Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Characterization of Tunable Carbohydrate-Based Materials for Sorption of Oil Sands Naphthenic Acids
Bibliographic record
Abstract
The potential for sorption and possible degradation of components in oil sands processed water (OSPW) by the use of synthetically engineered co-polymers is receiving growing attention. Recent research has highlighted the sorption of total oil sands naphthenic acid fraction components (NAFCs) by β-cyclodextrin (β-CD) co-polymers. The incorporation of β-CD within co-polymer frameworks represents a novel modular approach with significant potential for controlled tuning of the textural mesoporosity of the sorbents. Herein, we report the Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) characterization of aqueous samples containing oil sands NAFCs following sorption with a range of cyclodextrin-based co-polymers. The materials investigated were β-CD cross-linked with three different types of diisocyanates, namely, (i) 4,4′-dicyclohexylmethane diisocyanate, (ii) 4,4′-diphenylmethane diisocyanate, and (iii) 1,4-phenylene diisocyanate. Variable sorption of NAFCs was observed according to the cross-linking density of the co-polymer framework and the nature of the cross-linker unit. Furthermore, the sorption of the NAFCs by the co-polymers was not affected by other parameters, such as metal ions, salinity, and non-oil sands acid fractions present in OSPW. The observation of molecular selective sorption in co-polymer materials containing β-CD represents an important contribution toward the development of sorbent materials for the controlled removal of oil sands acids in aquatic environments. The FT-ICR MS measurements also contribute further to the understanding of the thermodynamic sorption mechanism of such materials.
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".