Self‐Assembly of Soft Cellulose Nanospheres into Colloidal Gel Layers with Enhanced Protein Adsorption Capability for Next‐Generation Immunoassays
Bibliographic record
Abstract
Abstract Soft cationic core/shell cellulose nanospheres can deform and interpenetrate allowing their self‐assembly into densely packed colloidal nanogel layers. Taking advantage of their water‐swelling capacity and molecular accessibility, the nanogels are proposed as a new and promising type of coating material to immobilize bioactive molecules on thin films and paper. The specific and nonspecific interactions between the cellulosic nanogel and human immunoglobulin G as well as bovine serum albumin (BSA) are investigated. Confocal microscopy, electroacoustic microgravimetry, and surface plasmon resonance are used to access information about the adsorption behavior and viscoelastic properties of self‐assembled nanogels. A significant BSA adsorption capacity on nanogel layers (17 mg m−2) is measured, 300% higher compared to typical polymer coatings. This high protein affinity further confirms the promise of the introduced colloidal gel layer, in increasing sensitivity and advancing a new generation of substrates for a variety of applications, including immunoassays, as demonstrated in this work.
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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 | 0.000 | 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".