Silver Nanoparticles Anchored on Single-Walled Carbon Nanotubes via a Conjugated Polymer for Enhanced Sensing Applications
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Single-walled carbon nanotubes (SWCNTs) are candidate matrices for loading metal nanoparticles (NPs) for sensor and catalytic applications owing to their high electron conductivity and mechanical strength, larger surface area, excellent chemical stability, and ease of surface modification. The performance of the formed NP/SWCNT composites is dependent on the NP size, the physical and chemical interactions between the components, and the charge transfer capabilities. Anchoring metal complexes onto the surface of SWCNTs through noncovalent interactions is a viable strategy for achieving high-level metal dispersion and high charge transfer capacities between metal NPs and SWCNTs. However, traditional metal complexes have small molecular sizes, and their noncovalent interactions with SWCNTs are limited to provide excellent sensing and catalytic capability with restricted efficiency and durability. Here, we selected poly(9,9-di- n -dodecylfluorenyl-2,7-diyl- alt -2,2′-bipyridine-5,5′) (PFBPy) to increase the noncovalent interactions between silver nanoparticles (AgNPs) and SWCNTs. A silver triflate (Ag–OTf) solution was added into a PFBPy-wrapped SWCNT solution to form Ag–PFBPy complexes on the nanotube surface, after which Ag + was photoreduced to AgNPs to form a Ag–PFBPy/SWCNT composite in the solution. In various feeding molar ratios of Ag–OTf over the BPy unit (0.4–50), the size of the formed AgNPs may be well-controlled at sub-nm levels to provide them with an energy level comparable to that of the SWCNTs. Additionally, the 2,2′-bipyridine (BPy) unit of the polymer provided a coordinating interaction with Ag + and the formed AgNPs as well. The 5,5′-linage of BPy with the fluorene unit in PFBPy ensured a straight main chain structure to retain strong π–π interactions with nanotubes before and after Ag + chelation. All of these factors confirmed a tight contact between the formed AgNPs and SWCNTs, promoting the charge transfer between them and enhancing the sensing capabilities with a 5-fold increase in humidity sensing sensitivity.
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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 | 0.001 |
| 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".