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At the frontier of electrocatalysis and heterogeneous reactions, significant effort has been devoted to Pt-based nanomaterials owing to their advantages of tunable morphology and excellent catalytic properties. In contrast to Ptbased nanocatalysts with other morphologies, nanowire catalysts, especially 1D ultrafine nanowire (NW) structure, are garnering increased attention because of their advantages of high atomic efficiency, intrinsic isotropy, rich high-index facets, better conductivity, robust structure stability for prohibiting dissolution, ripening, and aggregation. Regardless of these advantages, it is still challenging to realize the precise control of ultrafine Pt-based NWs in terms of their size, crystal phase structure, and composition. Aiming to synthesize advanced ultrafine Pt-based NWs catalysts with higher activity, durability, and selectivity toward catalytic reactions, this review summarizes the recently available approaches for improving the catalytic performance of ultrafine Pt-based NWs with detailed guidance. A summary of recent progress in ultrafine Pt-based NWs catalysts for advanced catalysis and heterogeneous reactions is also provided. Furthermore, integrated experimental and theoretical studies are reviewed to explain the activity, stability, and selectivity enhancement mechanism. In the final section, the challenges and outlook are also discussed to provide guidance for the rational engineering of efficient ultrafine Pt-based NWs catalysts for applications in renewable-energy-related devices.

In this paper, a 5,10,15,20-tetrakis(4-(hydroxyl)phenyl) porphyrin (TPPH) noncovalently functionalized reduced graphene oxide (RGO) nanohybrid has been facilely synthesized by immobilizing TPPH on RGO nanosheets. This nanohybrid was characterized by atomic force microscopy (AFM), transmission electron microscopy (TEM), and UV-vis spectra, which demonstrated that the TPPH molecule was attached on the surface of the graphene nanosheet. The results of fluorescence quenching and photocurrent enhancement of TPPH-RGO exhibit that the fast electrons transfer from photoexcited TPPH molecules to RGO sheets. Compared with bare TPPH or RGO functional Pt nanoparticles, the TPPH-sensitized RGO loaded with Pt nanoparticles shows remarkable enhanced photocatalytic activity under UV-vis light irradiation. The superior electron-accepting and electron-transporting properties of graphene greatly accelerate the electron transfer from excited TPPH to Pt catalysts, which promote the photocatalytic activity for hydrogen evolution. More importantly, with the assistance of cetyltrimethylammonium bromide (CTAB) surfactant, the catalytic activity and stability is further improved owing to aggregation prevention of TPPH-RGO nanocomposites. Our investigation might not only initiate new opportunities for the development of a facile synthesis yet highly efficient photoinduced hydrogen evolution system (composed of organic dye functionalized graphene) but also pave a new avenue for constructing graphene-based matericals with enhanced catalytic performance and stability under surfactant assistance.

A new simple and effective method named Monte Carlo cross validation (MCCV) has been introduced and evaluated for selecting a model and estimating the prediction ability of the model selected. Unlike the leave-one-out procedure widely used in chemometrics for cross-validation (CV), the Monte Carlo cross-validation developed in this paper is an asymptotically consistent method of model selection. It can avoid an unnecessarily large model and therefore decreases the risk of overfitting of the model. The results obtained from a simulation study showed that MCCV has an obviously larger probability than leave-one-out CV (LOO-CV) of selecting the model with best prediction ability and that a corrected MCCV (CMCCV) could give a more accurate estimation of prediction ability than LOO-CV or MCCV. The results obtained with real data sets demonstrated that MCCV could successfully select an appropriate model and that CMCCV could assess the prediction ability of the selected model with satisfactory accuracy.

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