Electrochemistry of robust gold nanoparticle–glassy carbon hybrids generated using a patternable photochemical approach
Bibliographic record
Abstract
Photolysis of diazirine modified small (3.9 ± 0.9 nm) gold nanoparticles (AuNP) generates a reactive interfacial carbene that then reacts via an insertion reaction to covalently attach the AuNP onto glassy carbon (GC) electrodes. This yields GC surfaces that are densely and homogeneously functionalized with AuNP. The AuNP hybrid glassy carbon electrode has been characterised by atomic force microscopy (AFM), energy-dispersive X-ray spectroscopy (EDX), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). The system is found to be robust to physical and electrochemical stresses due to the covalent bond between the AuNP and the surface formed through the carbene insertion reaction. The conductivity of the AuNP functionalized GC electrode is found to be similar to clean GC, and the AuNP serve to switch on electron transfer. The AuNP can be oxidatively desorbed from the electrode at surprisingly low potential (0.99 V). This yields a monolayer of insulator thiol/thiolate ligands that previously anchored the AuNP to the GC, which gives an interesting switch off effect of the electron transfer. We demonstrate that because the method is photoinitiated this methodology allows for spatial control and the AuNP, which can be photopatterned onto GC surfaces easily. The high stability, the good electrical conductivity, the facility of making patterns, and the ability to tune the physical and chemical properties of AuNP through its ligands, make this new functionalization method suitable for the development of sensors and electronic devices.
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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".