Exploring environmental nanobiogeochemistry using field-flow fractionation and ICP-MS-based tools: progress and frontiers
Bibliographic record
Abstract
The recent application of sophisticated instrumentation and novel experimental techniques to environmental systems has driven the study of natural nanoparticles and nanoparticle systems towards new horizons. Moving beyond the detection of engineered nanoparticles in natural systems, these technologies create new knowledge about the composition, behaviour, and functions of natural nanoparticles as individual entities and particle systems. In this perspective article, we describe the progress and frontiers in this research area using case studies drawn from a range of published and unpublished data spanning diverse environmental systems. The companion paper Exploring environmental nanobiogeochemistry using field-flow fractionation and ICP-MS-based tools: background and fundamentals defines the emerging field of environmental nanobiogeochemistry and describes the fundamentals, optimization, advantages, and disadvantages of field-flow fractionation and ICP-MS-based techniques for advancing our understanding of natural nanoscale particles and particle systems. Thus, by combining the necessary background with the most recent findings and key challenges, these contributions provide key knowledge for new and established researchers entering this exciting field and lay the groundwork for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".