Highly efficient removal of heavy metals from wastewater by MnO2-NP-CPC and γ-Fe2O3-NP-CPC nanomaterials: modeling and optimization with machine learning (Artificial Neural Network).

Highly efficient removal of heavy metals from wastewater by MnO2-NP-CPC and γ-Fe2O3-NP-CPC nanomaterials: modeling and optimization with machine learning (Artificial Neural Network). | 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 Highly efficient removal of heavy metals from wastewater by MnO2-NP-CPC and γ-Fe2O3-NP-CPC nanomaterials: modeling and optimization with machine learning (Artificial Neural Network). Ayoub Belcaid, Buscotin Horax Beakou, Saad Bouhsina, Abdellah Anouar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4022153/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this study, two nanomaterials with excellent adsorption capacities were developed to remove chromium (VI) and cobalt (II) heavy metals efficiently from wastewater. MnO2-NP-CPC and γ-Fe2O3-NP-CPC nanomaterials were successfully synthesized using an agricultural waste which is cassava peels, and characterized by different techniques namely FTIR, XRD, BET, SEM, and EDX analysis. The experimental tests for the adsorption process were done in a batch system, and the influence of various parameters such as temperature, initial concentration, pH, and contact time on the biosorption of cobalt (II) and chromium (VI) were fully investigated. Furthermore, the Qmax were 546,32 mg/g and 349,59 mg/g for chromium (VI) and cobalt (II) respectively. The results fitted well the monolayer Langmuir with the pseudo-second-order model, revealing that chemisorption controls heavy metals removal, while the thermodynamic sorption was an endothermic and spontaneous reaction. Artificial Neural Network (ANN) model was developed to predict as well as to simulate the experimental results, for this purpose, the proposed model was based on five independent inputs or variables and one output or response which is the predicted adsorbed amount of Cr (VI) and Co (II), the predicted results were in good agreement with the experimental values, indeed the proposed ANN model showed an appreciable prediction accuracy with high optimization ability for chromium (VI) and cobalt (II) removal. Hence the present work has great potential for the industrial and environmental applications of biochar and nanomaterials especially in wastewater treatment and green chemistry. Adsorption MnO2-NP-CPC γ-Fe2O3-NP-CPC Chromium (VI) Cobalt (II) Artificial Neural Network Full Text Supplementary Files supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4022153","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281928942,"identity":"9ca79493-4adb-428a-9893-a182bf94a8cc","order_by":0,"name":"Ayoub 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