NanoPADs and nanoFACEs: an optically transparent nanopaper-based device for biomedical applications
Bibliographic record
Abstract
Paper has been a popular material of choice for biomedical applications including for bioanalysis and cell biology studies. Regular cellulose paper-based devices, however, have several key limitations including slow fluid flow; large sample retention in the paper matrix for microfluidic paper-based analytical device (μPAD) application; serious solvent evaporation issues, and contamination and poor control of experimental conditions for cell culture. Here, we describe the development of two novel platforms, nanopaper-based analytical devices (nanoPADs) and nanofibrillated adherent cell-culture platforms (nanoFACEs), that use nanofibrillated cellulose (NFC) paper, simply called nanopaper, as the substrate material to create transparent, pump-free and hollow-channel paper-based microfluidic devices. Due to the natural hydrophilicity and nanoscale pore size of nanopaper, the hollow-channel microfluidic devices can realize a totally pump-free flow without any complicated surface chemical functionalization on the nanopaper. Experimental results showed that within a certain range, larger hollow channel size leads to faster pump-free flows. Different from previous designs of paper-based hollow-channel microfluidic devices, the high transparency of the nanopaper substrate enabled the integration of various optical sensing and imaging technologies together with the nanoPADs and nanoFACEs. As proof-of-concept demonstrations, we demonstrated the use of nanoPADs for colorimetric sensing of glucose and surface-enhanced Raman spectroscopy (SERS)-based detection of environmental pollutants and applied the nanoFACEs to the culture of human umbilical vein endothelial cells (HUVECs). These demonstrations show the great promise of nanoPADs and nanoFACEs for biomedical applications such as chemical/bioanalysis and cell biology studies.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".