Souza, Fernando Gomes., Bhansali, Shekhar., & Thundat, Thomas G. (2025). Harmfulness Score: A Data-Driven Framework for Ranking Environmental Risks of Microplastics. Macromolecular Rapid Communications. Advance online publication. https://doi.org/10.1002/marc.202500559
In this study, researchers analyzed 104,471 scientific abstracts about microplastics and nanoplastics using both bibliometric tools (methods for studying patterns in scientific publications) and machine learning models. This allowed them to map out major research themes and identify which plastic materials are most often linked to potential health and environmental risks.
They created a combined Harmfulness Score by using sentiment analysis, descriptions of harmful effects, and measures of how central certain terms are in scientific networks. Using this score, polystyrene (PS) and polyethylene (PE) ranked as the plastics most frequently associated with terms like oxidative stress, cytotoxicity, and genotoxicity, indicating that they are discussed more often in connection with harmful biological effects.
The analysis also revealed that important physicochemical details—such as particle size, density, and surface area—were rarely reported across studies (only 3.91%, 0.01%, and less than 0.01% of abstracts, respectively). This lack of information makes it harder to build accurate computer models or conduct reliable risk assessments.
Thematic clustering showed that current research focuses heavily on environmental policy and biological impacts of microplastics, while newer areas such as microbial and enzymatic degradation and legal-policy connections are rapidly emerging.
Overall, the findings point to a clear need for more standardized reporting practices and broader use of consistent analytical frameworks to improve the reliability of research and support better policymaking.
FIGURE 1
Co-occurrence network of terms in microplastic and nanoplastic research from 1961 to 2025, generated using VOSviewer. (a) Network visualization map showing clustered thematic areas based on term co-occurrence frequency and total link strength. (b) Overlay visualization map displaying the average publication year of terms, highlighting temporal trends and emerging research topics.