is reported. Transmission electron microscopy measurements indicate cube-shaped nanocrystals are obtained first and further selective growth of pyramid on each facet of the cube-shaped nanocrystal results in a convex cube shape. Pt34 Fe5 Ni20 Cu31 Mo9 Ru HEA catalysts show excellent electrocatalytic performance toward the oxygen reduction reaction (ORR), hydrogen evolution reaction (HER), and oxygen evolution reaction (OER), which exhibit a half-wave potential of 0.87 V for ORR in 0.1 m HClO4 , and overpotential of 20 and 259 mV for HER and OER at a current density of 10 mA cm-2 in an alkaline medium. A control experiment and density functional theory calculation indicate that the Ru element plays a decisive role in both the formation of the convex cube shape and also the high catalytic performance.

Enhancing museum experience through deep learning and multimedia technology.

Amidst the swift progression of artificial intelligence (AI) technology, the museum sector has witnessed a notable inclination towards its adoption. This manuscript endeavours to amplify the interactive milieu of contemporary museum patrons by amalgamating a deep learning algorithm with multimedia technology. The crux of our investigation is the exploration of an adaptive convolutional neural network (CNN) to enrich the interactive engagement of museum visitors. Initially, we leverage the adaptive CNN for the image recognition chore pertaining to museum artifacts and exhibits, thereby facilitating automatic recognition and categorization. Furthermore, to surmount the constraints of conventional pooling algorithms in image feature extraction, we suggest an adaptive pooling algorithm, grounded in the maximum pooling algorithm paradigm. Subsequently, multimedia algorithms are amalgamated into the interactive apparatus, enabling visitors to immerse in exhibits and avail more profound information and experiences. Through juxtaposition with traditional image processing algorithms, the efficacy of our proposed algorithm within a museum ambiance is assessed. Experimental outcomes evince that our algorithm attains superior accuracy and robustness in artifact identification and classification endeavours. In comparison to alternative algorithms, our methodology furnishes more precise and comprehensive displays and interpretations, accurately discerning and categorizing a myriad of exhibit types. This research unveils innovative notions for the digital metamorphosis and advancement of modern museums. Through the incorporation of avant-garde deep learning algorithms and multimedia technologies, the museum visitor experience is elevated, proffering more enthralling and interactive displays. The elucidations of this manuscript hold substantial merit for the continual evolution and innovation within the museum industry.

Tribology and Anti-Ablation Properties of SiC-VN-MoS2/Ta Composite Coatings on Carbon/Carbon Composites from 25 to 800 °C.

To improve the self-lubrication and anti-ablation performances of C/C (carbon/carbon) composites from 25 to 800 °C, we engineered three layers of composite coatings consisting of SiC-VN-MoS2/Ta to deposit on the surface of the C/C composites. The tribology and anti-ablation properties of the composite coatings were experimented under dry sliding wear. The equivalent stress and deformation of the composite coatings are studied. The results show that the CoFs (coefficients of friction) of the C/C composites are decreased by 156% at 800 °C due to the new generated self-lubricating compounds from the MoS2/Ta and VN coating. The anti-ablation of the C/C composites are improved by 25,300% due to the silicon glass, and the generated compounds from V, Mo and Si. The deformation of the C/C substrate under the protection of these coatings looks like a quadrangular star. The cack of the C/C composites is easily generated without the protection from coatings.

Low-pressure UV-initiated synthesis of cationic starch-based flocculant with high flocculation performance.

A kind of starch-based flocculant (starch-graft-poly[(2-methacryloyloxyethyl) trimethyl ammonium chloride], denoted St-g-PDMC-LPUV) has been synthesized by low-pressure ultraviolet initiation and was employed to remove humic acid (HA) for water purification. The physicochemical characteristics of starch and St-g-PDMC-LPUV were characterized by FT-IR, 1H NMR, XRD, TGA, SEM and BET to confirmed the successful grafting DMC onto starch. Effects of flocculant dosage, pH, the adding amount of Fe3O4, initial HA concentration and stirring speed were investigated systematically. The prepared St-g-PDMC-LPUV flocculant with non-toxic, biodegradability and environmental friendliness exhibited effective performance for removing HA from water in a wide pH range (5-10). The flocculation mechanism was attributed to the effective collision between function groups of the St-g-PDMC-LPUV flocculant and HA by charge neutralization, adsorption, bridging and patching.