ArticleNature communications2024
Data-centric artificial olfactory system based on the eigengraph.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Boosting Artificial Olfaction: Visual Cues-Enhanced Gas Classification by a Bimodal Neuromorphic Device.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Tailoring Chemiresistive Nanomaterials Toward Integrated Circuit Compatible Fabrication of Gas Sensors.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Intelligent artificial olfactory nervous system with sensory adaptation capabilities.Nature communications · 2026Article
- A High-Entropy Strategy for Chemoresistive Ethanol Sensors With Remarkably Rapid and Selective Response.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Scalable fabrication of gas sensors via spark-ablation printing of semiconductive metal oxide nanoparticles and heterostructures.Microsystems & nanoengineering · 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
- Visible Light-Driven Heterojunction Array Based on Type-I InSmall (Weinheim an der Bergstrasse, Germany) · 2026Article
- Skin-Integrated Soft Wearable XR Interfaces for Seamless and Realistic User Experience.Chemical reviews · 2025Review
- Advances in predicting human olfactory perception: from data acquisition to computational models.RSC advances · 2025Review
- Recent Advancements in Bionic Olfactory Biosensors: Components, Applications, and Future Perspectives.Chem & bio engineering · 2025Review
- Recent progress of gas sensors toward olfactory display development.Nano convergence · 2025Review
- TiOSmall (Weinheim an der Bergstrasse, Germany) · 2025Article
- Hybrid Series of Carbon-Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi-Task Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Artificial Olfactory System Enabled by Ultralow Chemical Sensing Variations of 1D SnOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Electrospinning and Nanofiber Technology: Fundamentals, Innovations, and Applications.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Selectivity in Chemiresistive Gas Sensors: Strategies and Challenges.Chemical reviews · 2025Review
- Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review.Polymers · 2024Review
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent studies of electronic nose system tend to waste significant amount of important data in odor identification. Until now, the sensitivity-oriented data composition has made it difficult to discover meaningful data to apply artificial intelligence in terms of in-depth analysis for odor attributes specifying the identities of gas molecules, ultimately resulting in hindering the advancement of the artificial olfactory technology. Here, we realize a data-centric approach to implement standardized artificial olfactory systems inspired by human olfactory mechanisms by formally defining and utilizing the concept of Eigengraph in electrochemisty. The implicit odor attributes of the eigengraphs were mathematically substantialized as the Fourier transform-based Mel-Frequency Cepstral Coefficient feature vectors. Their effectiveness and applicability in deep learning processes for gas classification have been clearly demonstrated through experiments on complex mixed gases and automobile exhaust gases. We suggest that our findings can be widely applied as source technologies to develop standardized artificial olfactory systems.
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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.