Morphology Tuning of NiMo Layered Double Hydroxide-Modified MXene for Highly Sensitive Detection of NH<sub>3</sub> with Long-Term Stability
Bibliographic record
Abstract
The sensitive detection of ammonia at room temperature is crucial for both the chemical industry and human health. In this study, phosphorized NiMo layered double hydroxide (NiMo-P)-modified MXene-based composite materials were prepared using a combination of hydrothermal and calcination processes. A two-dimensional (2D)/2D van der Waals heterojunction formed between the MXene and NiMo-P phases facilitated the interlayer shuttling of charge carriers during gas adsorption. Furthermore, the composite material acquired abundant acidic sites during phosphorization, which fundamentally enhanced its NH 3 sensing performance. In addition, the morphology of the composite was effectively controlled by tuning the poly(methyl methacrylate). The hollow microspherical MXene@NiMo-P (MX-s@NiMo-P) composite material demonstrates highest sensing response to NH 3 . At 25 °C and 45% relative humidity (RH), the response of MX-s@NiMo-P to 100 ppm of NH 3 was as high as 188.9%. After 30 d, the gas-sensing performance of the MX-s@NiMo-P sensor reached 93.4% of the initial value, highlighting its long-term stability over the 30-d period. This study describes an effective approach to improve the response of MXene-based gas sensor with long-term stability.
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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".