A paper-based microfluidic platform with shape-memory-polymer-actuated fluid valves for automated multi-step immunoassays
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Bibliographic record
Abstract
Smart fluid manipulation with automatically controlled paper valves will enable automated and multi-step immunoassays on paper-based microfluidic devices. In this work, we present an integrated paper-based microfluidic platform with shape-memory polymer (SMP)-actuated fluid valves capable of automated colorimetric enzyme-linked immunosorbent assays (ELISAs). A single-layer microfluidic paper-based analytical device (μPAD) was designed to store all the reagents on the chip, and sequentially transfer reagents to a paper test zone following a specific ELISA protocol through automatic fluidic flow control by the multiple SMP-actuated valves. The actuation of a paper valve was based on the thermally responsive, duel-state shape transformation of a SMP sheet attached to the root of a paper cantilever beam for driving a hydrophilic paper bridge to connect and disconnect two paper channels. A portable colorimetric reader was developed to control the on-chip valve operations, quantify the colorimetric signal output, display the assay result, and wirelessly transmit the data to a smart phone for the application of telemedicine. Reliable operations of the paper valve and the entire μPAD were demonstrated with success rates of 97% and 93%, respectively. A detection mechanism for valve malfunction was designed and confirmed effective to identify any mal-operation of individual valves, thus rendering our platform reliable in real assays. For device calibration, we conducted direct ELISAs of rabbit IgG in phosphate-buffered saline (PBS), and achieved a low limit of detection (LOD) of 27 pM (comparable to that of standard and paper-based ELISAs). In order to demonstrate the clinical application of our multi-step immunoassay platform, we also conducted sandwich ELISAs to quantify the protein level of an inflammatory cytokine, namely tumor necrosis factor (TNF)-α, in surgically injured laryngeal tissues of rats. The protein levels of TNF-α were shown similar between the conventional and μPAD ELISAs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 it