All-thermoplastic nanoplasmonic microfluidic device for transmission SPR biosensing
Bibliographic record
Abstract
Early and accurate disease diagnosis still remains a major challenge in clinical settings. Biomarkers could potentially provide useful tools for the detection and monitoring of disease progression, treatment safety and efficacy. Recent years have witnessed prodigious advancement in biosensor development with research directed towards rapid, real-time, label-free and sensitive biomarker detection. Among emerging techniques, nanoplasmonic biosensors pose tremendous potential to accelerate clinical diagnosis with real-time multiplexed analysis, rapid and miniaturized assays, low sample consumption and high sensitivity. In order to translate these technologies from the proof-of-principle concept level to point of care clinical diagnosis, integrated, portable devices having small footprint cartridges that house low-cost disposable consumables are sought. Towards this goal, we developed an all-polymeric nanoplasmonic microfluidic (NMF) transmission surface plasmon resonance (SPR) biosensor. The device was fabricated in thermoplastics using a simple, single step and cost-effective hot embossing technique amenable to mass production. The novel 3D hierarchical mold fabrication process enabled monolithic integration of blazed nanogratings within the detection chambers of a multichannel microfluidic system. Consequently, a single hard thermoplastic bottom substrate comprising plasmonic and fluidic features allowed integration of active fluidic elements, such as pneumatic valves, in the top soft thermoplastic cover, increasing device functionality. A simple and compact transmission-based optical setup was employed with multiplexed end-point or dual-channel kinetic detection capability which did not require stringent angular accuracy. The sensitivity, specificity and reproducibility of the transmission SPR biosensor was demonstrated through label-free immunodetection of soluble cell-surface glycoprotein sCD44 at clinically relevant picomolar to nanomolar concentrations.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".