Exploiting shape-selected iron oxide nanoparticles for the destruction of robust bacterial biofilms – active transport of biocides <i>via</i> surface charge and magnetic field control
Bibliographic record
Abstract
Biofilms that form on reusable medical devices are a cause of hospital acquired infections; however, sanitization of biofilms is a challenge due to their dense extracellular matrix. This work presents an innovative strategy using biocide-loaded iron oxide nanoparticles transported within the matrix via a magnetic field to eradicate biofilms. Results show that the active delivery of the biocide to underlying cells effectively penetrates the extracellular matrix and inactivates Methicillin resistant Staphylococcus aureus (MRSA) biofilms (responsible for several difficult-to-treat infections in humans). To optimize this treatment, the loading of spherical, cubic and tetrapod-shaped nanoparticles with a model biocide, CTAB (cetyltrimethylammonium bromide) was studied. Biocide loading was determined to be dependent on the shapes' surface charge density instead of the surface area, meaning that biocide attachment is greater for nanoparticles with sharp edges (e.g. cubes and tetrapods). These results can be used to optimize treatment efficacy, and help further understanding of biofilm and nanoparticle surface zeta potentials, and the nanoparticle-biofilm interactions.
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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) | 0.000 | 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".