Ni formed by photo-reduction of Ni(ii) within 30 min and the ratio of Ni/g-C3N4 can be adjusted conveniently.

No abstract

Presently, precise deposition of transition metal phosphides at the electron outlet of photoactive materials as cocatalysts for hydrogen generation is rarely reported. Demonstrated here is a general photochemical strategy for the preparation of metal phosphides as cocatalysts for hydrogen generation. In this work, Ni x P was successfully deposited on g-C 3 N 4 using nickel sulfate (NiSO 4 ) and hypophosphite sodium (NaH 2 PO 2 ) as Ni and P sources, respectively. The Ni x P/g-C 3 N 4 composite exhibits excellent performance of hydrogen evolution via water splitting (about 8585 μmol g −1 h −1 in a triethanolamine aqueous solution under a 300 W Xe lamp with an AM 1.5G filter). More importantly, due to the intrinsic acid-resistant properties of Ni x P and g-C 3 N 4 , the Ni x P/g-C 3 N 4 composite demonstrated excellent acid-stable photocatalytic activity for H 2 evolution (stable run for 75 h under acidic solution of pH 2). Furthermore, the mechanism for photocatalytic activity of g-C 3 N 4 enhanced by Ni x P was investigated in detail by steady-state photoluminescence spectra and surface photovoltage spectra, which indicated that separation efficiency of photogenerated carriers from g-C 3 N 4 was effectively enhanced by Ni x P.

No abstract

In multivariate calibration using a spectral dataset, it is difficult to optimize nonsystematic parameters in a quantitative model, i.e., spectral pretreatment, latent factors and variable selection. In this study, we describe a novel and systematic approach that uses a processing trajectory to select three parameters including different spectral pretreatments, variable importance in the projection (VIP) for variable selection and latent factors in the Partial Least-Square (PLS) model. The root mean square errors of calibration (RMSEC), the root mean square errors of prediction (RMSEP), the ratio of standard error of prediction to standard deviation (RPD), and the determination coefficient of calibration (Rcal2) and validation (Rpre2) were simultaneously assessed to optimize the best modeling path. We used three different near-infrared (NIR) datasets, which illustrated that there was more than one modeling path to ensure good modeling. The PLS model optimizes modeling parameters step-by-step, but the robust model described here demonstrates better efficiency than other published papers.