Formation pathways of Zn-spinel nanoparticles in Zn-contaminated soils and sediments
Bibliographic record
Abstract
Abstract This study depicts novel formation pathways of low-temperature spinel nanoparticles (NPs). Understanding these pathways is of environmental and technological importance as the spinel structure can incorporate an extensive range of cations, which allows it to sequester metal contaminants in soils, water, and tailings. Using the focused ion beam and ion mill technique in combination with transmission electron microscopy, we investigated various formation pathways of Zn-bearing spinel NPs in the smelter-impacted soils of the Flin Flon area, Manitoba, Canada. Four pathways were identified in soil organic matter and a clay mineral microaggregate: (I) the formation of euhedral franklinite NPs through crystallization of particle and monomer attachment in proximity to dissolving surfaces of high-T micrometer-sized Zn-Fe oxide particles; (II) the heterogeneous nucleation of franklinite NPs on the surface of a dissolving wurtzite (ZnS) precipitate, with the latter being facilitated by the sulfidation of Ag ions or Ag NPs on its surface; (III) the heterogeneous nucleation of Zn-bearing magnetite at low nucleation rates preferentially along the edges of basal surfaces of illite-smectite minerals with K:Ca ratios ranging from 1:2 to 2:1; (IV) aggregation and attachment of franklinite NPs at higher nucleation rates to clusters on basal surfaces or growing surfaces of euhedral franklinite NPs. These pathways are compared with general observed formation pathways of nanomaterials in the critical zone and with previously identified formation pathways of Zn-spinel nanomaterials. Identification of formation pathways of spinel-group minerals under ambient Earth surface conditions provides a better understanding of the sequestration and environmental fate of metals compatible with their structure. This is relevant for regions impacted by smelter activities, as divalent cations such as Ni2+, Cu2+, and Zn2+ are common metal contaminants in their corresponding soils and sediments.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".