Author

Ankush Mehta

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Open access Sep 2026

Poly(lactic acid) hybrid nanocomposites with synergistic cellulose nanocrystal-carbon reinforcement for sustainable packaging and multifunctional applications

Sustainable and high-performance polymer composites are needed for advanced technological applications. This study outlines the straightforward synthesis and comprehensive analysis of novel bio-nanocomposites based on poly(lactic acid) (PLA) that are reinforced in a combined way with cellulose nanocrystals (CNC) and a hybrid filler system comprising carbon nanotubes (CNTs) and reduced graphene oxide (rGO). The addition of CNTs and rGO resulted in improved crystallinity. FTIR analysis indicated changes in the local chemical environment consistent with noncovalent matrix–nanofiller interactions. Differential Scanning Calorimetry (DSC) demonstrated that 0.50 wt% rGO exhibited the most favorable thermal behavior, as well as significantly enhanced crystallinity. Thermogravimetric analysis (TGA) demonstrated that nanocomposites exhibited enhanced thermal stability by the incorporation of nanofillers. The contact angle rising from 56.2° in PLA/CNC to 82.9° in 1 wt% rGO loading showed enhancement in hydrophobicity. Lysozyme-assisted degradation tests revealed slower degradation rates in composites loaded with nanofillers, which indicates improved resistance to enzymatic breakdown. A substantial decrease in water vapor permeability (WVP) and oxygen permeability (OP) study revealed improving barrier characteristics. The Alamar blue assay was carried out to study the cytotoxicity of hMSCs (human mesenchymal stem cells). The 0.25 wt% CNT-loaded composite showed a highly significant increase in cell survival by day 7 (p < 0.001). This study demonstrates a route for developing PLA nanocomposites with tunable structural, thermal, barrier, surface, and degradation properties.

Kartikey Verma, S. Siddiki, C. Maity et al. · RSC Advances · 0 citations

Review Open access Aug 2026

AI-aided metaheuristic optimized framework for job sequencing and machine failure prediction in T-shirt production system

This research proposes a study on AI-assisted optimized Job-Shop Production System (JPS) to improve performance, reduce cost and time, and detect machine failure. In the current industrial landscape, optimizing Job Shop Production Systems (JPS) is critical for achieving higher efficiency, reduced costs, and improved resource utilization. This study presents a comprehensive review of both conventional and artificial intelligence (AI)/machine learning (ML)-based approaches applied to JPS, with particular emphasis on job sequencing and machine failure prediction. The system is modeled as a multi-stage job-shop with diverse tasks and constraints, highlighting challenges in sequencing, resource allocation, and machine reliability. This work addresses job-shop scheduling in a T-shirt manufacturing system using Grey Wolf Optimization (GWO). While conventional GWO provides feasible job sequencing, it suffers from premature convergence. An Improved Grey Wolf Optimization (IGWO) algorithm is therefore proposed to enhance exploration and convergence efficiency. Simulation results show that IGWO achieves better job sequencing with reduced make-span, lower production cost, and improved resource utilization compared to standard GWO.

Munish Kumar, Ravinder Tonk, Shahbaz Juneja et al. · Discover Mechanical Engineer... · 0 citations