Ferrocenyl-Based Signal Amplification across Self-Assembled Monolayers in Electrochemical Biosensors
Bibliographic record
Abstract
A major challenge in effectively treating infections is to provide timely diagnosis of a bacterial or viral agent. Current cell culture methods require > 24 hours to identify the cause of infection. The Toll-Like Receptor (TLR) family of proteins from the human immune system can rapidly identify classes of pathogens and has been shown to work well in an impedance-based biosensor, where the protein is attached to a Au electrode via a self-assembled monolayer (SAM). While the sensitivity of these sensors has been good, they contain a high resistance (>1 kΩ) SAM, generating relatively small signals and requiring longer data collection times, which is ill-suited to implementation outside of a laboratory. Here, we describe a refined approach to increasing the signal magnitude and decreasing the measurement time of a TLR-4 biosensor by inserting a redox-active ferrocenyl-terminated alkane thiol into a mixed SAM containing hydroxyl- and carboxyl-terminated alkane thiols. The introduction of ferrocene into the SAM provides an avenue for the electrochemical mediation of soluble redox couples. This dramatically increases the measured current across the SAM when compared to SAMs that do not contain such a redox-active moiety, with confirmation of the intended surface modification of the Au electrodes obtained using a variety of advanced surface techniques. It is shown that these TLR-4 biosensors, utilizing the ferrocene-based SAMs, exhibit a ≤ 1 kΩ interfacial resistance that can be measured in less than one minute. These biosensors respond selectively to their intended target, Gram-negative bacteria, while remaining insensitive to Gram-positive bacteria or viral particles. These sensors were further interrogated with an inexpensive (< $100) open-source hand-held potentiostat that could be easily employed in a non-laboratory setting, including in remote or third-world applications. Figure 1
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".