Method Development for Electrochemical Impedance Spectroscopy Studies of Bovine Serum Albumin on Platinum Electrode Surfaces
Bibliographic record
Abstract
In this thesis, a method is presented for studying protein adsorption on solid surfaces by Electrochemical Impedance Spectroscopy (EIS), using a model system of Bovine Serum Albumin (BSA) on platinum electrode surfaces. EIS is a commonly used analytical technique for electrochemical biosensors, particularly useful because of the ability to model the electrochemical data with Equivalent Electrical Circuits (EECs) and extract meaningful quantitative values for physical processes that are occurring in the electrochemical system (for example, double-layer capacitance Cdl or charge-transfer resistance Rct). In this thesis, a comprehensive method for EEC selection is developed by which the data are found to be Kramers-Kronig compliant, a library of possible circuits is selected, and then residual errors, parameter values, and standard deviations based on replicate measurements are used to find the circuit which best models the electrochemical system. The influence of number of factors related to experimental set-up and EIS conditions on BSA adsorption is examined, and suggestions are given as to avoid signal convolution by these factors. Most influentially, performing EIS in Phosphate Buffered Saline (PBS) before exposure to BSA results in a significant decrease in BSA film formation. BSA adsorption is strongly influenced by potential, with more positive potentials resulting in greater BSA adsorption. The application of ac vs. dc potential, however, does not influence the BSA film. Surface-Enhanced Raman Spectroscopy (SERS) experiments of BSA and Human Serum Albumin (HSA) adsorption on gold Sphere Segment Void (SSV) substrates are also presented. Finally, BSA adsorption on platinum surfaces is monitored over time using EIS. Results suggest that, after incubation, BSA desorbs from the electrode surface but the rate of desorption is dependent on the concentration gradient between the surface and the bulk solution. Results also suggest that BSA adsorption is strongly affected by concentration, with higher concentrations resulting in greater BSA adsorption on the electrode surface and greater BSA desorption upon change to a less positive potential.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".