Tuning the Chemical and Mechanical Properties of Conductive MoS<sub>2</sub> Thin Films by Surface Modification with Aryl Diazonium Salts
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Molybdenum disulfide (MoS 2 ) is a promising material for applications in sensors, energy storage, energy conversion devices, solar cells, and fuel cells. Because many of those applications require conductive materials, we recently developed a method for preparing a conductive form of MoS 2 (c-MoS 2 ) using dilute aqueous hydrogen peroxide in a simple and safe way. Here, we investigate modulating the chemical and mechanical surface properties of c-MoS 2 thin films using diazonium chemistry. In addition to a direct passivation strategy of c-MoS 2 with diazonium salts for electron-withdrawing groups, we also propose a novel in situ synthetic pathway for modification with electron-donating groups. The obtained results are examined by Raman spectroscopy and X-ray photoelectron spectroscopy. The degree of surface passivation of pristine and functionalized c-MoS 2 films was tested by exposing them to aqueous solutions of different metal cations (Fe 2+, Zn 2+, Cu 2+, and Co 2+ ) and detecting the chemiresistive response. While pristine films were found to interact with several of the cations, modified films did not. We propose that a surface charge transfer mechanism is responsible for the chemiresistive response of the pristine films, while both modification routes succeeded at complete surface passivation. Functionalization was also found to lower the coefficient of friction for semiconducting 2H-MoS 2, while all conductive materials (modified or not) also had lower coefficients of friction. This opens up a pathway to a palette of dry lubricant materials with improved chemical stability and tunable conductivity. Thus, both in situ and direct diazonium chemistries are powerful tools for tuning chemical and mechanical properties of conductive MoS 2 for new devices and lubricants based on conductive MoS 2 .
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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 | 0.000 | 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".