Microstructure Optimization of Mos<sub>2</sub>/Sepiolite Nanocomposites via a Surfactant-Assisted Hydrothermal Strategy for High Efficiency Photocatalysis
Bibliographic record
Abstract
The unique structure of two-dimensional molybdenum disulfide (MoS2) with rich active sites makes it a promising catalyst, whereas it also brings structural instability. Surfactant-assisted synthesis of MoS2 can be regarded as a simple way to regulate the microstructure. In this work, the surfactant additives were adopted to optimize the microstructure of MoS2/sepiolite nanocomposite, and the effects of surfactants type and concentration were investigated. For the sample prepared with 1 mol/L sodium dodecyl benzene sulfonate (SDBS), it exhibits the highest intensity for the peak of MoS2 at 14.2°, highly dispersed MoS2 nanosheet on the sepiolite, the lowest absorption intensity of Rhodamine B (RhB) at 553 nm of the wavelength, and the highest photocatalytic activity which is 2.5 times and 4.2 times higher than those prepared with 1 mol/L hexadecyl trimethyl ammonium bromide (CTAB) and 1 mol/L polyvinyl pyrrolidone (PVP) after a 150-minute irradiation, respectively. The above results suggest SDBS is the optimal surfactant to optimize the microstructure of MoS2/sepiolite nanocomposite. This work could provide new insights into the fabrication of high-quality MoS2-based nanocomposite.
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 | 0.000 |
| 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".