Nanoparticles in the Environment as Revealed by Transmission Electron Microscopy:Detection, Characterisation and Activities
Bibliographic record
Abstract
The characterisation of natural aquatic nanoparticles (especially in relation to flocculation processes, contaminant transport and biogeochemistry) has become an important field of environmental science. Ubiquitous colloid-size microbes and their nanoscale extracellular components affect the chemistry and physical properties of their surroundings in all habitable environments on Earth, thus affecting fundamentally the planets geochemical systems. The adverse health effects of airborne particles, and the atmospheric deposition of particulate contaminants into surface waters, are well recognised environmental issues, with serious questions being posed about the biomedical effects of the nanoparticle component. There is a growing public health concern about nanoparticles in general, as a result of biomedical findings which reveal that atmospheric nanoparticles can present unanticipated toxicity and mechanisms for entering biological cells. The evolving analytical needs, issues, concerns and new facts call for improved means to detect and characterise environmental nanoparticles. Transmission electron microscopy (TEM) is making a major contribution. With foci on aquatic and airborne examples, this review presents literature highlighting nanoparticle relevance to environmental and public health. Common “species” of nanoparticles are described, while characterisation by TEM is considered in terms of apparatus, artifact minimisation and standard protocols for isolation and concentration. Evolving correlative microscopical approaches to characterisation are outlined, along with successful case studies involving heterogeneous environmental samples. Diverse activities of aquatic nanoparticles are featured, with reference to planetary-scale biogeochemical processes and water treatment. Informed speculation is presented on upcoming improvements to nanoparticle characterisation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".