A Hierarchical 3D Nanostructured Microfluidic Device for Sensitive Detection of Pathogenic Bacteria
Bibliographic record
Abstract
Abstract Efficient capture and rapid detection of pathogenic bacteria from body fluids lead to early diagnostics of bacterial infections and significantly enhance the survival rate. We propose a universal nano/microfluidic device integrated with a 3D nanostructured detection platform for sensitive and quantifiable detection of pathogenic bacteria. Surface characterization of the nanostructured detection platform confirms a uniform distribution of hierarchical 3D nano‐/microisland (NMI) structures with spatial orientation and nanorough protrusions. The hierarchical 3D NMI is the unique characteristic of the integrated device, which enables enhanced capture and quantifiable detection of bacteria via both a probe‐free and immunoaffinity detection method. As a proof of principle, we demonstrate probe‐free capture of pathogenic Escherichia coli (E. coli) and immunocapture of methicillin‐resistant‐Staphylococcus aureus (MRSA). Our device demonstrates a linear range between 50 and 104 CFU mL−1, with average efficiency of 93% and 85% for probe‐free detection of E. coli and immunoaffinity detection of MRSA, respectively. It is successfully demonstrated that the spatial orientation of 3D NMIs contributes in quantifiable detection of fluorescently labeled bacteria, while the nanorough protrusions contribute in probe‐free capture of bacteria. The ease of fabrication, integration, and implementation can inspire future point‐of‐care devices based on nanomaterial interfaces for sensitive and high‐throughput optical detection.
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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 | 0.000 |
| 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".