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Open Access

ISSN 2689-5846

© 2025 by the authors; CC BY 4.0 licence

Recent Progress in Materials , Volume 7 , Issue 4 (2025)

Pages: 31

Published: January 2026

(This book is a printed edition that was published in  Recent Progress in Materials)

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Cover Story: This work describes the rapid synthesis of silver nanoparticles and alginate-silver nanocomposites in the form spherical beads using a Bahraini medicinal herb mashmoom (Ocimum basilicum) for environmental applications.  The study highlights the importance of green synthesis in using common household herbs in the development of advanced materials for targeted applications. View this paper

Volume 7,Issue 4

Open Access Original Research

Rapid Green Synthesis of Silver Nanoparticles and Alginate-Silver Nanocomposite Beads Using Mashmoom (Ocimum basilicum), a Bahraini Household Medicinal Herb

Received: 16 June 2025;  Published: 17 November 2025;  doi: 10.21926/rpm.2504017

Abstract

The study focuses on the rapid green synthesis of silver nanoparticles (AgNPs) and alginate-silver nanocomposite (Alg-AgNc) beads using sweet basil or Mashmoom (Ocimum basilicum), a common household medicinal herb in Bahrain. The aqueous extract of mashmoom was used as a reducing and stabilizing agent in the reduction of the precursor salt, silver nitrate, and formation of stable AgNPs. The Alg-AgNc beads were prepared by ionotropic gelation of sodium alginate-AgNPs solution [...]

Open Access Original Research

Deep Learning Approaches for Predicting Strain Energy in Heterogeneous Materials

Received: 15 July 2025;  Published: 24 October 2025;  doi: 10.21926/rpm.2504016

Abstract

This paper investigates data-driven strain energy prediction for heterogeneous elastic materials using the Mechanical MNIST benchmark dataset (60,000 28 × 28 stiffness maps by precomputed finite-element results). The results by classical regression models (linear regression, random forest, and gradient boosting) using hand-crafted features were compared with the results by the deep learning models (Convolutional neural network (CNN) and residual network (ResNet)) trained end-to-end on images. In [...]
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