Nanopattern-Assisted Direct Growth of Peony-like 3D MoS<sub>2</sub>/Au Composite for Nonenzymatic Photoelectrochemical Sensing
Bibliographic record
Abstract
The chalcogenide material MoS2 has been recognized as a promising candidate for photoelectrochemical (PEC) applications due to its enhanced photocatalytic and electrocatalytic activities. However, few reports have been focused on the designated catalytic MoS2 for the nonenzymatic PEC sensing of small molecules. Here, we report on a novel in situ and fab-free method for the direct growth of three-dimensional (3D) porous Peony-like MoS2 nanosheets supported by nanohole-patterned TiO2 and composited with gold deposits. The direct growth resulted in enhanced electrical conductivity between the substrate and 3D-standing MoS2 nanosheets and thus the uniform distribution of gold electrodeposits from the MoS2 lattice. The hybrid 3D MoS2/gold nanocomposite demonstrated enhanced abundance of exposed catalytic edge sites and improved optic and electrical coupling, which ultimately led to excellent photoelectrochemical activities. We performed full characterization of the morphology, crystallinity, lattice configuration, and optical properties of hybrid MoS2 nanosheets via field emission scanning microscope, high-resolution transmission electron microscopy, and energy-dispersive X-ray, Raman, and UV–vis spectroscopies. The 3D COMSOL simulation also confirmed enhanced electric field distribution at the interface of the proposed 3D MoS2/gold nanocomposite electrode in comparison with other morphologies. We acquired the Peony-like 3D MoS2/Au composite for photoelectrochemical sensing of glucose in buffer and diluted plasma solutions with a very low limit of detection of 1.3 nM and superb sensitivity in plasma. Overall, we have successfully synergized both electrical and optical merits from individual components to form a novel composite, which offered an effective scaffold for the development of PEC sensors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".