Imidazolium-Based Ionic Liquid Surfaces for Biosensing
Bibliographic record
Abstract
Ionic liquid self-assembled monolayers (SAM) were designed and applied for binding streptavidin, promoting affinity biosensing and enzyme activity on gold surfaces of sensors. The synthesis of 1-((+)-biotin)pentanamido)propyl)-3-(12-mercaptododecyl)-imidazolium bromide, a biotinylated ionic liquid (IL-biotin), which self-assembles on gold film, afforded streptavidin sensing with surface plasmon resonance (SPR). The IL-biotin-SAM efficiently formed a full streptavidin monolayer. The synthesis of 1-(carboxymethyl)-3-(mercaptododecyl)-imidazoliumbromide, a carboxylated IL (IL-COOH), was used to immobilize anti-IgG to create an affinity biosensor. The IL-COOH demonstrated efficient detection of IgG in the nanomolar concentration range, similar to the alkylthiols SAM and PEG. In addition, the IL-COOH demonstrated low fouling in crude serum, to a level equivalent to PEG. The IL-COOH was further modified with N,N'-bis (carboxymethyl)-l-lysine hydrate to bind copper ions and then, chelate histidine-tagged biomolecules. Human dihydrofolate reductase (hDHFR) was chelated to the modified IL-COOH. By monitoring enzyme activity in situ on the SPR sensor, it was revealed that the IL-COOH SAM improved the activity of hDHFR by 24% in comparison to classical SAM. Thereby, IL-SAM has been synthesized and successfully applied to three important biosensing schemes, demonstrating the advantages of this new class of monolayers.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".