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 ₂ nanonetworks functionalized with Au and Pd nanocatalysts are fabricated via a systematic deposition process, enhancing gas diffusion and reaction kinetics. Stability improvements through controlled aging process reduce the coefficient of variation to below 5% across seven target gases: acetone, hydrogen, ethanol, carbon monoxide, propane, isoprene, and toluene. The platform exhibits exceptional deep learning performance, achieving over 99.5% classification accuracy using a residual network model, even in high‐humidity environments (up to 80% relative humidity) and at parts‐per‐trillion detection limits. This study highlights the synergy between nanostructure engineering and AI, establishing a robust framework for next‐generation bioinspired electronic nose systems with enhanced reliability and analytical capability.

Polycyclic aromatic hydrocarbons (PAHs) are known to be representative carcinogenic environmental pollutants with high toxicity. However, information on the potential ecological and environmental risks of PAH contamination in soil remains scarce. Thus, this study was evaluated the potential ecological risks of PAHs in soils of five Korean areas (Gunsan (GS), Gwangju, Yeongnam, Busan, and Gangwon) using organic carbon (OC)-normalized analysis, mean effect range-median quotient (M-ERM-Q), toxic equivalent quantity (TEQ) analysis, and risk quotient (RQ) derived by the species sensitivity distribution model. In this study, atmospheric particulate matter has a significant effect on soil pollution in GS through the presence of hopanes and the similar pattern of PAHs in soil and atmospheric PAHs. From analysis of source identification, combustion sources in soils of GS were important PAH sources. For PAHs in soils of GS, the OC-normalized analysis, M-ERM-Q, and TEQ analysis have 26.78 × 10⁵ ng/g-OC, 0.218, and 49.72, respectively. Therefore, the potential ecological risk assessment results showed that GS had moderate-high ecological risk and moderate-high carcinogenic risk, whereas the other regions had low ecological risk and low-moderate carcinogenic risk. The risk level (M-ERM-Q) of PAH contamination in GS was similar to that in Changchun and Xiangxi Bay in China. The Port Harcourt City in Nigeria for PAH has the highest risk (M-ERM-Q = 4.02 and TEQ = 7923). Especially, compared to China (RQ_Phe =0.025 and 0.05), and Nigeria (0.059), phenanthrene showed the highest ecological risk in Korea (0.001-0.18). Korea should focus on controlling the release of PAHs originating from the PM in GS.

The demand for high-performance two-dimensional gas barrier materials is increasing owing to their potential for application in optoelectronic devices. These materials can help the devices maintain their properties over a long period. Therefore, in this study, we investigated the gas barrier performance of hexagonal boron nitride (h-BN) monolayers grown on copper foils via electrochemical polishing (ECP). The ECP treatment helped reduce the surface roughness of the copper foils. As a result, the nucleation density was reduced and highly crystalline h-BN monolayers were produced. The gas barrier performance of h-BN monolayers on copper foils with ECP was comparable to that of graphene. Our finding demonstrates the potential of monolayer h-BN as a high-performance and economical gas barrier material for organic-based optoelectronic devices.

Recently, laser-scribed graphene (LSG) has gained significant attention in sensor applications due to its cost-effectiveness, simplicity, environmental friendliness, excellent conductivity, and high thermal stability. Compared with many other graphene fabrication methods, LSG stands out as a promising approach for developing advanced sensors. In this study, we present a rapid, one-step synthesis for fabricating a highly sensitive and selective LSG sensor for self-activated chemiresistive detection of nitrogen dioxide (NO2). Sensors were characterized using field emission scanning electron microscopy (FE-SEM), high-resolution transmission electron microscopy (HR-TEM), Raman spectroscopy, X-ray diffraction (XRD), X-ray photoemission spectroscopy, Hall measurement, and a thermographic infrared (IR) camera. The LSG-based chemiresisitve sensor exhibited high electrical conductivity with a sheet resistance of around 23.4 ± 0.8 Ω/sq at room temperature and highly sensitive, selective, and fully reversible responses to NO2 without external heating. Three-dimensional porous graphene plays an essential role in long-term reliability (42 days) in its self-activated state. Also, the sensor demonstrated a detection limit of parts per billion (ppb) levels of 2.68 ppb with high reliability. This rapid, one-step synthesis method provides a promising approach for the scalable production of high-performance graphene-based sensors for gas detection.