Ionic‐complementary peptide‐modified highly ordered pyrolytic graphite electrode for biosensor application
Bibliographic record
Abstract
Ionic-complementary peptides are promising new biomaterials with potential applications in bionanotechnology. In the present investigation, a typical ionic-complementary peptide, EFK16-II, was used to modify a highly ordered pyrolytic graphite (HOPG) electrode. Upon modification, peptide nanofibers, parallel or oriented 60 degrees or 120 degrees to each other, were formed on the surface of HOPG electrode. Surface wettability of the electrode was improved as indicated by a significant decrease in the water contact angle. The electrochemical response of the EFK16-II nanofiber-modified HOPG electrode for the ferricyanide/ferrocyanide redox couple was characterized. Cyclic voltammograms indicated that the presence of peptide nanofibers on the HOPG electrode did not block electron transfer at slow scan rates ( approximately 2 mV/s), but did so at high scan rates ( approximately 100 mV/s). A model enzyme glucose oxidase (GOx) was covalently immobilized onto this nanofiber-modified electrode, and its potential as an enzyme-based biosensor for glucose was examined. At an applied potential of +0.45 V (vs. Ag/AgCl), the current increased linearly with glucose concentration up to 7.5 mM and a relative high sensitivity was obtained at 11.3 +/- 1.0 nA/(mM mm(2)). The immobilized GOx showed high affinity for glucose, with a Michaelis-Menten constant K(m) of 6.8 +/- 0.9 mM. It also exhibited relatively good storage and operational stabilities, and reflected in only a small decrease (13%) in the current response after 1 month storage and negligible changes upon 50 cyclic voltammetric scans. The results presented here demonstrate an excellent potential of the use of ionic-complementary peptides to modify electrode surfaces for biomolecular sensing and diagnostics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 teacher head, 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".