Crystal-chemical and biological controls of trace and minor element incorporation into magnetite nanocrystals
Bibliographic record
Abstract
Magnetite nanoparticles possess numerous fundamental, biomedical and industrial applications, many of which depend on tuning the magnetic properties. This is often achieved by the incorporation of trace and minor elements into the magnetite lattice. Such incorporation was shown to depend strongly on the magnetite formation pathway (i.e., abiotic vs biological), but the mechanisms controlling element partitioning between magnetite and its surrounding precipitation solution remain to be elucidated. Here, we used a combination of theoretical modelling (lattice and crystal field theories) and experimental evidence (high-resolution inductively coupled plasma mass spectrometry and X-ray absorption spectroscopy) to demonstrate that element incorporation into abiotic magnetite nanoparticles is controlled principally by cation size and valence. Elements from the first series of transition metals (Cr to Zn) constituted exceptions to this finding as their incorporation appeared to be also controlled by the energy levels of their unfilled 3d orbitals, in line with crystal field mechanisms. We then show that element incorporation into biological magnetite nanoparticles produced by magnetotactic bacteria (MTB) cannot be explained by crystal-chemical parameters alone, which points to the biological control exerted by the bacteria over the element transfer between MTB growth medium and the intracellular environment. This screening effect generates biological magnetite with a purer chemical composition than the abiotic materials formed in a solution of similar composition. Our work establishes a theoretical framework for understanding the crystal-chemical and biological controls of trace and minor cation incorporation into magnetite, thereby providing predictive methods to tailor the composition of magnetite nanoparticles for improved control over magnetic properties.
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 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 | 0.000 | 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 | 0.000 |
| 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 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".