Electrochemical detection of metals at gold ultramicroelectrodes with application to capillary electrophoresis
Bibliographic record
Abstract
Electrochemical detection of metals can be done at polycrystalline gold ultramicroelectrodes using repetitive cyclic voltammetry (RCV), a detection method sharing some similarities with anodic stripping voltammetry (ASV). Each cycle of the potential waveform for RCV involves application of a negative preconcentration potential (for 50 to 300 ms) followed by a cyclic voltammetry (CV) scan at 20 to 1000 V/s. The response due to the metals is evident at potentials negative of the region for oxide formation in the resulting CVs. Metals are deposited at the Au surface by underpotential deposition (UPD) processes. Any metal that can be analyzed by RCV could potentially be quantified using UPD-ASV at Au (rather than by ASV at Hg). The UPD kinetics of Pb and Cu at polycrystalline Au were examined by setting kinetic parameters (rate constant, symmetry factor, and electrosorption valency) within a simulation program used to generate simulated CVs. Reasonably good agreement between experimental and simulated CVs was possible using the simulation, with the same kinetic parameters used to generate simulated CVs to match experimental CVs over a range of sweep rates for each system. Using this method, the following rate constants (k) were estimated: for UPD of Cu in H₂SO₄ and HClO₄, ks ~ 3.6 x 10⁴ sˉ¹ and 1.1 x 10⁴ sˉ¹ respectively, and for UPD of Pb in H₂SO₄, kₛ ~ 4.0 x 10⁵ sˉ¹. Repetitive cyclic voltammetry was applied to the detection of metals separated by capillary electrophoresis. Separation of Tl⁺, Cd²⁺, Cu²⁺, Pb²⁺, Zn²⁺, Ni²⁺, Co²⁺ and Mn²⁺ was demonstrated in 0.01 mol/L acetic acid and 0.01 mol/L ammonium acetate(pH ~ 4.6) using RCV. While stacking is commonly exploited for sensitivity enhancement during injection, it was shown that detection-end stacking is also useful. A novel technique named electrophoretic extraction (EE) was developed for analysis of particle-containing solutions (e.g. soil extracts or other colloidal suspensions). EE involves application of backpressure during CE to prevent particles from entering the separation capillary: the applied pressure is regulated so analyte ions enter the capillary and migrate to the detector, whereas other particles are prevented from entering the capillary. The feasibility of this approach was demonstrated.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".