A Review of Analytical Techniques to Characterise Nanomaterial Associations with Minerals, Organic Matter and Organisms
Bibliographic record
Abstract
Nanomaterials (NMs) have unique properties and control processes relevant to the fate of contaminants in soils, air, and aquatic systems and within the carbon cycle. Many NMs often occur in association with larger mineral grains, organic matter, or living organisms such as microbes, plants and fungi. The preservation of the spatial, textural, chemical, and mineralogical relations between NMs and minerals, organic matter, and organism (NM‐associations) is of fundamental importance as it provides information about the origin and formation mechanisms of NMs. Here we review analytical approaches and techniques to study NM‐associations at the bulk‐, micro‐, nano‐ and atomic‐scale. We will focus on (a) X‐ray diffraction and mass‐spectroscopy techniques; (2) automatisms within software packages that permit the search of features without operators; (3) preparation and analytical techniques such as the focused‐ion beam technology, transmission electron microscopy and atom probe tomography; (4) nano‐spectroscopic techniques such as tip‐enhanced Raman spectroscopy, synchrotron infrared nanospectroscopy, and nano‐X‐ray fluorescence spectroscopy; (5) ptychographic X‐ray computer tomography. This review paper concludes with selected new perspectives such as (a) the characterisation of NM‐precursors, (b) the role of NM‐associations in the stabilisation of soil organic matter and (c) the interaction of NM‐associations in wildfire smoke with contaminants from other sources.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".