Transparent Nanowire Electrodes As a Tool for Electrochemical Detection
Bibliographic record
Abstract
The development of new nanomaterial like carbon nanotubes, graphene and nanowires brings new possibilities to existing technologies. With the emergence of transparent electrode based on these materials, we could imagine new type of sensors, which combines transparency and flexibility. Silver nanowire transparent electrodes are currently transforming this industry. Touch screen sensors, OLED and solar panel can now be fabricated on flexible plastic sheets with low cost printing techniques. In the same way, this new type of electrode could bring electrochemical sensor toward new transparent and flexible sensing application for life science. In our study, we fabricate and characterize transparent electrode based in metal nanowires for electrochemical detection. By changing the length, diameter and amount of deposited nanowires, we can vary their transparency (up to 90% transparent), sheet resistance (up to 0.1 ohm/square) and effective surface area. Compared to conventional evaporated plain electrodes, printed nanowire electrodes can achieve more than 80% transparency (in visible range) with the same electrochemical effective surface area. Using these electrodes, we can fabricate new sensors with high transparency (> 90%), flexibility and high sensitivity to oxygen and hydrogen peroxide. The detection of these two components is predominant in healthcare and environmental monitoring. The combination of those properties could allow us to use this type of sensors as opto-electrochemical sensors for monitoring living cells and also for glucose detection on humans. Transparent nanowire electrodes can also be a real alternative to existing indium tin oxide (ITO) transparent electrodes for other applications. Figure 1.Pictures of electrochemical sensors based on metal nanowires on PET transparent substrate. WE and REF electrodes are made from metal nanowire have 80% transparency, CE electrode are made from gold. Figure 2. SEM image of silver nanowire electrode and the size distribution of nanowires. Figure 3. Voltammetry response of the nanowire based electrochemical sensor to hydrogen peroxide in phosphate buffer (pH=7) Figure 4. Dose-response curve to hydrogen peroxide using the nanowire-based sensor in phosphate buffer (pH=7) Figure 1
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".