Electrografting a Hybrid Bilayer Membrane via Diazonium Chemistry for Electrochemical Impedance Spectroscopy of Amyloid-β Aggregation
Bibliographic record
Abstract
Herein, a novel hybrid bilayer membrane is introduced as a platform to study the aggregation of amyloid-β1–42 (Aβ1–42) peptide on surfaces. The first layer was covalently attached to a glassy carbon electrode (GCE) via diazonium electrodeposition, which provided a highly stable template for the hybrid bilayer formation. To prepare the long-chain hybrid bilayer membrane (lcHBLM)-modified electrodes, GCE surfaces were modified with 4-dodecylbenzenediazonium (DDAN) followed by the modification with dihexadecyl phosphate (DHP) as the second layer. For the preparation of short-chain hybrid bilayer membrane (scHBLM)-modified electrodes, GCE surfaces were modified with 4-ethyldiazonium (EDAN) as the first layer and bis(2-ethylhexyl) phosphate (BEHP) was utilized as the second layer. X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) were used to characterize the bilayer formation. Both positively charged [Ru(NH3)6]3+ and negatively charged ([Fe(CN)6]3-/4-) redox probes were used for electrochemical characterization of the modified surfaces using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). EIS results showed a decrease in charge transfer resistance (Rct) upon incubation of Aβ1–42 on the hybrid bilayer-modified surfaces. This framework provides a promising electrochemical platform for designing hybrid bilayers with various physicochemical properties to study the interaction of membrane-bound receptors and biomolecules on surfaces.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".