Redox Interfaces for Electrochemically Controlled Protein–Surface Interactions: Bioseparations and Heterogeneous Enzyme Catalysis
Bibliographic record
Abstract
Redox-active materials are an attractive platform for engineering specific interactions with charged species by electrochemical control. We present nanostructured redox-electrodes, functionalized with poly(vinyl)ferrocene embedded in a carbon nanotube matrix, for modulating the adsorption and release of proteins through electrochemical potential swings. The affinity of the interface toward proteins increased dramatically following oxidation of the ferrocenes, and, due to the Faradaic nature of the organometallic centers, the electrodes were maintained at sufficiently low overpotentials to ensure the preservation of both protein structure and catalytic activity. Our system was selective for various proteins based on size and charge distribution, and exhibited fast kinetics (<120 s for a charge–discharge cycle) and high uptake capacities (>200 mg/g) under moderate overpotentials (+0.4 V vs Ag/AgCl), as well as remarkable stability for binding under ferrocene oxidation conditions. The preservation of bioactivity and protein structure at the interface indicates the potential for these redox-mediated surfaces to be used as heterogeneous supports for enzyme catalysis. This work draws on the molecular selectivity of ferrocene-functionalized materials toward organic anion groups, and demonstrates that these smart redox-active materials can be used for modulation of the macroscopic affinity of surfaces for charged biomacromolecules to enhance processes such as bioseparations, electrochemically controlled protein purification, biocatalysis, and electrochemically mediated drug release.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".