Treffer 1 - 6 von 6 für Suche 'Taromi, Arsia Afshar', Suchdauer: 0,41s Treffer weiter einschränken
  1. 1
    Green diesel production via continuous hydrotreatment of triglycerides over mesostructured γ-alumina supported NiMo/CoMo catalysts

    Green diesel production via continuous hydrotreatment of triglycerides over mesostructured γ-alumina supported NiMo/CoMo catalysts von Afshar Taromi, Arsia, Kaliaguine, Serge

    Veröffentlicht in Fuel processing technology

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  2. 2
    Hydrodeoxygenation of triglycerides over reduced mesostructured Ni/γ-alumina catalysts prepared via one-pot sol-gel route for green diesel production

    Hydrodeoxygenation of triglycerides over reduced mesostructured Ni/γ-alumina catalysts prepared via one-pot sol-gel route for green diesel production von Afshar Taromi, Arsia, Kaliaguine, Serge

    Veröffentlicht in Applied catalysis. A, General

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  3. 3
    Synthesis of ordered mesoporous γ-alumina – Effects of calcination conditions and polymeric template concentration

    Synthesis of ordered mesoporous γ-alumina – Effects of calcination conditions and polymeric template concentration von Afshar Taromi, Arsia, Kaliaguine, Serge


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  4. 4
    Green diesel production via continuous hydrotreatment of triglycerides over mesostructured ?-alumina supported NiMo/CoMo catalysts

    Green diesel production via continuous hydrotreatment of triglycerides over mesostructured ?-alumina supported NiMo/CoMo catalysts von Taromi, Arsia Afshar, Kaliaguine, Serge

    Veröffentlicht in Fuel processing technology

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  5. 5
    Hydrodeoxygenation of triglycerides over reduced mesostructured Ni/?-alumina catalysts prepared via one-pot sol-gel route for green diesel production

    Hydrodeoxygenation of triglycerides over reduced mesostructured Ni/?-alumina catalysts prepared via one-pot sol-gel route for green diesel production von Taromi, Arsia Afshar, Kaliaguine, Serge

    Veröffentlicht in Applied catalysis. A, General

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  6. 6
    XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions

    XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions von Karbassiyazdi, Elika, Fattahi, Fatemeh, Yousefi, Negin, Tahmassebi, Amirhessam, Taromi, Arsia Afshar, Manzari, Javad Zyaie, Gandomi, Amir H, Altaee, Ali, Razmjou, Amir

    Veröffentlicht in Environmental research

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