Facile Microembossing Process for Microchannel Fabrication for Nanocellulose-Paper-Based Microfluidics
Bibliographic record
Abstract
Nanofibrillated cellulose paper (nanopaper) has gained growing interest as one promising substrate material for paper-based microfluidics, thanks to its ultrasmooth surface, high optical transparency, uniform nanofiber matrix with nanoscale porosity, and tunable chemical properties. Recently, research on nanopaper-based microfluidics has quickly advanced; however, the current technique of patterning microchannels on nanopaper (i.e., 3D printing, spray coating, or manual cutting and sticking), that is fundamental for application development, still has some limitations, such as ease-of-contamination, and more importantly, only enabling millimeter-scale channels. This paper reports a facile process that leverages the simple operations of microembossing with the convenient plastic micro-molds, for the first time, patterning nanopaper microchannels downing to 200 μm, which is 4 times better than the existing methods and is time-saving (<45 mins). We also optimized the patterning parameters and provided one quick look-up table as the guideline for application developments. As proof-of-concept, we first demonstrated two fundamental microfluidic devices on nanopaper, the laminar-mixer and droplet generator, and two functional nanopaper-based analytical devices (NanoPADs) for glucose and Rhodamine B (RhB) sensing based on optical colorimetry and surface-enhanced Raman spectroscopy, respectively. The two NanoPADs showed outstanding performance with low limits of detection (2 mM for glucose and 19fM for RhB), which are 1.25× and 500× fold improvement compared to the previously reported values. This can be attributed to our newly developed highly accurate microchannel patterning process that enables high integration and fine-tunability of the NanoPADs along with the superior optical properties of nanopaper.
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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".