Optimizing the Temporal Resolution of Fast-Scan Cyclic Voltammetry
Bibliographic record
Abstract
Electrochemical detection with carbon-fiber microelectrodes has become an established method to monitor directly the release of dopamine from neurons and its uptake by the dopamine transporter. With constant potential amperometry (CPA) the measured current provides a real time view of the rapid concentration changes, but the method lacks chemical identification of the monitored species and markedly increases the difficulty of signal calibration. Monitoring with fast-scan cyclic voltammetry (FSCV) allows species identification and concentration measurements, but often exhibits a delayed response time due to the time-dependent adsorption/desorption of electroactive species at the electrode. We sought to improve the temporal resolution of FSCV to make it more comparable to CPA by increasing the waveform repetition rate from 10 to 60 Hz with uncoated carbon-fiber electrodes. The faster acquisition led to diminished time delays of the recordings that tracked more closely with CPA measurements. The measurements reveal that FSCV at 10 Hz underestimates the normal rate of dopamine uptake by about 18%. However, FSCV collection at 10 Hz and 60 Hz provide identical results when a dopamine transporter (DAT) blocker such as cocaine is bath applied. To verify further the utility of this method, we used transgenic mice that over-express DAT. After accounting for the slight adsorption delay time, FSCV at 60 Hz adequately monitored the increased uptake rate that arose from overexpression of DAT and, again, was similar to CPA results. Furthermore, the utility of collecting data at 60 Hz was verified in an anesthetized rat by using a higher scan rate (2400 V/s) to increase sensitivity and the overall signal.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".