We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Improving interdisciplinary research: Ontology and database to structure characteristics of Microplastics (MP)
AI summary Read the abstract
Researchers developed a formal ontology and linked database to standardize how microplastic particle properties — including morphology, material composition, degradation parameters, eco-coronas, and biofilms — are described and shared across disciplines, with the goal of enabling reproducible, cross-study comparisons in transport, ecotoxicology, and environmental fate research.
Microplastics (MP) are a major environmental pollutant with highly variable properties. They present challenges to society that attract researchers from various disciplines. Important re-search questions include the transport and fate in the environment, the ecological impact and ecotoxicological effects, and the changing properties of MP exposed to different environments. For this question interdisciplinary research is needed which requires a common conceptual approach for discussing MP. Such an ontology connected to a database, that allows interdisciplinary researchers to exchange and harmonize knowledge, is presented here. The ontology structures the key characteristics of MP particles, including particle properties, material properties, parameters of particle degradation, eco-coronas, and biofilms. Additionally, the ontology enables comprehensible and reproducible research through the documentation of manufacturing processes, sample preparation and measurement techniques. A connection to other MP research topics, such as toxicity studies and environmental transport vie unique MP identifiers opens the door to synergistic effects to achieve a deeper understanding of the interplay of MP properties and their behavior and effects.
More Papers Like This
Improving interdisciplinary research: Ontology and database to structure characteristics of Microplastics (MP)
AI summary Read the abstract
Scientists studying microplastics come from many different fields—biology, chemistry, environmental science—and often don't use the same terms or measurement methods, making it hard to combine their findings into a clear picture of health risks. This paper introduces a shared "dictionary" and database system that lets researchers organize and compare data on microplastic size, shape, coating, and breakdown consistently. While this tool doesn't reveal new health effects itself, it's a behind-the-scenes step that could speed up future research connecting microplastic exposure to human health outcomes.
A provenance-aware semantic framework and evidence-aware knowledge graph for micro- and nanoplastic environmental-health evidence
AI summary Read the abstract
Researchers built a computer system to organize and connect scientific studies on micro- and nanoplastics, covering topics like environmental spread, lab toxicity tests, and human exposure studies. The system aims to help link findings from very different types of research together in a structured, searchable way.
Morphological description of microplastic particles for environmental fate studies
AI summary Read the abstract
Researchers proposed a standardized morphological classification system for microplastic particles based on three-dimensional measurements of the smallest enclosing parallelepiped, offering a more objective framework for describing particle shape to improve cross-study comparability.
A proposed nomenclature for microplastic contaminants
AI summary Read the abstract
Researchers propose a standardized naming system for microplastic contaminants to improve consistency across scientific studies. Clear, agreed-upon definitions are essential for comparing data across labs and regions, which is critical for understanding the true scope of microplastic pollution.
research data
AI summary Read the abstract
Researchers compiled a complete microplastic particle dataset from multiple detection studies, providing a comprehensive data resource documenting MP characteristics including size, shape, colour, and polymer type across sampled environmental matrices to support meta-analyses and model development.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.