Genetic Tuning of Iron Oxide Nanoparticle Size, Shape, and Surface Properties in <i>Magnetospirillum magneticum</i>
Bibliographic record
Abstract
Abstract Different applications require iron oxide nanoparticles (IONPs) of varying size, shape, crystallinity, and surfaces that can be controlled through the synthesis reaction conditions. Under ambient conditions, Magnetospirillum magneticum AMB‐1 builds uniform Fe3O4 IONPs with shapes and crystal forms difficult to achieve with chemical synthesis. Genetic engineering can be used to change their properties, but there are few tools to fine‐tune expression over a wide range. To this end, ribosome binding sites, minimal constitutive promoters, and inducible systems (IPTG, aTc, and OC6) with large dynamic range are designed. These are used to control M. magneticum genes that affect IONP properties, including size (mamC), morphology (mms6), chain length (mamK), and surface coating (mamC fusions). These systems increase the fraction of IONPs that are less than 30 nm, produce rounded particles, and lead to the production of intracellular chains with 24 or more IONPs. In addition, the R5 peptide from diatoms is found to silica coat the surface of metal oxide nanoparticles (Fe, Ti, Ta, Hf) and can be genetically directed to the IONP surface. This work demonstrates the genetic control of IONP properties, but also highlights the robustness of the system, which complicates genetic engineering to produce radically different particles and structures.
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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 | 0.000 | 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) | 0.001 | 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".