Pd-Loaded MoS<sub>2</sub> Nanoflowers for Enhanced Room-Temperature Methanol Sensing
Bibliographic record
Abstract
The rapid increase in environmental pollution has created a critical need for the highly sensitive detection of volatile organic compounds (VOCs). These compounds play a vital role across various sectors, spanning industries, such as healthcare, environmental surveillance, as well as the food and agriculture sectors. Pristine MoS2 and (1%, 2%, and 4%) Pd-loaded MoS2 are produced through a simple hydrothermal synthesis technique. Surface morphology, crystal structure, elemental composition, and surface area were thoroughly analyzed. The sensing performance of the synthesized pristine MoS2 and Pd-loaded MoS2 nanoflowers under dynamic flow gas sensing setup at room temperature has been analysed. The 2% Pd-loaded MoS2 exhibits 6.5 times response (65%) as compare to pristine MoS2 response (10%) to 100-ppm methanol exposure. Furthermore, the 2% Pd-loaded MoS2 sensor exhibits 6.6 ppb of limit of detection (LoD) and 21.7 ppb of limit of qualification (LoQ) values. The fabricated sensor has rapid transient performance having 35 s and 43.2 s response and recovery time, respectively. The fabricated 2% Pd-loaded MoS2 sensor exhibits excellent reproducibility, repeatability, and ultraselective behavior toward methanol gas in order to avoid the interference in the measurement by other gases.
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) | 0.001 | 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".