CO Activation Using Nitrogen-Doped Carbon Nanotubes for Reductive Carbonylation of Nitroaromatics to Benzimidazolinone and Phenyl Urea

Low-temperature NH3 abatement via selective oxidation over a supported copper catalyst with high Cu+ abundance

Selective catalytic NH3-to-N2 oxidation (NH3-SCO) is highly promising for abating NH3 emissions slipped from stationary flue gas after-treatment devices. Its practical application, however, is limited by the non-availability of low-cost catalysts with high activity and N2 selectivity. Here, using defect-rich nitrogen-doped carbon nanotubes (NCNT-AW) as the support, we developed a highly active and durable copper-based NH3-SCO catalyst with a high abundance of cuprous (Cu+) sites. The obtained Cu/NCNT-AW catalyst demonstrated outstanding activity with a T50 (i.e. the temperature to reach 50% NH3 conversion) of 174°C in the NH3-SCO reaction, which outperformed not only the Cu catalyst supported on N-free O-functionalized CNTs (OCNTs) or NCNT with less surface defects, but also those most active Cu catalysts in open literature. Reaction kinetics measurements and temperature-programmed surface reactions using NH3 as a probe molecule revealed that the NH3-SCO reaction on Cu/NCNT-AW follows an internal selective catalytic reaction (i-SCR) route involving nitric oxide (NO) as a key intermediate. According to mechanistic investigations by X-ray photoelectron spectroscopy, Raman spectroscopy, and X-ray absorption spectroscopy, the superior NH3-SCO performance of Cu/NCNT-AW originated from a synergy of surface defects and N-dopants. Specifically, surface defects promoted the anchoring of CuO nanoparticles on N-containing sites and, thereby, enabled efficient electron transfer from N to CuO, increasing significantly the fraction of SCR-active Cu+ sites in the catalyst. This study puts forward a new idea for manipulating and utilizing the interplay of defects and N-dopants on carbon surfaces to fabricate Cu+-rich Cu catalysts for efficient abatement of slip NH3 emissions via selective oxidation.

A new synthetic approach to cyclic ureas through carbonylation using di-tert-butyl dicarbonate (boc anhydride) via one pot strategy

A new approach has been successfully employed to synthesize cyclic ureas via carbonylation, utilizing Boc anhydride and employing K2CO3 as a base along with N,N-dimethylformamide as the solvent. Remarkably high yields were achieved using K2CO3 in conjunction with (Boc)2O, enabling the streamlined preparation of benzimidazolones and 2-benzoxazolones within a single reaction vessel. Significantly, this approach obviates the necessity for using any dangerous reagents, rendering it environmentally friendly, and its key benefit lies in being a metal-free system. The method stands out for its efficiency, concise pathway, optimization from readily accessible starting materials, and ease of execution. The resulting benzimidazolones and 2-benzoxazolones were thoroughly characterized using techniques including LCMS, 1H NMR, and 13C NMR.

Catalytic utilization of converter gas – an industrial waste for the synthesis of pharmaceuticals

From waste to value. An efficient and convenient ruthenium-catalyzed reduction of aromatic nitro compounds using converter gas as a reducing agent to produce valuable pharmaceuticals has been developed.

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD₅₀). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R² = 0.88), validation set (R² = 0.95), and true external test set (R² = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.