A facile synthesis is reported of two‐dimensional (2D) bimetallic (Fe/Co=1:2) metal–organic frameworks (MOF, ca. 2.2 nm thick) via simple stirring of the reaction mixture of Fe/Co salts and 1,4‐benzene dicarboxylic acid (1,4‐BDC) in the presence of triethylamine and water at room temperature. The mechanism of the 2D, rather than bulk, MOF was revealed by studying the role of each component in the reaction mixture. It was found that these 2D MOF‐Fe/Co(1:2) exhibited excellent electrocatalytic activity for the oxygen evolution reaction (OER) under basic conditions. The electrocatalytic mechanism was disclosed via both experimental results and density functional theory (DFT) calculation. The 2D morphology and co‐doping of Fe/Co contributed to the superior OER performance of the 2D MOF‐Fe/Co(1:2). The simple and efficient synthetic method is suitable for the mass production and future commercialization of functional 2D MOF with low cost and high yield.
Designing highly efficient and durable electrocatalysts that accelerate sluggish oxygen reduction reaction kinetics for fuel cells and metal–air batteries are highly desirable but challenging. Herein, a facile yet robust strategy is reported to rationally design single iron active centers synergized with local S atoms in metal–organic frameworks derived from hierarchically porous carbon nanorods (Fe/N,S‐HC). The cooperative trithiocyanuric acid‐based coating not only introduces S atoms that regulate the coordination environment of the active centers, but also facilitates the formation of a hierarchically porous structure. Benefiting from electronic modulation and architectural functionality, Fe/N,S‐HC catalyst shows markedly enhanced ORR performance with a half‐wave potential (E1/2) of 0.912 V and satisfactory long‐term durability in alkaline medium, outperforming those of commercial Pt/C. Impressively, Fe/N,S‐HC‐based Zn–air battery also presents outstanding battery performance and long‐term stability. Both electrochemical experimental and density functional theoretical (DFT) calculated results suggest that the FeN4 sites tailored with local S atoms are favorable for the adsorption/desorption of oxygen intermediate, resulting in lower activation energy barrier and ultraefficient oxygen reduction catalytic activity. This work provides an atomic‐level combined with porous morphological‐level insights into oxygen reduction catalytic property, promoting rational design and development of novel highly efficient single‐atom catalysts for the renewable energy applications.
In
this work, a novel strategy that combined molecular thermodynamic
and machine learning was proposed to accurately predict the solubility
of drugs in various solvents. The strategy was based on 16 molecular
descriptors representing drug–drug interactions and drug–solvent
interactions including physical parameters, pure perturbed-chain statistical
associating fluid theory (PC-SAFT) parameters of drugs and solvents,
and mixing rules. These molecular descriptors were inputted into five
machine learning algorithms [multiple linear regression (MLR), artificial
neural network (ANN), random forest (RF), extremely randomized trees
(ET), and support vector machine (SVM)] to train the predictive model.
A single-hidden-layer neural network was finally determined as the
predictive model for predicting the solubility of drugs in various
solvents. The drug solubility in the generalization evaluation set
has also been successfully predicted, which indicates the good prediction
performance of the model. Three directions for improving the model
were summarized as adding molecular descriptors of drug–solvent
interactions in the water system and drug–drug interactions
in the organic solvent system and expanding the dataset to adequately
obtain the features of multiple drugs. These findings show that the
proposed model has the capability of solubility prediction, which
is expected to provide important information for drug development
and drug solvent screening.
The fluorescence-lifetime
imaging microscopy (FLIM) technique is
utilized to probe the photoluminescence properties of individual MoS2 flakes. This measurement allows identification of the layer
number of the flakes: two fluorescence decay lifetimes (τ1 and τ2) exhibit linear relationships with
the layer number. Our investigation of the fluorescence lifetime reveals
exciton dynamics in monolayer and multilayers MoS2. We
find the distinct difference on the decay rates between A exciton
(fast) and B exciton (slow). K′/Γ emission has different
decay behaviors with respect to the layer number (N) because of its variable energy in monolayer and multilayer samples.
The interplay of these transition channels also plays an important
impact on the overall decay. Our results demonstrate that FLIM is
an effective measurement for studying the luminescence properties
of transition metal dichalcogenides.
Metal-free photocatalysts with excellent visible-light absorption and highly efficient photocatalytic activity are attractive in the field of photocatalysis owing to their environmental friendliness. Black phosphorus (BP) shows a great potential in photoelectric conversion and photocatalysis due to its tunable band gap and two-dimensional structure. In this work, a stabilized metal-free photocatalyst, reduced graphene oxide (rGO)-wrapped BP heterostructure, was prepared by assembling BP and GO nanosheets in aqueous solution followed by partial reduction and lyophilization. The surface tension of the partially reduced GO during lyophilization could make rGO nanosheets tightly wrap on both surfaces of exfoliated BP nanosheets. This wrapped heterostructure with tight bonding between rGO and BP nanosheets led to a high photocatalytic activity, owing to the rapid transfer of the photogenerated electron−hole pairs at the rGO/BP heterojunction and the high stability of rGO protecting BP from oxygen attack. This work not only provided a general method to prepare the sandwiched heterojunction based on GO with good interface binding capability but also constructed a highly active, stable, metal-free photocatalyst based on BP.
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