Bipyridinium and Imidazolium Ionic Liquids for Nanomaterials Synthesis: pH Effect, Phase Transfer Behavior, and Protein Extraction
Bibliographic record
Abstract
We demonstrate the potential use of 1,1′- bis (2-(cyclohexyloxy)-2-oxoethyl)-[4,4′-bipyridin]-1,1′-diium bromide (BP) and 1-ethyl-3-methylimidazolium chloride (EMI) ionic liquids (ILs) in in situ synthesis of gold nanoparticles (Au NPs) without using any external reducing or stabilizing agents. Both ILs produced nearly monodisperse NPs of 4–8 nm which were present in the form of self-assembled states. BP coated NPs formed self-assembled sheets and easily transferred to the organic phase by employing the water insoluble IL as a phase transfer agent. The efficiency of the phase transfer process was related to the extent of aggregation as well as functional groups. Both IL coated NPs were further used to extract the proteins from the complex biological mixtures. EMI coated NPs extracted proteins of large molar masses whereas BP coated NPs were good for the extraction of low molecular mass proteins. This disparity was controlled by the substituted functional groups of ILs. Bulky cyclohexyloxy functional groups of BP did not allow extraction of large molar mass proteins. Such a wide applicability of ILs in nanomaterials synthesis opens several new applications in the field of nanomedicine and nanobiotechnology where IL coated NPs can be used for diverse protein complexation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".