Heterostructured α‐Fe<sub>2</sub>O<sub>3</sub>@ZnO@ZIF‐8 Core–Shell Nanowires for a Highly Selective MEMS‐Based ppb‐Level H<sub>2</sub>S Gas Sensor System
Bibliographic record
Abstract
Abstract Highly selective and sensitive H2S sensors are in high demand in various fields closely related to human life. However, metal oxide semiconductors (MOSs) suffer from poor selectivity and single MOS@metal organic framework (MOF) core–shell nanocomposites are greatly limited due to the intrinsic low sensitivity of MOF shells. To simultaneously improve both selectivity and sensitivity, heterostructured α‐Fe2O3@ZnO@ZIF‐8 core–shell nanowires (NWs) are meticulously synthesized with the assistance of atomic layer deposition. The ZIF‐8 shell with regular pores and special surface functional groups is attractive for excellent selectivity and the heterostructured α‐Fe2O3@ZnO core with an additional electron depletion layer is promising with enhanced sensitivity compared to a single MOS core. As a result, the heterostructured α‐Fe2O3@ZnO@ZIF‐8 core–shell NWs achieve remarkable H2S sensing performance with a high response (Rair/Rgas = 32.2 to 10 ppm H2S), superior selectivity, fast response/recovery speed (18.0/31.8 s), excellent long‐term stability (at least over 3 months), and relatively low limit of detection (down to 200 ppb) at low operating temperature of 200 °C, far beyond α‐Fe2O3@ZIF‐8 or α‐Fe2O3@ZnO core–shell NWs. Furthermore, a micro‐electromechanical system‐based H2S gas sensor system with low power consumption is developed, holding great application potential in smart cities.
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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".