Stabilization of Graphene Oxide Dispersion in Plasma-like Isotonic Solution Containing Aggregating Concentrations of Bivalent Cations
Bibliographic record
Abstract
Graphene oxide's (GO) intravascular applications and biocompatibility are not fully explored yet, although it has been proposed as an anticancer drug transporter, antibacterial factor or component of wearable devices. Bivalent cations and the number of particles' atom layers, as well as their structural oxygen content and pH of the dispersion, all affect the GO size, shape, dispersibility and biological effects. Bovine serum albumin (BSA), an important blood plasma protein, is expected to improve GO dispersion stability in physiological concentrations of the precipitating calcium and magnesium cations to enable effective and safe tissue perfusion. METHODS: Four types of GO commercially available aqueous dispersions (with different particle structures) were diluted, sonicated and studied in the presence of BSA and physiological cation concentrations. Nanoparticle populations sizes, electrical conductivity, zeta potential (Zetasizer NanoZS), structure (TEM and CryoTEM), functional groups content (micro titration) and dispersion pH were analyzed in consecutive preparation stages. RESULTS: BSA effectively prevented the aggregation of GO in precipitating concentrations of physiological bivalent cations. The final polydispersity indexes were reduced from 0.66-0.91 to 0.36-0.43. The GO-containing isotonic dispersions were stable with the following Z-ave results: GO1 421.1 nm, GO2 382.6 nm, GO3 440.2 nm and GO4 490.1 nm. The GO behavior was structure-dependent. CONCLUSION: BSA effectively stabilized four types of GO dispersions in an isotonic dispersion containing aggregating bivalent physiological cations.
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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".