Hierarchical Nanotextured Microelectrodes Overcome the Molecular Transport Barrier To Achieve Rapid, Direct Bacterial Detection
Bibliographic record
Abstract
Detection of biomolecules at low abundances is crucial to the rapid diagnosis of disease. Impressive sensitivities, typically measured with small model analytes, have been obtained with a variety of nano- and microscale sensors. A remaining challenge, however, is the rapid detection of large native biomolecules in real biological samples. Here we develop and investigate a sensor system that directly addresses the source of this challenge: the slow diffusion of large biomolecules traveling through solution to fixed sensors, and inefficient complexation of target molecules with immobilized probes. We engineer arrayed sensors on two distinct length scales: a ∼100 μm length scale commensurable with the distance bacterial mRNA can travel in the 30 min sample-to-answer duration urgently required in point-of-need diagnostic applications; and the nanometer length scale we prove necessary for efficient target capture. We challenge the specificity of our hierarchical nanotextured microsensors using crude bacterial lysates and document the first electronic chip to sense trace levels of bacteria in under 30 min.
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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) | 0.001 | 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".