2D Palladium Sulphate for Visible‐Light‐Driven Optoelectronic Reversible Gas Sensing at Room Temperature
Bibliographic record
Abstract
2D metal sulphides (MSs) have attracted enormous amounts of attention in developing high‐performance gas sensors. 2D noble metal sulphides and their derivatives, however, have been less studied due to their predominant nonlayered crystal structures for inefficient exfoliation, despite their surface and peculiar optoelectronic properties. Herein, we successfully synthesize 2D palladium sulphate (PdSO4) from palladium sulphide (PdS) bulk crystals by liquid‐phase exfoliation, in which the presence of oxygen species in the exfoliation solvent plays a key role in the sulphate transformation. Ultrathin 2D PdSO4planar nanosheets, with thicknesses of ≈3 nm and submicrometer lateral dimensions, exhibit a broad absorption across the visible spectrum, a narrow bandgap of ≈1.35 eV, and a nanosecond scaled long exciton lifetime, which are all suitable for the visible‐light‐driven optoelectronic gas sensing applications. The 2D PdSO4‐based sensor demonstrates a reversible, selective, and sensitive response toward ppb‐leveled NO2gas at blue light irradiation, featuring a response factor of ≈3.28% for 160 ppb NO2, a low limit of detection of 1.84 ppb, anda > 3 times response factor enhancement over other gases. Herein, the possibility of realizing 2D ultrathin noble metal sulphide compounds from their nonlayered crystal structures and strong potentials in developing high‐performance chemical sensors is explored.
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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".