Surfactant Structure-Dependent Interactions with Modified Starch Nanoparticles Probed by Fluorescence Spectroscopy
Bibliographic record
Abstract
The interactions between the surfactants sodium dodecyl sulfate (SDS) and sodium dioctyl sulfosuccinate (AOT) and starch nanoparticles (SNPs) hydrophobically modified with the hydrophobic dye pyrene (Py-SNPs) were investigated in water by steady-state and time-resolved fluorescence. The Py-SNPs formed interparticulate aggregates in water, which were disrupted by adding SDS to the Py-SNP aqueous dispersions. SDS was found to interact with Py-SNPs at SDS concentrations that were close to 2 orders of magnitude lower than its critical micelle concentration (CMC). These interactions led to the breakup of the Py-SNP aggregates, which was confirmed by conducting fluorescence resonance energy transfer experiments between naphthalene-labeled SNPs (Np-SNPs) and Py-SNPs. By the time the SDS concentration reached the CMC of SDS, the Py-SNPs were separated from each other and excimer was generated from isolated Py-SNPs in the aqueous dispersions. Whereas SDS interacted with the Py-SNPs at SDS concentrations lower than CMC, SDS did not seem to target the hydrophobic pyrene aggregates. Only above the CMC did SDS appear to interact with the pyrene aggregates, as evidenced from diffusive pyrene excimer formation between excited and ground-state pyrenes. Most surprisingly, no interaction was observed between sodium dioctyl sulfosuccinate (AOT) and Py-SNP at AOT concentrations where SDS interacted with the Py-SNPs. This observation led to the conclusion that SDS below its CMC interacted not with hydrophobic pyrene aggregates but rather through the formation of inclusion complexes, which led to the electrostatic stabilization of individual Py-SNPs and enabled the breakup of Py-SNP aggregates. The formation of inclusion complexes with linear surfactants like SDS might thus provide a new means of stabilizing hydrophobically modified starch nanoparticles in water, which bears the promise of finding future applications.
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How this classification was reachedexpand
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) | 0.002 | 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 teacher head, 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".