Bibliographic record
Abstract
In this paper, a theoretical approach of extended Stern model is formulated to represent the electric double layer (EDL) for biochemical as well as biological samples. The existing Stern model is used for several decades to describe the phenomena of electric double layer of electrode/electrolyte interface. In the conventional stern model the double layer which is formed between the electrode and electrolyte interface is described by double layer capacitance. Using the existing Stern model, the equivalent circuit model is not valid for electrical double layer capacitance of electrode/electrolyte interface in β dispersion range. The protein molecules form chemical coupling and chemical adsorption along with classical ionic bonding with gold electrodes. Thus, the compactness of EDL decreases and the double layer capacitance is replaced by a constant phase element (CPE). In the present paper, a three-electrode based ECIS device was used to measure the impedance of various enzymatic solutions for practical realization of theoretical approach. The results obtained from experimental work, were simulated by equivalent circuit simulator, ZsimpWin to validate the extended Stern model by comparing χ2 value. Finally the electrical parameters were extracted and compared for Stern model and extended Stern model. The results obtained by practical experiment and equivalent circuit simulation showed the effectiveness of extended Stern model over Stern model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".