Rapid and Selective NH<sub>3</sub> Sensing by Porous CuBr
Bibliographic record
Abstract
Abstract Fast and selective detection of NH 3 at parts‐per‐billion (ppb) concentrations with inexpensive and low‐power sensors represents a long‐standing challenge. Here, a room temperature, solid‐state sensor is presented consisting of nanostructured porous (78%) CuBr films. These are prepared by flame‐aerosol deposition of CuO onto sensor substrates followed by dry reduction and bromination. Each step is monitored in situ through the film resistance affording excellent process control. Such porous CuBr films feature an order of magnitude higher NH 3 sensitivity and five times faster response times than conventional denser CuBr films. That way, rapid (within 2.2 min) sensing of even the lowest (e.g., 5 ppb) NH 3 concentrations at 90% relative humidity is attained with outstanding selectivity (30–260) over typical confounders including ethanol, acetone, H 2 , CH 4 , isoprene, acetic acid, formaldehyde, methanol, and CO, superior to state‐of‐the‐art sensors. This sensor is ideal for hand‐held and battery‐driven devices or integration into wearable electronics as it does not require heating. From a broader perspective, the process opens exciting new avenues to also explore other bromides and classes of semiconductors (e.g., sulfides, nitrides, carbides) currently not accessible by flame‐aerosol technology.
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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) | 0.000 | 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".