Smart Nanomaterials and AI-Enhanced Technologies for Environmental Separation and Sensing

Clean water, air, and soil are essential resources that play a critical role in the economic, social, and cultural development of nations. Today, widespread issues such as wastewater discharge, air pollution, and soil contamination have created a growing demand for advanced technologies that support a sustainable environmental ecosystem. Recent studies have highlighted the use of smart materials and nanomaterials such as functionalized adsorbents, superhydrophobic surfaces, and magnetic composites for selective pollutant capture and separation across diverse applications. Integrating these materials with artificial intelligence (AI) can further accelerate advancements in material design and environmental monitoring by enabling accurate property prediction, real-time sensing, and system optimization. Emerging adaptive AI methodologies can be effectively applied alongside nanotechnology, separation techniques, and sensing systems, ushering in a new era of intelligent environmental solutions. This interdisciplinary field, which brings together engineering, science, and data, is rapidly progressing toward scalable and efficient technologies for environmental protection and resource recovery.

The main goal of this Research Topic is using new technology development, including using smart materials or nanomaterials and also using the application of AI technology in this field, in order to tackle common issues such as wastewater, air pollution, and soil contamination to build a sustainable environmental ecosystem. Conventional strategies face limitations such as high energy consumption, low specificity, and poor adaptability. Recent progress in smart materials has led to more selective pollutant-targeting systems, while artificial intelligence offers powerful tools for predictive modeling, real-time sensing, and material optimization in environmental applications. By using AI, we can overcome current limitations in environmental remediation, moving toward intelligent systems that not only help in the synthesis of materials and their optimization but also can be used to detect and capture pollutants and adapt in real time to changing conditions.

We aim to accelerate this interdisciplinary collaboration to create a bridge across different fields of engineering, science, and AI by using new AI methodologies for the development of robust, cost-effective solutions for water purification, air filtration, and soil decontamination, and beyond this, for sensing and monitoring.

We welcome Original Research, Review, Mini Review and Perspective articles on themes including, but not limited to:

• Synthesis and characterization of functional nanomaterials
• Development of superhydrophobic, magnetic, or multifunctional nanomaterials for environmental remediation, pollutant capture, and wastewater treatment.
• Use of AI for material discovery, property prediction, and optimization in separation processes.
• Computational modeling of adsorption, diffusion, and separation processes using AI.
• Integration of smart materials into scalable platforms for water purification, air filtration, or gas separation.
• Application of sustainable materials derived from biomass, waste, or green chemistry principles in environmental remediation.
• Design of AI-assisted environmental sensors
• Hybrid separation systems combining nanotechnology, AI, and real-time sensing for adaptive pollution control in dynamic environments.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Smart Nanomaterials, artificial intelligence (AI), AI-Driven Modeling, Pollutant Sensing, Environmental Applications

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.