Electrochemical Droplet Sculpturing of Short Carbon Fiber Nanotip Electrodes for Neurotransmitter Detection
Bibliographic record
Abstract
Carbon fiber nanotip electrodes (CFNEs) are essential for electrochemical recordings of neurotransmitter release in confined spaces like synapses and for intracellular measurements through cytoplasm insertion. However, fabricating CFNEs with small surface areas to reduce noise remains challenging. Conventional methods struggle in controlling electrode tip size and length, reproducibility and success rate of fabrication. Here, we established a reliable, straightforward and user-friendly method to fabricate and shape CFNEs, enabling control over tip diameter, length, and tailor tip geometry. This method utilizes real-time microscopy imaging for positioning cylindrical carbon fiber microelectrodes (CFMEs) into a potassium hydroxide droplet, where a series of time- and voltage- controlled pulses enables a gentle, stepwise electrochemical etching of the CFME tips. The microscope-guided electrode positioning determines the etched region, while voltage pulse size and number control the extent of CFME tip removal. Hence, real-time adjustments to electrode positioning at the droplet’s liquid-air interface and incremental voltage pulses enable precise electrode sculpturing, akin to woodcarving with a knife. Using this method, we demonstrate the successful fabrication of short (10 μm) CFNEs with tip diameters of ~100 nm and sculptured into two distinct geometries: cone and needle shaped electrodes. These CFNEs exhibited excellent electrochemical properties and were employed for low-current noise electroanalysis of dopamine (DA) released from individual ~200 nm liposomes preloaded with DA. The data, supported by in silico simulation, suggest that electrode shape influences detection efficiency of liposome sub-populations based on their size, thus highlighting the critical role of electrode geometry in vesicle-based electroanalysis studies.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".