Electrochemical Droplet Sculpturing of Short Carbon Fiber Nanotip Electrodes for Neurotransmitter Detection
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Carbon fiber nanotip electrodes (CFNEs) are crucial for electrochemical recordings of neurotransmission release in confined spaces, such as synapses and intracellular measurements. However, fabricating CFNEs with small surface area to minimize noise remains challenging due to inconsistent tip size control, low reproducibility, and low fabrication success rate. Here, we present a reliable, user-friendly method with high reproducibility and success rate for precise CFNE fabrication using microscopy-guided electrochemical etching of cylindrical carbon fiber microelectrodes in a potassium hydroxide droplet. The electrode positioning at the droplet’s liquid–air interface determines the etched region, while manually applied time- and amplitude-controlled voltage pulses regulate material removal. Hence, real-time adjustments to electrode positioning and incremental voltage pulses enable precise sculpturing, akin to woodcarving with a knife. Using this method, we demonstrate successful fabrication of short (10 m) CFNEs with tip diameters of 100 nm, with excellent electrochemical properties and sculptured into cone- and needle-shaped electrodes. Employing these CFNEs for low-noise amperometric dopamine (DA) detection from individual 200 nm DA-loaded liposomes, combined with in silico simulations, revealed that electrode shape influences detection efficiency based on vesicle size. These findings highlight the critical role of electrode geometry in vesicle-based electroanalysis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".