Optimization of iron (III) oxide nanoparticles production from natural waste for use in wastewater applications
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Manjakuppam Malika
Abstract
Research on environmentally friendly synthesis using natural waste materials has gained momentum due to the increasing demand for sustainable nanomaterials. This study proposes a green synthesis approach for Fe2O3 nanoparticles using an extract from Golden Shower Flower as a natural stabilizing agent. The environmental impact of this bio-based synthesis is assessed through a Life Cycle Assessment (LCA) framework, following ISO 14040-44 standards, and is compared to conventional chemical methods. Key findings indicate that this approach significantly reduces environmental impact, including climate change potential (0.002022 kg CO2), fossil depletion (3.17E-07 kg oil eq.), and human toxicity levels, making it a more sustainable alternative. However, potential environmental trade-offs exist due to the energy consumption required for extract preparation and purification. To further optimize the synthesis process, a response function model was developed using Central Composite Design (CCD), achieving a high R2 value of 0.998, confirming strong predictive accuracy. A confirmatory experiment using optimized conditions – 0.03 M precursor concentration, 80.5 min of ultrasonication, and 550 °C calcination – resulted in the production of Fe2O3 nanoparticles with an average size of approximately 15 nm. The synthesized Fe2O3 nanoparticles exhibited excellent performance in wastewater treatment, offering a dual environmental benefit by reducing waste while improving water purification solutions. The uniform particle size distribution and magnetic properties reinforce the potential of waste-derived nanomaterials in driving sustainable nanotechnology. Additionally, they emphasize the importance of scalability, process optimization, and industrial integration in realizing a circular economy-driven approach.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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