ArticleMicrosystems & nanoengineering2025
Pulse-driven MEMS gas sensor combined with machine learning for selective gas identification.
Article in Microsystems & nanoengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- A fabrication-aware vectorized generative representation for topology optimization of path-defined MEMS microstructures.Microsystems & nanoengineering · 2026Article
- TiSiNanomaterials (Basel, Switzerland) · 2026Article
- Multi-Scale Temporal-Dependency Learning for Single-Sensor Gas Classification.Sensors (Basel, Switzerland) · 2026Article
- Diode-Laser-Based Raman Spectroscopy Applied to the Thermodynamic Characterization of Natural Gas and Hydrogen-Enriched Natural Gas.Sensors (Basel, Switzerland) · 2026Article
- Thermally activated excess noise by subgap density-of-states in Si-doped ZnSnO thin-film transistor-type gas sensor.Microsystems & nanoengineering · 2026Article
- Scalable fabrication of gas sensors via spark-ablation printing of semiconductive metal oxide nanoparticles and heterostructures.Microsystems & nanoengineering · 2026Article
- A comprehensive review of hydrogen sensor for thermal runaway monitoring: fundamentals, recent advancements, and challenges.Microsystems & nanoengineering · 2026Review
- Recent Advances in Decoupling Strategies for Soft Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Review
- AI‑driven photonic noses: from conventional sensors to cloud‑to-edge intelligent microsystems.Microsystems & nanoengineering · 2025Review
- Machine Learning-Driven Structural Optimization of a Bistable RF MEMS Switch for Enhanced RF Performance.Micromachines · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The sensing and identification of trace gases are essential for ensuring chemical safety and protecting human health. This study introduces a low-power electronic nose system that utilizes a single sensor driven by repeated pulsed power inputs, offering a viable alternative to conventional sensor array-based methods. The sensor's compact design and suspended architecture facilitate a rapid thermal response, effectively decoupling the influences of temperature, physisorption, and charge exchange on the conductivity of the sensing material. This mechanism generates distinct gas sensing responses, characterized by alternating dual responses within a single time period. The unique dynamics of the dual signals, which vary with gas type and concentration, enable precise identification of multiple gas species using machine learning (ML) algorithms. Microfabricated through wafer-level batch processing, our innovative electronic nose system holds significant potential for battery-powered mobile devices and IoT-based monitoring applications.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.