Redox Active Polyaniline‐Chitosan Supported Nano Silver Composites with Tunable Surface Structure and Catalytic Activity
Bibliographic record
Abstract
ABSTRACT Surface and interface engineering of heterogeneous catalysts requires an understanding of structure‐activity relationships to enhance catalytic activity and selectivity. Herein, we elaborate on the role of (bio)polymer supports on catalytic activity in polyaniline‐chitosan nanocomposites (NCs) that contain Ag nanoparticles (Ag@PNI‐ (x) ‐CHT). These NCs can be tailored to enhance selectivity in reductive transformations of model dyes: methylene blue (MB) and p ‐nitrophenol (PNP). Adjusting the composition (x) of the (bio)polymer fraction is shown to regulate the catalyst structure, stability, activity, and selectivity. The findings herein underscore the critical role of chitosan content and surface modification in governing the interaction with the dye to yield improved catalyst performance and selectivity. The greater Langmuir binding affinity ( K L = ∼68 L mmol −1 ) and monolayer adsorption capacity ( q m = 0.63 mmol.g −1 ) of Ag@PNI‐ (25) ‐CHT with MB versus PNP reveal that modification of catalysts with chitosan led to surface accumulation of hydrophilic substrates onto the catalyst. Greater catalyst activity parallels the greater rate constant and values of K L between the catalyst with dyes, and higher q m values correlate with enhanced catalytic performance. The structure‐property relationships reveal a unique reduction mechanism for MB over the PNP dye system, wherein polyaniline serves as the surface‐active reduction site.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".