Covalent Chemistry on Carbon Nanotubes: From Electronic Fundamentals to Sensor Applications
Bibliographic record
Abstract
Carbon nanotube devices are particularly well-suited to build chemical or biological electronic nanosensors due to their inherent nanoscale channel, exceptional electrical conductance and high sensitivity to charge transfer. This charge sensitivity is mostly unselective though, which means that functionality must be added to the nanotube sidewall in order to tailor its affinity to specific chemical species. Single-point functionalization is particularly desirable to allow probing molecules at the individual level. Among the various functionalization types available for carbon nanotubes, covalent chemistry provides the most robustness and reproducibility. However, its invasive nature is known to alter the electronic performance of the nanotubes, and achieving single-point covalent binding on pristine nanotubes is challenging. Here we present experimental work providing fundamental insight of the impact of covalent reactions on carbon nanotubes electronic properties, as well as recent advances on the use of covalently chemistry for assembling single-molecule nanosensors. First, electrical transport experiments are used to probe the electronic states of carbon nanotubes fully covered with covalent adducts. Results on numerous individual nanotube devices show that addition of monovalent groups such as aryl derivatives severely disrupt the nanotube electronic bands and also generate graft-induced localized states in the nanotube band gap [1]. Oppositely, divalent grafting using carbene-based addition reactions is found to leave the nanotube electronic properties unaltered [2]. We discuss the mechanisms behind these results based on symmetry and conjugation considerations. Second, high-resolution lithography patterning and aryldiazonium chemistry are used to add covalent adducts on various portions of carbon nanotubes devices, from several microns down to 20-nm segments. The intensity of conductance alteration is found to scale exponentially with the length of the exposed segment, with large variations in decay constants between devices. All devices nevertheless present a robust 20% current drop for the shortest exposed segments, which points to a consistent small number of binding sites. Finally, we demonstrate the ability of this approach to bind few molecules onto carbon nanotubes with controlled position and high yield over arrays of hundreds of devices, which opens a very promising route for assembling a variety of carbon-nanotube-based single-molecule electronic nanosensors. [1] D. Bouilly, J. Laflamme-Janssen, J. Cabana, M. Côté, R. Martel, under revision (2014) [2] D. Bouilly, J. Cabana, R. Martel. Appl. Phys. Lett. 101, 053116 (2012)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".