Etalon@lateral flow strip for integrated separation-sensing microfluidic platforms
Bibliographic record
Abstract
Lateral flow assays (LFAs) are widely favored for on-site analysis due to their simplicity and cost-effectiveness. However, their limited quantitative capabilities constrain them to qualitative testing. In contrast, etalon sensors offer high sensitivity and enable quantitative detection. They operate by producing interference-based optical signals through multiple reflections of light within a cavity formed by two parallel reflective surfaces. This requires a stable optical path length, traditionally limiting their use to smooth substrates. This study presents an integrated separation-sensing microfluidic platform (EtLFA). By fabricating etalons on commercial membranes and evaluating sensor's sensitivity and surface roughness, we determined that membrane surface roughness must meet two criteria - Sa < 0.5 μm and Smr > 90% - to support functional etalons. Capillary and permeability remain intact after etalon integration, ensuring membrane's purification performance. We further functionalized the etalon to respond specifically to glucose, to demonstrate the quantitative detection of glucose levels in a mimic blood sample. A glucose-responsive etalon@nylon served as the sensor module, while regenerated cellulose membrane enabled separation. This dual-module configuration filtered PDMS particles mimicking red blood cells and produced a 25 nm shift for 100 mg/dL glucose, enabling linear quantification via portable spectrometry. By incorporating etalon sensor onto rough membrane substrates, our platform transforms conventional LFAs into a quantitative analytical tool, offering novel avenues for enhancing analytical capabilities and broadening the applications of lateral flow assays.
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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".