Tuning the Bacterial Detection Sensitivity of Nanostructured Microelectrodes
Bibliographic record
Abstract
Fast, sensitive nucleic acid sensors that enable direct detection of bacteria and diagnosis of infectious disease would offer significant advantages over existing approaches that employ enzymatic amplification of nucleic acids. We have developed chip-based microelectrodes that are highly effective for bacterial detection and have shown that they can capture and permit the analysis of large slow moving mRNA targets. Here, we explore new approaches to tune their analytical sensitivity and investigate the effect of sensor size, material composition, and probe density on the electrochemical signals obtained in the presence of bacteria. Sensor size can be varied from 10 to 100 μm, and this parameter can change detection limits obtained by a factor of 100. Changing the surface coating can also be used to tune sensitivity, with more nanostructured coatings yielding the most sensitive detectors. Moreover, we assessed performance of the sensors by tuning probe density. Varying the density of the immobilized probe had a dramatic effect on sensitivity, with sparse probe monolayers providing superior levels of performance. Overall, this study points to several factors that can be used to tune detection limits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".