Impedimetric Detection of Bacteria Using Hierarchical 3D Nanostructured Gold
Bibliographic record
Abstract
Impedimetric electrochemical sensing offers direct, label-free, cost-effective, accurate, and rapid detection of biological analytes. Among different types of nanostructured platforms, hierarchical structures of gold have proven to be beneficial for impedimetric and direct diagnosis of bacteria. In this research, a two-step electrodeposition method was used to achieve hierarchical nanostructures of gold for impedimetric distinction of E. coli-k12. The current observations demonstrate that the two-step electrodeposition method can convert sharp and needle-shaped structures of gold into curved and smooth structures that are able to detect bacteria, with enhanced sensitivity. The results of Electrochemical impedance spectroscopy (EIS) showed a limit of detection of approximately 90 CFU in 30 μl of solution and a linear range of detection between 3×103and 3×107CFU mL-1 for the final sensor. In addition, this structure can detect bacteria in less than 10 minutes. Ultimately, this ultrasensitive label-free bacteria sensor, based on a novel engineered hybrid method, opens interesting avenues in bacteria sensing with exciting detection abilities. Keywords: E. coli bacteria, electrodeposition, hierarchical nanostructures of gold, electrochemical impedance spectroscopy (EIS).
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".