Clicking Fluorometric Probes on Micropatterned Glass Microfiber Filters for ppb-Level Copper Quantitation
Bibliographic record
Abstract
In this work, we demonstrated the efficacy of combining covalent modification via the click reaction and benchtop machine-cutting of glass microfiber filters (GMFs) to fabricate microfluidic paper-based analytical devices (μPADs) for performing ultrasensitive fluorometric assays. This has been accomplished by activating the GMF surface through the self-assembly of propargyl-PEG3-triethoxysilane (PPTES) and the click reaction to immobilize fluorometric probes (i.e., 3-azido-7-hydroxycoumarin, A-7-HC). Specifically for making the device, mini-discs of GMF ( d = 0.5 cm or any other shape and size) are machine-cut and treated with a low concentration of PPTES in dichloromethane, while the remainder of the original GMF substrate is rendered superhydrophobic by reacting with octadecyltrimethoxysilane (OTMS). Following the reassembly of the device, the Cu(I)-catalyzed alkyne–azide cycloaddition (CuAAC)-based assay is performed by introducing A-7-HC in the presence of different concentrations of copper standards. Coupling with smartphone imaging under customized LED irradiation, we showed that the assay sites (discs) are distinct and uniform (free of coffee-ring effect); the achieved limits of detection (9.2 ± 0.8 ppb) for copper are impressive in comparison with traditionally prepared devices (drop-casting reagents on photolithographically patterned GMF/paper). As we have validated the device with the results obtained with atomic absorption spectroscopy of commercial copper diary supplements, this work reveals the potential of combining covalent surface modification with benchtop patterning techniques to create flexible μPADs for performing quantitative colorimetric and fluorometric assays.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".