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A machine‐learning framework integrating multimodel prediction, feature selection, and SHAP interpretability is developed to uncover structure–performance relationships of Cu‐based CO2‐to‐methanol catalysts. The optimized XGBoost model and an online prediction platform enable accurate selectivity prediction and data‐driven catalyst design.
Conglong Su +11 more
wiley +1 more source
Functionalized carbon nanoparticles for smartphone-based sensing of formaldehyde. [PDF]
Cavallaro A +13 more
europepmc +1 more source
China's petrochemical plants' CO<sub>2</sub> emissions and high-impact contributors for carbon-neutrality production. [PDF]
Chen X +5 more
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Growth mechanism and formaldehyde sensing of mixed phase cobalt oxide nanowalls. [PDF]
Barala S +3 more
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A Perspective on Lanthanide-Containing Nanocomposite Hydrogels: Current Research and Future Directions. [PDF]
An YN, Yeh YC.
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Air Pollutants in Puerto Rico: Key Pollutants and Carcinogenic Properties. [PDF]
Kaya D +6 more
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Electrostatically mediated phosphorescence enhancement of micro-nano composites. [PDF]
Xu W +7 more
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Emission of formaldehyde from indoor surface materials
Kolarik, Barbara +2 more
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Formaldehyde Emissions from Dry Low Emissions Industrial Gas Turbines
Volume 3B: Combustion, Fuels, and Emissions, 2022Abstract Formaldehyde is listed as a Hazardous Air Pollutant (HAP) by various regulatory agencies around the world because of its role as a carcinogen. To address this impact, several countries have regulated formaldehyde emissions from land-based gas turbines. In the United States (U.S.), the federal regulatory level is 91 ppb and state
Ivan Carlos +3 more
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