The electronic coordination configuration of metal active
sites
and the reaction mechanism were investigated by constructing homo-diatomic
Fe sites for visible-light-assisted heterogeneous peroxymonosulfate
(PMS) activation. A novel Fe2N6 catalyst was
synthesized by selecting uniform pyridinic-N of graphitic carbon nitride
(g-C3N4) as anchoring sites. The results demonstrated
that homo-diatomic Fe sites modulated the d-band center and electron
delocalization and thus enhanced the PMS activation kinetics (3.58
times vs single-atom Fe catalyst) with k
obs of 0.111 min–1 owing to the synergistic effect
between adjacent Fe atoms. New Fe–Fe coordination significantly
decreased the contribution of the antibonding state in the Fe–O
bond due to the coupling of the Fe-3d orbitals, which facilitated
the O–O bond cleavage of the Fe2–HOO–SO3 complex with a reduced thermodynamic energy barrier of only
−0.29 eV. This work provided comprehensive mechanistic insights
into developing homo-diatomic catalysts governed by the coordination
configuration and radical pathway for efficient heterogeneous PMS
catalysis.
Oily sludge produced in the process of petroleum exploitation and utilization is a kind of hazardous waste that needs to be urgently dealt with in the petrochemical industry. The oil content of oily sludge is generally between 15–50% and has a great potential for oil resource utilization. However, its composition is complex, in which asphaltene is of high viscosity and difficult to separate. In this study, The oily sludge was extracted with toluene as solvent, supplemented by three kinds of ionic liquids (1-ethyl-3-methylimidazole tetrafluoroborate ([EMIM] [BF4]), 1-ethyl-3-methylimidazole trifluoro-acetate ([EMIM] [TA]), 1-ethyl-3-methylimidazole Dicyandiamide ([EMIM] [N(CN)2])) and three kinds of deep eutectic solutions (choline chloride/urea (ChCl/U), choline chloride / ethylene glycol (ChCl/EG), and choline chloride/malonic acid (ChCl/MA)). This experiment investigates the effect of physicochemical properties of the solvents on oil recovery and three machine learning methods (ridge regression, multilayer perceptron, and support vector regression) are used to predict the association between them. Depending on the linear correlation of variables, it is found that the conductivity of ionic liquid is the key characteristic affecting the extraction treatment in this system.
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