Most of the synthetic chemical transformation reactions involve the use of different organic solvents. Unfortunately, some of these toxic solvents are used in chemical laboratory, industry and have been considered a very serious problem for the health, safety of workers and environmental damage through pollution. The purpose of green chemistry is to provide a path that reduces or eliminates the use of such hazardous toxic solvents. Therefore, the key factor of the green synthetic approach is to utilize renewable materials, nontoxic chemical and to perform the reactions under solvent-free conditions. In this review, we have discussed most recent literature survey on applications of solvent-free techniques in organic synthesis which would offer a new opportunity to a researcher to overcome the problem of using environmental harmful solvents.

A simple, rapid, efficient, and environmentally benign procedure for synthesis of 2-pyrazoline derivatives has been achieved by reacting 2 ? -hydroxychalcones with hydrazine hydrate under solvent-free grinding technique.The short reaction time, cleaner reaction, easy workup, higher yields, and mild reaction conditions make this protocol practical and economically attractive in comparison with the classical reaction.

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A novel series of pyrazolo [1,5-a] pyrimidines were synthesized by the condensation of substituted chalcones with 5-amino pyrazole in presence of dimethyl formamide. All the synthesized products were characterized by the spectral analysis. Further, all newly synthesized compounds were screened for their antimicrobial activity. Most of the compounds showed potent activity.

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Background: Non-negative Matrix Factorization (NMF) has been extensively used in gene expression data. However, most NMF-based methods have single-layer structures, which may achieve poor performance for complex data. Deep learning, with its carefully designed hierarchical structure, has shown significant advantages in learning data features. Objective: In bioinformatics, on the one hand, to discover differentially expressed genes in gene expression data; on the other hand, to obtain higher sample clustering results. It can provide the reference value for the prevention and treatment of cancer. Method: In this paper, we apply a deep NMF method called Deep Semi-NMF on the integrated gene expression data. In each layer, the coefficient matrix is directly decomposed into the basic and coefficient matrix of the next layer. We apply this factorization model on The Cancer Genome Atlas (TCGA) genomic data. Results: The experimental results demonstrate the superiority of Deep Semi-NMF method in identifying differentially expressed genes and clustering samples. Conclusion: The Deep Semi-NMF model decomposes a matrix into multiple matrices and multiplies them to form a matrix. It can also improve the clustering performance of samples while digging out more accurate key genes for disease treatment.