OntoNanoMat: A Semantic Dataset and Ontology for Green-Synthesized Nanomaterials in Environmental Remediation

Background: Research on green-synthesized nanomaterials (GSNs) for environmental remediation is growing rapidly, yet data remains fragmented in non-interoperable formats. Methods: We present OntoNanoMat, a comprehensive semantic resource consisting of a modular OWL 2 DL ontology and a curated dataset of case studies. The data was structured into five thematic modules: Identification, Synthesis, Mechanism, Performance, and Provenance. Results: The dataset is provided in three interoperable formats: CSV for tabular analysis, JSON for web applications, and Turtle (RDF) for Semantic Web integration. Technical validation was performed using SHACL shapes and SPARQL query libraries to ensure logical consistency and data integrity. Conclusions: OntoNanoMat provides a FAIR-compliant (Findable, Accessible, Interoperable, and Reusable) foundation for future machine learning applications and knowledge graph integration in sustainable nanotechnology.