What does this research mean for the field?

Artificial intelligence significantly enhances the design, optimization, and application of nanomaterials for environmental remediation by improving predictive capabilities, reducing costs, and expediting the creation of high-performing materials. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Synthesize recent advances, methodologies, and limitations in utilizing artificial intelligence to design and optimize nanomaterials for environmental pollution cleanup.

September 15, 2026Discover MaterialsOpen Access

Artificial intelligence guided nanomaterials for sustainable environmental remediation advances and future perspectives

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Authors

SDSelamu DugunaShoolini University AAAbdulahi Mahammed AdemShoolini University MEMahmoud H. Abu ElellaCairo University

Discussion

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Implication

Systematic review reveals artificial intelligence accelerates nanomaterial discovery and optimization for environmental remediation, highlighting critical pathways toward automated pollutant removal.

Key Points

Synthesize recent advances, methodologies, and limitations in utilizing artificial intelligence to design and optimize nanomaterials for environmental pollution cleanup.
Conducted a systematic review adhering to PRISMA 2020 guidelines across Scopus, Web of Science, PubMed, IEEE Xplore, ScienceDirect, and Google Scholar from 2015 to 2026.
Screened 816 candidate records to evaluate 202 eligible peer-reviewed studies focused on AI-driven nanomaterials synthesis, characterization, and application.
AI algorithms—including graph neural networks, reinforcement learning, and Bayesian optimization—substantially improve property prediction, accelerate synthesis, and decrease experimental costs, with the highest concentration of publications occurring between 2023 and 2026.
Persistent constraints including poor data quality, lack of standardized benchmarks, limited model interpretability, and uncertain environmental safety impede practical, large-scale implementation.

Cite This Study

Duguna et al. (2026) studied this question.

synapsesocial.com/papers/6aa913819013453be30a181f https://doi.org/10.1007/s43939-026-00951-6

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Implication

Systematic review reveals artificial intelligence accelerates nanomaterial discovery and optimization for environmental remediation, highlighting critical pathways toward automated pollutant removal.