A Nanostructured Gold/Graphene Microfluidic Device for Direct and Plasmonic-Assisted Impedimetric Detection of Bacteria
Bibliographic record
Abstract
Hierarchical 3D gold nano-/microislands (NMIs) are favorably structured for direct and probe-free capture of bacteria in optical and electrochemical sensors. Moreover, their unique plasmonic properties make them a suitable candidate for plasmonic-assisted electrochemical sensors, yet the charge transfer needs to be improved. In the present study, we propose a novel plasmonic-assisted electrochemical impedimetric detection platform based on hybrid structures of 3D gold NMIs and graphene (Gr) nanosheets for probe-free capture and label-free detection of bacteria. The inclusion of Gr nanosheets significantly improves the charge transfer, addressing the central issue of using 3D gold NMIs. Notably, the 3D gold NMIs/Gr detection platform successfully distinguishes between various types of bacteria including Escherichia coli ( E. coli ) K12, Pseudomonas putida ( P. putida ), and Staphylococcus epidermidis ( S . epidermidis ) when electrochemical impedance spectroscopy is applied under visible light. We show that distinguishable and label-free impedimetric detection is due to dissimilar electron charge transfer caused by various sizes, morphologies, and compositions of the cells. In addition, the finite-difference time-domain (FDTD) simulation of the electric field indicates the intensity of charge distribution at the edge of the NMI structures. Furthermore, the wettability studies demonstrated that contact angle is a characteristic feature of each type of captured bacteria on the 3D gold NMIs, which strongly depends on the shape, morphology, and size of the cells. Ultimately, exposing the platform to various dilutions of the three bacteria strains revealed the ability to detect dilutions as low as ∼20 CFU/mL in a wide linear range of detection of 2 × 10 1 –10 5, 2 × 10 1 –10 4, and 1 × 10 2 –1 × 10 5 CFU/mL for E. coli, P. putida, and S. epidermidis, respectively. The proposed hybrid structure of 3D gold NMIs and Gr, combined by novel plasmonic and conventional impedance spectroscopy techniques, opens interesting avenues in ultrasensitive label-free detection of bacteria with low cost and high stability.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".