Adsorption of bovine serum albumin (BSA) by bare magnetite nanoparticles with surface oxidative impurities that prevent aggregation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bare, uncoated magnetite nanoparticles, synthesized using an electrochemical surfactant-free synthesis, have highly oxidized surfaces that prevent aggregation. These particles have demonstrated highly intriguing biological activity showing extremely potent antibiotic activity against both gram-positive and gram-negative bacteria with little toxicity to rats. This difference in activity could be ascribed to the nature of the protein corona. The kinetics and thermodynamics of the binding of bovine serum albumin (BSA), used as a model serum protein, to these magnetite nanoparticles were analyzed. There is no significant change in particle diameter by dynamic light scattering following adsorption, indicating corona formation does not induce aggregation. The maximum adsorption capacity of the particles was determined to be 300 mg of BSA per gram of magnetite. The particles are able to adsorb 90% of the BSA at protein concentrations as high as 500 mg/L. The adsorption is best described using a pseudo second order model and a Langmuir Type III isotherm model. Thermodynamic analysis showed that the process is entropically driven and is spontaneous at all tested temperatures and conditions. However, it appears to be a weak to moderate physical adsorption. This moderate binding affinity could indicate the differential biological activity of these particles towards bacteria and mammalian cells and further support the contention that these are potentially useful new tools for targeting antibiotic-resistant bacteria.
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.
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 | 0.000 |
| 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 it