Affinity Immobilization of Semiconductor Quantum Dots and Metal Nanoparticles on Cellulose Paper Substrates
Bibliographic record
Abstract
Colloidal semiconductor quantum dots (QDs), metal nanoparticles, and cellulose paper are materials with numerous applications in bioanalysis and beyond. The functional properties of QDs and metal NPs are substantially different than those of cellulose, such that their integration with cellulose paper is potentially enabling for many applications. Here, we characterize and evaluate multiple chemistries that modify cellulose paper substrates for the affinity-based immobilization of QDs, gold nanoparticles (Au NPs), and platinum nanoparticles (Pt NPs). These chemistries include grafting of cellulose fibers with imidazole and dithiol groups, as well as the aminosilanization of cellulose fibers (both with and without subsequent grafting with dithiol groups). Cellulose modifications and nanoparticle immobilization are characterized by multiple techniques, including, but not limited to, X-ray photoelectron spectroscopy, scanning electron microscopy, and optical imaging, extinction, and fluorescence measurements. We demonstrate the on-paper immobilization of color-tuned mixtures of QDs, on-paper patterning of QDs by microcontact printing, and post-immobilization enhancement of energy transfer and model assays of protease activity. The robustness of QD photoluminescence is also evaluated between immobilization chemistries. Paper-immobilized Au NPs and Pt NPs are evaluated as potential substrates for SERS and as supported catalysts for a model decolorization reaction. Our cumulative results indicate that there may not be a one-size-fits-all immobilization chemistry. Instead, the immobilization chemistry should be tailored and optimized for the downstream application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".