Optimizing Nanomaterial Selection for Wastewater Treatment: A Multi-Criteria Analysis of Adsorption Efficiency for Methylene Blue and Heavy Metals

Optimizing Nanomaterial Selection for Wastewater Treatment: A Multi-Criteria Analysis of Adsorption Efficiency for Methylene Blue and Heavy Metals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimizing Nanomaterial Selection for Wastewater Treatment: A Multi-Criteria Analysis of Adsorption Efficiency for Methylene Blue and Heavy Metals Amine Zribi, Levente Csóka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6905036/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jan, 2026 Read the published version in Discover Water → Version 1 posted 12 You are reading this latest preprint version Abstract Nanotechnology offers promising solutions for wastewater treatment owing to the distinctive physicochemical properties of nanomaterials. However, selecting the most effective nanomaterial for a specific application remains challenging. This study employs multi-criteria decision-making (MCDM) methodologies to facilitate an informed selection process for nanomaterials in wastewater treatment, with a particular focus on the removal of heavy metals from industrial wastewater. A comprehensive literature review was conducted to identify the critical factors influencing nanomaterial performance in wastewater treatment. The entropy–Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods were used to quantitatively evaluate these factors. The key factors identified were efficiency, adsorption capacity and contact time. The MCDM analysis provided a quantitative ranking of nanomaterials based on their performance in heavy metal removal from industrial wastewater. This study offers an objective approach to guide the selection of cost-effective and efficient nanomaterials for wastewater treatment. The findings aim to support professionals in making data-driven decisions, ultimately improving treatment processes and outcomes. Nanomaterial Nanotechnology Wastewater treatment MCDM Entropy – SAW Entropy – TOPSIS Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFileWastewaterTreatmentZribi.docx Cite Share Download PDF Status: Published Journal Publication published 12 Jan, 2026 Read the published version in Discover Water → Version 1 posted Editorial decision: Revision requested 24 Jul, 2025 Reviews received at journal 21 Jul, 2025 Reviews received at journal 18 Jul, 2025 Reviews received at journal 11 Jul, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers invited by journal 26 Jun, 2025 Editor assigned by journal 19 Jun, 2025 Submission checks completed at journal 19 Jun, 2025 First submitted to journal 16 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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