Benchtop-fabricated lipid-based electrochemical sensing platform for the detection of membrane disrupting agents
Bibliographic record
Abstract
There are increasing concerns about the danger that water-borne pathogens and pollutants pose to the public. Of particular importance are those that disrupt the plasma membrane, since loss of membrane integrity can lead to cell death. Currently, quantitative assays to detect membrane-disrupting (lytic) agents are done offsite, leading to long turnaround times and high costs, while existing colorimetric point-of-need solutions often sacrifice sensitivity. Thus, portable and highly sensitive solutions are needed to detect lytic agents for health and environmental monitoring. Here, a lipid-based electrochemical sensing platform is introduced to rapidly detect membrane-disrupting agents. The platform combines benchtop fabricated microstructured electrodes (MSEs) with lipid membranes. The sensing mechanism of the lipid-based platform relies on stacked lipid membranes serving as passivating layers that when disrupted generate electrochemical signals proportional to the membrane damage. The MSE topography, membrane casting and annealing conditions were optimized to yield the most reproducible and sensitive devices. We used the sensors to detect membrane-disrupting agents sodium dodecyl sulfate and Polymyxin-B within minutes and with limits of detection in the ppm regime. This study introduces a platform with potential for the integration of complex membranes on MSEs towards the goal of developing Membrane-on-Chip sensing devices.
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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 | 0.000 |
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".