ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Artificial Olfactory System Enabled by Ultralow Chemical Sensing Variations of 1D SnO
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Tailoring Chemiresistive Nanomaterials Toward Integrated Circuit Compatible Fabrication of Gas Sensors.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Bioinspired periodic mesoporous tin oxides enable steady nonequilibrium chemical sensing for enhanced gas discrimination.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Chemiresistive Effect in TiSensors (Basel, Switzerland) · 2026Article
- Scalable Photoactive NOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Lead-free halide perovskite memristors for scalable crossbar arrays.Nano convergence · 2025Review
- Artificial Olfactory System Enabled by Ultralow Chemical Sensing Variations of 1D SnOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
16 authors.
Funding
Abstract
AI-assisted electronic nose systems often emphasize sensitivity-driven datasets, overlooking the comprehensive analysis of gaseous chemical attributes critical for precise gas identification. Conventional fabrication methods generate inconsistent datasets and focus primarily on improving classification accuracy through deep learning, neglecting the fundamental role of sensor material design. This study addresses these challenges by developing a highly reliable sensor platform to standardize gas sensing for deep learning applications. Specifically, 1D SnO
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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.