Experimental comparison of surface chemistries for biomolecule immobilization on paper-based microfluidic devices
Bibliographic record
Abstract
Abstract Biomolecules (e.g. proteins and nucleic acids) as target analytes of microfluidic paper-based analytical devices ( μ PADs) are usually immobilized on a cellulose paper substrate (with intrinsically anionic surface) through physical adsorptions by van der Waals forces and electrostatic interaction thanks to cationic patches on the biomolecule. However, the physical adsorption could lead to weak biomolecule-substrate binding strength and thus low biosensing performance. Benefitting from the abundance of hydroxyl groups on the cellulose paper, chemical modification based on specific surface chemistries is capable of biofunctionalization on the μ PADs by providing functional groups for covalent bindings with the target biomolecule. There are many previous reports on chemical modifications of cellulose surface for improvement of biomolecule immobilization. Nevertheless, no study has been performed on experimental evaluation of modification efficiencies of various biofunctionalization methods in the context of biosensing applications. In this paper, we compare five surface chemistries for protein immobilization on μ PADs made from pure cellulose paper. For each chemical modification method, surface analyses were first conducted to monitor the surface modification process. Then, paper-based fluorometric experiments and colorimetric enzyme-linked immunosorbent assays (ELISA) were carried out on paper substrates modified by the five surface chemistries to compare their efficiencies of covalent protein immobilization. Finally, a stability experiment was carried out on the five types of surface-modified paper after 30 d storage. It was demonstrated that the potassium periodate (KIO 4 )-modified cellulose paper has the best performance with 53% increase in the signal output and 59% decrease in background noise of the colorimetric ELISA, and only 13% bioactivity loss after the 30 d storage. The comparison results provide a valuable experimental guideline for selecting the suitable surface chemistry for protein immobilization on μ PADs.
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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".