A Nanocellulose‐Paper‐Based SERS Multiwell Plate with High Sensitivity and High Signal Homogeneity
Bibliographic record
Abstract
Abstract Paper‐based surface‐enhanced Raman scattering (SERS) substrates have gained growing interest as an eco‐friendly and low‐cost tool for chemical and biosensing. However, paper‐based SERS substrates often suffer relatively low signal spatial homogeneity because of their nonuniform hot‐spot distribution. In this paper, a nanofibrillated cellulose paper (nanopaper) based SERS multiwell plate is developed for trace chemical detection with high sensitivity and high signal homogeneity. The SERS plate is fabricated from ultrasmooth (2,2,6,6‐tetramethylpiperidin‐1‐yl)oxyl‐oxidized NFC paper (TO‐nanopaper) through wax‐printing‐based multiwell patterning followed by silver nanoparticle (AgNP) growth based on a successive ionic layer adsorption and reaction (SILAR) process. Taking advantage of the abundance of carboxyl groups on the TO‐nanopaper, uniformly distributed and densely arranged AgNPs are successfully synthesized through the SILAR process on the NFC multiwell surface under ambient conditions. The SERS performance of the device is evaluated for testing two Raman marker chemicals, rhodamine B and 2‐naphthalenethiol, and picomolar detection limit and high Raman enhancement factor (up to 1.46 × 109) are achieved. The Raman signal mapping results show superior signal spatial homogeneity of the device with low variations (≤11%). The nanopaper‐based SERS device represents a promising SERS platform for chemical and biomolecule detections with high sensitivity and high repeatability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".