Soft X‐ray spectromicroscopy for speciation, quantitation and nano‐eco‐toxicology of nanomaterials
Bibliographic record
Abstract
There is a critical need for methods that provide simultaneous detection, identification, quantitation and visualization of nanomaterials at their interface with biological and environmental systems. The approach should allow speciation as well as elemental analysis. Using the intrinsic X-ray absorption properties, soft X-ray scanning transmission X-ray spectromicroscopy (STXM) allows characterization and imaging of a broad range of nanomaterials, including metals, oxides and organic materials, and at the same time is able to provide detailed mapping of biological components. Thus, STXM offers considerable potential for application to research on nanomaterials in biology and the environment. The potential and limitations of STXM in this context are discussed using a range of examples, focusing on the interaction of nanomaterials with microbial cells, biofilms and extracellular polymers. The studies outlined include speciation and mapping of metal-containing nanomaterials (Ti, Ni, Cu) and carbon-based nanomaterials (multiwalled carbon nanotubes, C60 fullerene). The benefits of X-ray fluorescence detection in soft X-ray STXM are illustrated with a study of low levels of Ni in a natural river biofilm.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".