Characterization of the Chemical Composition of Polyisobutylene-Based Oil-Soluble Dispersants by Fluorescence
Bibliographic record
Abstract
A novel methodology based on fluorescence quenching measurements is introduced to determine quantitatively the amine content of polyisobutylene succinimide (PIBSI) dispersants used as engine oil-additives. To this end, a series of five PIBSI dispersants were prepared by reacting 2 mol equiv of polyisobutylene succinic anhydride (PIBSA) with 1 mol equiv of hexamethylenediamine (HMDA), diethylenetriamine, triethylenetetramine, tetraethylenepentamine, and pentaethylenehexamine to yield the corresponding b-PIBSI dispersants. After having demonstrated that the presence of hydrogen bonds between the polyamine linker and the succinimide carbonyls of the dispersants prevents the quantitative analysis of the (1)H NMR and FTIR spectra of the dispersants to determine their chemical composition, alternative procedures based on gel permeation chromatography (GPC) and fluorescence quenching were implemented to estimate the amine content of the b-PIBSI dispersants. Taking advantage of the doubling in size that occurs when 2 mol of PIBSA are reacted with 1 mol of HMDA, a combination of GPC and FTIR was employed to follow how the chemical composition and molecular weight distribution of the polymers produced evolved with the reaction of PIBSA and HMDA mixed at different molar ratios. These experiments provided the PIBSA-to-HMDA molar ratio yielding the largest b-PIBSI dispersants and this molar ratio was then selected to prepare the four other dispersants. Having prepared five b-PIBSI dispersants with well-defined secondary amine content, the fluorescence of the succinimide groups was found to decrease with increasing number of secondary amines present in the polyamine linker. This result suggests that fluorescence quenching provides a valid method to determine the chemical composition of b-PIBSI dispersants which is otherwise difficult to characterize by standard (1)H NMR and FTIR spectroscopies.
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.
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) | 0.000 | 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 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".