Improvement of Low-Cost Commercial Carbon Screen-Printed Electrodes Conductivities with Controlled Gold Reduction Towards Thiol Modification
Bibliographic record
Abstract
Effectively detecting bacteria in the environment is crucial for researchers to make informed decisions about the safety of public areas, such as lakes. This led to an increased need in the development of portable handheld devices, capable of on-the-spot chemical and biological sensing applications. Specific interests lie in electrochemical biosensors and screen-printed electrodes (SPEs) due to the decreased costs, an ability to integrate with handheld devices, and their user-friendly nature. Together, these qualities make the devices more accessible in resource-poor settings. Two of the most common substrates used to fabricate SPEs are carbon and gold. Carbon SPEs are effective in sensing applications yet challenged when attempting to covalently attach biomolecules to the surface. Gold SPEs have higher affinity towards biomolecules and improve the sensitivity, selectivity, and stability of a device; yet they can be costly. A carbon SPE modified with gold may be an ideal candidate to create an efficient low-cost device, using electrochemical gold deposition. In this study, electrochemical gold deposition on SPEs is explored to enhance the surface area and conductivity towards sensing applications. These SPEs were then modified with a thiol-based self-assembled monolayer (SAM) which demonstrates this technique could be used for further modification towards biosensing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".