ORCID
- Amit K. Jaiswal: 0000-0002-4551-4182
Abstract
Conventional wastewater treatment performs reliably against routine contaminants but remains inconsistent for complex industrial effluents and for emerging pollutants such as pharmaceutical residues, per- and polyfluoroalkyl substances and micro/nano plastics. Engineered nanomaterials and artificial intelligence (AI)/machine learning (ML) have each been reviewed extensively, but predominantly in isolation. The present review is distinguished from existing reviews in three specific respects. First, the nanomaterial-AI interface is treated as the unit of analysis rather than the two fields being surveyed in parallel: AI-guided material selection, dosing and pH control, membrane fouling prediction, nano sensor-assisted monitoring and regeneration scheduling are examined together. Second, evidence published between 2023 and 2026 is prioritized, with earlier work retained only where it remains the primary source for mechanistic or environmental-fate claims. Third, an implementation-oriented appraisal is applied to industrial wastewater, in which reported performance is evaluated alongside dataset provenance, validation design, regeneration durability, nanoparticle release and post-treatment environmental fate. Metallic and metal-oxide nanoparticles, carbon nanotubes, graphene derivatives, metal-organic frameworks and biologically synthesized nanoparticles are compared by removal mechanisms (adsorption, photocatalysis, reduction and antimicrobial action) and by configuration within membranes, fixed-bed reactors, nano filters and hybrid bioreactors. Quantitative claims are reported as class-specific ranges with the measurement or validation method stated, since both specific surface area and reported model accuracy are strongly method-dependent and are not transferable between material classes or dataset types. The analysis indicates that the principal constraints on deployment are not predictive accuracy but dataset generalizability, colloidal and chemical stability during prolonged operation, quantified particle and ion release, and the absence of standardized monitoring and reporting frameworks. Research priorities are identified accordingly.
Keywords
Artificial intelligence, Environmental biotechnology, Machine learning, Nanomaterials, Nanotechnology, Wastewater bioremediation
DOI Link
Publication Date
2026-01-01
Publication Title
Chemical Engineering Journal Advances
Volume
27
Deposit Date
2026-08-25
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Tiwari, Ananya; Dharsandia, Isha; Tiwari, Shreedhar; Vishwakarma, Gajendra Singh; Tiwari, Budhi Sagar; Kumar, Narendra; and Jaiswal, Amit K., "Multifunctional Nanomaterials Coupled with AI-Based Optimization for Advanced Wastewater Treatment" (2026). Research Outputs: 2025-Present. 72.
https://arrow.tudublin.ie/schfsehro/72