Influence of the Surface Roughness of Platinum Electrodes on the Calibration of the Electrochemical Quartz-Crystal Nanobalance
Bibliographic record
Abstract
The electrochemical quartz-crystal nanobalance (EQCN) is an in situ technique that measures mass changes (Δ m ) associated with interfacial phenomena. Analysis of Δ m sheds light on the mass balance (in addition to the charge and energy balances) and provides new insight into the nature of electrochemical processes. The EQCN measures changes in frequency (Δ f ) of a quartz-crystal resonator, which are converted into Δ m using the Sauerbrey equation containing the characteristic constant ( C f ). The value of C f is determined by physical parameters of the crystal and refers to an atomically smooth surface. However, real resonators are not smooth and electrodes have their intrinsic roughness. Thus, the conversion of Δ f to Δ m should be done using an experimentally determined characteristic constant ( C f,exp ) for a given value of the surface roughness factor ( R ). Here, we calibrate the system using Ag electrodeposition on Pt electrodes of gradually increasing R; the latter is adjusted through Pt electrodeposition. The surface morphology of the Pt substrates prior to and after Ag electrodeposition is examined using atomic force microscopy. The values of C f,exp are determined by analyzing the slopes of charge density versus Δ f plots for the Ag electrodeposition. They are different than C f and increase logarithmically with R . The C f and C f,exp values are used in a comparative analysis of the mass changes (δΔ m ) for complete cyclic voltammetry profiles covering the 0.05–1.40 V range. This reveals that the employment of C f instead of C f,exp provides inaccurate values of δΔ m, and the magnitude of the discrepancy increases with R .
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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 | 0.001 |
| 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 | 0.001 | 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".