SN2 Reaction at the Amide Nitrogen Center Enables Hydrazide Synthesis.

Nucleophilic substitutions are fundamentally important transformations in synthetic organic chemistry. Despite the substantial advances in bimolecular nucleophilic substitutions (SN2) at saturated carbon centers, analogous SN2 reaction at the amide nitrogen atom remains extremely limited. Here we report an SN2 substitution method at the amide nitrogen atom with amine nucleophiles for nitrogen-nitrogen (N-N) bond formation that leads to a novel strategy toward biologically and medicinally important hydrazide derivatives. We found the use of sulfonate-leaving groups at the amide nitrogen atom played a pivotal role in the reaction. This new N-N coupling reaction allows the use of O-tosyl hydroxamates as electrophiles and readily available amines, including acyclic aliphatic amines and saturated N-heterocycles as nucleophiles. The reaction features mild conditions, broad substrate scope (>80 examples), excellent functional group tolerability, and scalability. The method is applicable to late-stage modification of various approved drug molecules, thus enabling complex hydrazide scaffold synthesis.

Residual current detection method based on improved VMD-BPNN.

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution.