Application of monodispersed pegs in surfactant molecules
Bibliographic record
Abstract
Monodispersed PEGs have been gaining interest in chemistry applications that previously used polydispersed PEGs. Traditional, polydispsersed PEGs are synthesized through a polymerization process that generates a statistical mixture of ethylene glycol chains. The molecular weight of the PEG is an average. Monodispersed PEGs are synthesized through numerous chain elongation and column purification steps. Monodispersed or “highly pure” PEG mixtures contain PEGs with the same molecular weight. Production costs are significantly higher for monodispersed PEGs due to the synthesis techniques they require. The aim of this thesis was to demonstrate how monodispersed PEGs behave differently than their polydispersed coutnerparts. Specifically, in applications that employ nonionic surfactants. It was hypothesized the uniformity of the monodispersed mixtures would improve performance and stability.\nThe first part of the project focused on the synthesis of three monodispersed PEG derivatives consisting of 8,16, and 24 ethylene glycol units. Stepwise organic chemistry was used to synthesize the monodispersed PEGs to prevent giving away any proprietary information. The next step was to synthesize various sets of nonionic surfactant conjugates and compare the monodispersed and polydispersed counterparts. Particle size distributions of the nanoparticles formed under identical formulation conditions provided the basis for comparing the derivatives. However, due to the unanticipated complexity of the dynamic equilibrium present in surfactant solutions, the significance of these differences could not be related to performance. Qualitatively, monodispersed and polydispersed counterparts were unique. In an attempt to quantitatively demonstrate monodispersed PEGs enhance performance the project was directed towards one application, nanoparticle drug delivery systems. After experimental parameters were outlined for niosome and liposome formulations, sodium fluorescein, a hydrophilic dye and model hydrophilic drug, was introduced and a series of encapsulation experiments were performed. After much trial and error, more data is required to prove if monodispersed PEGs are superior to their polydispersed counterparts. Nevertheless, as shown in the results discussed in this thesis, monodispersed and polydispersed PEGs need to be at least treated as individual compounds.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".