Instant‐in‐Air Liquid Metal Printed Ultrathin Tin Oxide for High‐Performance Ammonia Sensors
Bibliographic record
Abstract
Abstract Liquid metal‐based printing techniques are emerging as an exemplary platform for harvesting non‐layered 2D materials with a thickness down to a few nanometres, leading to an ultra‐large surface‐area‐to‐volume ratio that is ideal for sensing applications. In this work, the synthesis of 2D tin dioxide (SnO 2 ) by exfoliating the surface oxide of molten tin is reported which highlights the enhanced sensing capability of the obtained materials to ammonia (NH 3 ) gas is reported. It is demonstrated that amperometric gas sensors based on liquid metal‐derived 2D SnO 2 nanosheets can achieve excellent NH 3 sensing performance at low temperature (150 °C) with and without UV light assistance. Detection over a wide range of NH 3 concentrations (5–500 ppm) is observed, revealing a limit of detection at the parts per billion (ppb) level. The 2D SnO 2 nanosheets also feature excellent cross‐interference performance toward different organic and inorganic gas species, showcasing a high selectivity. Further, ab initio DFT calculations reveal the NH 3 adsorption mechanism is dominated by chemisorption with a charge transfer into 2D SnO 2 nanosheets. In addition, a proof of concept for prototype flexible ammonia sensors is demonstrated by depositing 2D SnO 2 on a polyimide substrate, signifying the high potential of employing liquid metal printed SnO 2 for realizing wearable gas 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) | 0.000 | 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 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".