Hydrothermal Growth of Molybdenum Disulfide Composited with Electrodeposited Gold and Its Photoelectrochemical Properties
Bibliographic record
Abstract
Recently, molybdenum disulfide (MoS2) has received remarkably attention due to its excellent optical and electrical properties. Notably, the narrow bandgap of transition metal dichalcogenide (TMDC) MoS2 makes it a promising candidate for photoelectrochemical (PEC) applications. Direct growth of MoS2 via hydrothermal synthesize offers improved electrical conductivity between the MoS2 and the substrate, which allows for better application and electrical signal readings as a result of elevated electron transfer routs compared with drop-casted MoS2. Here, we studied the effect of hydrothermal growth time of MoS2 nanosheets on the morphology evolution as well as the PEC sensing properties. The time evolution studies conducted in the range of 2-10 hours. We used surface characterization method such as field emission scanning electron microscopy and analytical techniques such as chronoamperometry, cyclic voltammetry and electrochemical impedance spectroscopy to study the morphology evolution and PEC sensing properties. Our results revealed that the direct growth of MoS2 not only enhances the sensor signal but also encourages nanocomposites of MoS2 with gold- a key to simultaneouse amplification of the optical and electrical properties. Effect of applied bias potential in the range of 0.45-0.85 V vs. Ag/AgCl on photocurrent response was also investigated. We further used 3D COMSOL simulation to assess the effect of gold thickness on the electric field intensity of the sensing MoS2/Au electrodes. Eventually, we studied sensitive and selective detection of glucose as an important molecule in the human body. Our developed MoS2/Au composite demonstrated a very low limit of detection (1.3 nM) and excellent sensitivity for non-enzymatic PEC sensing of glucose. Moreover, the selectivity of the fabricated sensor were examined by monitoring the photocurrent responses towards different interfering agents under illumination.
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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) | 0.000 | 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 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".