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Experimental Optimization and Modeling of Sodium Sulfide Production from H2 S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network
A facile and green method for the production of novel and potentiallybiocompatible poly(amide-imide)/ZrO2 poly(vinyl alcohol)nanocomposites containing trimellitylimido-l-leucine linkages
A facile and green method for the production of novel and potentiallybiocompatible poly(amide-imide)/ZrO2 poly(vinyl alcohol)nanocomposites containing trimellitylimido-l-leucine linkages
Efficient surface modification of MWCNTs with vitamin B1 and production of poly(ester-imide)/MWCNTs nanocomposites containing L-phenylalanine moiety Thermal and microscopic study
An innovative strategy for the production of novel magnetite poly(vinyl alcohol) nanocomposite films with double-capped synthesized Fe3O4 nanoparticles with citric acid and vitamin C
Determination of volatile residual solvents in pharmaceutical products by static and dynamic headspace liquid-phase microextraction combined with gas chromatography-flame ionization detection
Determination of volatile residual solvents in pharmaceutical products by static and dynamic headspace liquid-phase microextraction combined with gas chromatography-flame ionization detection
Comparison and evaluation of dust detection algorithms using MODIS Aqua/Terra Level 1B data and MODIS/OMI dust products in the Middle East
Comparison and evaluation of dust detection algorithms using MODIS Aqua/Terra Level 1B data and MODIS/OMI dust products in the Middle East
Surface modification of MWCNTs with glucose and their utilization for the production of environmentally friendly nanocomposites using biodegradable poly(amide-imide) based on N-trimellitylimido-S-valine matrix
Surface modification of MWCNTs with glucose and their utilization for the production of environmentally friendly nanocomposites using biodegradable poly(amide-imide) based on N-trimellitylimido-S-valine matrix
Predicting hydrogen production in porous foams for steam methane reforming: A combined approach using computational fluid dynamics and machine learning regression models
Predicting hydrogen production in porous foams for steam methane reforming: A combined approach using computational fluid dynamics and machine learning regression models
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