Stable sensor layers self-assembled onto surfaces using azobenzene-containing polyelectrolytes
Bibliographic record
Abstract
Polyelectrolytes functionalized with photoisomerizable azobenzene chromophores were multi-layered onto inorganic and metal surfaces, by the repeated adsorption from dilute aqueous solution, alternating between oppositely charged polymers. These layer-by-layer ionically self-assembled thin films were investigated for their suitability as sensor host materials with respect to the criteria of control over physical layer properties, versatility to different substrates and adsorption geometries, and stability of the formed layers to heat, solvent, and sonication. Layer thickness was found to be controllable between 5 A and 500 nm by varying the total number of layers deposited, from a single monolayer to 1000 layers. Control over individual layer thickness was achieved by varying the pH of the adsorption solutions. This multi-layer self-assembly was demonstrated to be suitable for a wide range of metal and inorganic substrates, and achievable with surfaces of high curvature (r = 50 nm), and confined geometry. The deposited layers exhibited good stability to desorption in a range of organic solvents, aqueous temperatures to 100 degrees C, and cleaning protocols such as sonication. The laser-induced geometric isomerization of the azobenzene chromophores was shown to be strongly dependent on aqueous solution properties, demonstrating an application as a hydroxide ion sensor in highly alkaline media.
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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) | 0.001 | 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 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".