Bibliographic record
Abstract
My thesis research work focuses on the integration of potential functionality into microfluidic paper-based analytical devices (μPADs) for a variety of practical applications. The current academic efforts on μPADs are dedicated to seeking to investigate potential incorporating functionality, including i) detection and readout, ii) programming and timing, iii) multi-step processing, and iv) surface chemistry from laboratory-scale research to commercialization.In the research, we first developed an all-in-one μPAD to improve the functionality of μPADs on detection and readout to diagnose cardiac biomarkers in real human blood. By introducing high surface-to-volume ratio and superior electron-transferring properties, zinc oxide nanowires (ZnO NWs) were used to amplify electrochemical signal outputs on μPADs. The ZnO-NW-decorated μPADs were employed in biosensing for ultrasensitive measurements that were demonstrated equivalent to commercial enzyme-linked immunosorbent assay (ELISA) kits. The all-in-one μPAD enables a portable, utility, and user-friendly detection in a highly integrated paper-based device as the promising detection and readout integration for μPADs. To investigate and incorporate functionality of programming and timing, and multi-step processing on μPADs, we established an autonomous platform to turn on and off heat-responsive shame-memory-polymer (SMP) actuators to connect and disconnect paper channels on a μPAD by manipulating paper cantilever arms. The developed platform is capable of realizing sample-in-answer-out (SIAO) detections in fully-automated behaviors for multi-step assays such as ELISAs. We demonstrated its practical feasibility of on-site testing for rat and human samples by running automated ELISAs. The platform can be further tuned for DNA sensing that is suitable to improve its sensing ability from protein-level to nucleic-acid-level. We also carried out a systematic investigation of comparing five single-step biofunctionalization methods for μPADs to explore surface chemistry functionality. The chemically-modified μPADs provided more active binding sites on paper surface for covalent binding of proteins. The modification methods were compared and the potassium periodate (KIO4)-based biofunctionalization route was then utilized with superior surface chemistry performance in experimental sections of automated ELISAs on the autonomous platform
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".