Sulfur-doped graphene oxide quantum dots (S-GOQDs) were synthesized and investigated for efficient photocatalytic hydrogen generation application. The UV/Vis, FTIR, and photoluminescence spectra of the synthesized S-GOQDs exhibit three absorption bands at 333, 395, and 524 nm, characteristic of C=S and C-S stretching vibration signals at 1075 and 690 cm , and two excitation-wavelength-independent emission signals with maxima at 451 and 520 nm, respectively, confirming the successful doping of S atom into the GOQDs. Electronic structural analysis suggested that the S-GOQDs exhibit conduction band minimum (CBM) and valence band maximum (VBM) levels suitable for water splitting. Under direct sunlight irradiation, an initial rate of 18 166 μmol h g in pure water and 30 519 μmol h g in 80 % ethanol aqueous solution were obtained. Therefore, metal-free and inexpensive S-GOQDs hold great potential in the development of sustainable and environmentally friendly photocatalysts for efficient hydrogen generation from water splitting.

It is important to develop viable technologies for efficient hydrogen (H2) production and carbon dioxide (CO2) conversion to realize the future supply of clean energy and reduction of global CO2 concentration. Herein, we report a series of carbamate‐substituted ruthenium‐dithiolate complexes 1–3, [Rux(CO)y(μ‐SCH(NCO2(C(CH3)3))CH2CH2S)] (x=2, 3, 6 and y=6, 9, 20), which can effectively catalyze the reversible formic acid‐carbon dioxide (FA‐CO2/H2) cycle that can be used to convert CO2 and produce H2. Complex 2 effectively dehydrogenates FA to produce H2 with an unprecedented turnover frequency (TOF) of 1.15×106 h−1 under conditions of sunlight irradiation. Under conditions of high temperature and pressure, complex 3 hydrogenates CO2 to FA with a high initial TOF of 1.02×106 h−1, resulting in an efficient FA‐CO2/H2 cycle. These results can potentially help provide the platform for the development of technologies that can be used in future to produce clean energy and control environmental threats.

In this paper we devote ourselves to a general growth curve model with power transformation, random effects and AR(1) dependence via a Bayesian approach. Two priors are proposed and both parameter estimation and prediction of future values are considered. Some numerical results with a set of real data are also given.