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 two illustrative case studies serving as proof-of-concept demonstrations. The data was structured into five thematic modules: Identification, Synthesis, Mechanism, Performance, and Provenance. Results: The dataset is provided in three interoperable formats: CSV, JSON, and Turtle (RDF) and is validated through SHACL shapes to ensure structural integrity and FAIR compliance. Conclusions: OntoNanoMat provides a FAIR-compliant (Findable, Accessible, Interoperable, and Reusable) foundation for future machine learning applications and knowledge graph integration in sustainable nanotechnology.