Peroxide-Induced Tuning of the Conductivity of Nanometer-Thick MoS<sub>2</sub> Films for Solid-State Sensors
Bibliographic record
Abstract
Abstract Applications of molybdenum disulfide (MoS2) in energy storage devices, solar cells, electrocatalysts, and sensors require good electrical conductivity. However, neither of the current ways to prepare conductive MoS2 (lithium intercalation and hydrothermal processes) is easily amenable to scale-up. A possible alternative pathway is the modulation of the electronic properties of the semiconducting form of MoS2 through structural defects. Here, we report the preparation of nanoscale conductive MoS2 flakes by treating exfoliated 2H-MoS2 with dilute aqueous hydrogen peroxide at room temperature. Sheet resistance measurements as well as Raman and photoelectron spectroscopy reveal the partial formation of hydrogen molybdenum bronze (HxMoO3) and substoichiometric MoO3–y, which help tune the conductivity of the nanometer-scale thin films without impacting the sulfur-to-molybdenum ratio. We have cast the material into thin film networks to fabricate highly stable chemiresistive pH sensors. Our work introduces a straightforward and safe way of preparing a conductive form of MoS2 and its application as a low-cost solid-state sensor.
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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".