Exploring Environmental Nanoplastics Research: Networks and Evolutionary Trends
Bibliographic record
Abstract
Abstract Analyzing scientific advances and networks in NPs research can provide valuable insights into the evolving trends, research gaps, and priorities for future research efforts, highlighting the importance of scientific research in pollution control and risk management of uncontrolled and unknown nanoplastics (NPs) that pose a potential global threat, and have raised concerns in the scientific community and media. A total of 2055 nanoplastics (NPs) studies published from 1995 onwards were retrieved from the Web of Science Core Collection database. Bibliometric methods were applied to assess evolving scientific advances and networks. The general term, “nanoplastics,” was first introduced in 1995 as “intelligent” materials. Before 2009, defined as the ambiguous stage, NPs were produced and applied in many different manufacturing areas and processes. The first research referring to nano-scale plastic particles/debris as potential hazardous contaminants appeared in 2010. Thereafter, the number of annual publications on NPs has increased rapidly, particularly from 2018 onwards. Results showed China published 822 scientific papers, overtaking the United States’ 229 papers, whereas European researches, i.e., the Netherlands, Portugal, German, and the United Kingdom, led in quality and citation with extensive international collaborations. Furthermore, we concluded three main research themes from keyword cluster analysis: environmental monitoring (identification, quantification, fresh-water, marine-environment); environmental behaviors (fate, adsorption, aggregation, transport); and toxicology (toxicity, exposure, ingestion, oxidative stress). Toxicology and environmental behaviors of NPs were the leading themes. An overview of the current understanding of NPs in the above three major themes provides perspectives to identify future research directions based on knowledge gaps, e.g., advancing analytical methods, and exploring the mobility and fate of NPs in different ecosystems. Scientific research on NPs is a key fundamental requirement for their pollution control and risk management. To bridge the gap between research and reality, future efforts are required to promote the dissemination of scientific research findings and encourage actions in engineering, policy, education, etc., to support a sustainable society. Graphical Abstract
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".