Real-Time Catalytic Activity of Bimetallic Ru-Pt Nanoparticles Attached on Carbon Nanotube Electronic Devices
Bibliographic record
Abstract
Catalysis plays a crucial role in chemical research and industry, yet its dynamics at the nanoscale has been little explored. Here, we use carbon nanotube transistors to measure in real time the catalytic activity of metallic nanoparticles. First, we present a method based on covalent nanotube functionalization to selectively attach a small number of nanoparticles on an individual carbon nanotube device. We demonstrate the covalent attachment of bimetallic Ru-Pt clusters and their aggregation into less than 10 nanoparticles (RuPtNP) on each nanotube device, using a combination of techniques including electrical spectroscopy and atomic force microscopy. Second, we monitor the catalytic transformation of dimethylphenylsilane in dimethylphenylsilanol with water in real-time, through changes in the nanotube electrical conductance. Upon exposure to silane, RuPtNP-decorated carbon nanotube devices show a rapid change in electrical conductance that decays slowly back to its initial state. We present the effect of silane concentration and electrostatic doping of the nanotube, and discuss possible mechanisms for the interaction between the catalytic reaction and the electrical signature. Carbon nanotube electronic sensors form a powerful tool to investigate various catalysis reactions, providing real-time monitoring over a broad range of time scales.
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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 | 0.001 | 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".