Utilizing ion mobility and tandem mass spectrometry to evaluate the structure and behaviour of multimeric cyclodextrin complexes
Bibliographic record
Abstract
Characterizing noncovalent complexes of molecular dimers and higher complexes using tandem mass spectrometry (MS/MS) can be hindered due to spectral overlap in both the MS and the MS/MS. We investigated the structures and dissociation energetics of multimeric β-cyclodextrin (β-CD) complexes alone or with substrates using combinations of ion mobility spectrometry (IMS), MS/MS, and Rice–Ramsperger–Kassel–Marcus (RRKM) unimolecular rate modelling. The doubly charged β-CD dimers ([(β-CD)2 – 2H+]2−) dissociate to two [β-CD – H+]− ions with the same m/z. IMS was used to separate source generated [(β-CD)2 – 2H+]2− from [β-CD – H+]− and the extent of [(β-CD)2 – 2H+]2− dissociation versus collision energy was determined by modelling changes in the ion’s isotopic profile. The RRKM derived critical energy (E0) for dissociation of [(β-CD)2 – H+]− and [(β-CD)2 – 2H+]2− were 1.85 ± 0.11 eV and 1.79 ± 0.09 eV, respectively, corresponding to a slight decrease in complex stability due to increased charge–charge repulsion in the dianion. This approach was extended to include dimeric complexes complexed to 4,4′-(propane-1,3-diyl) dibenzoic acid (PDDA) and ibuprofen (Ibu).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".