Soft X‐ray spectromicroscopy for speciation, quantitation and nano‐eco‐toxicology of nanomaterials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it