Ramazan YILDIRIM
PhD: Chemical Engineering, University of California, USA, 1993
MS: Chemical Engineering, Boğaziçi University, Turkey, 1988
BS: Chemical Engineering, Ege University, Turkey, 1983
Management Consultant, 1994-2000
- Catalyst development
- Molecular modeling in catalysis
- Data mining, modeling and knowledge extraction in catalysis
- Renewable energy
1. Ozdemir C., Akin A. N., Yildirim R., Low temperature CO oxidation in hydrogen rich streams on Pt-SnO2/Al2O3 catalyst using Taguchi method", Applied Catalysis: A, 258 45-152, 2004
2. Nikerel ›.E., Toksoy E., Kirdar B., Yildirim R., Optimizing Medium Composition for TaqI Endonuclease Production by Recombinant Escherichia coli Using Response Surface Methodology, Process Biochemistry, 40, 1633-1639, 2005
3. Uysal G., Akin A.N., Onsan Z.I., Yildirim R., ìHydrogen clean-up by preferential CO oxidation over Pt-Co-Ce/MgO, Catalysis Letters, 108 193-196, 2006
4. Günay M. E., Yildirim R., Neural network aided design of Pt-Co-Ce/Al2O3 catalyst for selective oxidation in hydrogen-rich streams, Chemical Engineering Journal, 140,) 324-331, 2008
5. Davran-Candan T., Aksoylu A.E., Yildirim R., Reaction pathway analysis for CO oxidation over anionic gold hexamers using DFT, Journal of Molecular Catalysis, 306, 118-122, 2009
6. Davran-Candan T., Günay M.E, Yildirim R., Structure and activity relationship for CO and O2 adsorption over gold nanoparticles using DFT and artificial neural networks, Journal of Chemical Physics, 132, 174113, (2010)
7. Gunay, ME; Yildirim, R, Knowledge Extraction from Catalysis of the Past: A Case of Selective CO Oxidation over Noble Metal Catalysts between 2000 and 2012, CHEMCATCHEM, 2013, 5, 6, 1395-1406
8.Sener, AN; Gunay, ME; Leba, A; Yildirim, R, Statistical review of dry reforming of methane literature using decision tree and artificial neural network analysis, CATALYSIS TODAY, 2018, 299, 289-302,
9. Tapan, NA; Yildirim, R; Gunay, ME, Analysis of past experimental data in literature to determine conditions for high performance in biodiesel production, BIOFUELS BIOPRODUCTS & BIOREFINING-BIOFPR, 2016, 10, 4, 422-434
10. Saadetnejad, D; Yildirim, R, Photocatalytic hydrogen production by water splitting over Au/Al-SrTiO3, INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2018, 43, 2, 1116-1122,
11. Yildiz, MG; Davran-Candan, T; Gunay, ME; Yildirim, R, CO2 capture over amine-functionalized MCM-41 and SBA-15: Exploratory analysis and decision tree classification of past data, JOURNAL OF CO2 UTILIZATION, 2019, 31, 27-42
12. Gulsoy, Z; Sezginel, KB; Uzun, A; Keskin, S; Yildirim, R, Analysis of CH4 Uptake over Metal-Organic Frameworks Using Data-Mining Tools, ACS COMBINATORIAL SCIENCE , 2019, 21, 4, 257-268
13. Can, E; Yildirim, R, Data mining in photocatalytic water splitting over perovskites literature for higher hydrogen production, APPLIED CATALYSIS B-ENVIRONMENTAL, 2019, 242, 267-283
14. Odabasi, C; Yildirim, R, Performance analysis of perovskite solar cells in 2013-2018 using machine-learning tools, NANO ENERGY, 2019,56, 770-791
15.Kilic, A; Odabasi, C; Yildirim, R; Eroglu, D, Assessment of critical materials and cell design factors for high performance lithium-sulfur batteries using machine learning, CHEMICAL ENGINEERING JOURNAL, 2020, 390
16. Odabasi, C; Yildirim, R, Machine learning analysis on stability of perovskite solar cells, SOLAR ENERGY MATERIALS AND SOLAR CELLS, 2020, 205
17. Khenkin, MV; Katz, EA; Abate, A; Bardizza, G; Berry, JJ; Brabec, C; Brunetti, F; Bulovic, V; Burlingame, Q; Di Carlo, A; Cheacharoen, R; Cheng, YB; Colsmann, A; Cros, S; Domanski, K; Dusza, M; Fell, CJ; Forrest, SR; Galagan, Y; Di Girolamo, D; Gratzel, M; Hagfeldt, A; von Hauff, E; Hoppe, H; Kettle, J; Kobler, H; Leite, MS; Liu, S; Loo, YL; Luther, JM; Ma, CQ; Madsen, M; Manceau, M; Matheron, M; McGehee, M; Meitzner, R; Nazeeruddin, MK; Nogueira, AF; Odabasi, C; Osherov, A; Park, NG; Reese, MO; De Rossi, F; Saliba, M; Schubert, US; Snaith, HJ; Stranks, SD; Tress, W; Troshin, PA; Turkovic, V; Veenstra, S; Visoly-Fisher, I; Walsh, A; Watson, T; Xie, HB; Yildirim, R; Zakeeruddin, SM; Zhu, K; Lira-Cantu, M, Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures, NATURE ENERGY, 2020, 5 ,1, 35-49,
18. Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning, A Coşgun, ME Günay, R Yıldırım, Renewable Energy 163, 1299-1317, 2021
19. Critical review of machine learning applications in perovskite solar research, B Yılmaz, R Yıldırım, Nano Energy, 80, 105546, 2021
20. Recent advances in knowledge discovery for heterogeneous catalysis using machine learning, M Erdem Günay, R Yıldırım, Catalysis Reviews, 1-45 (available online)
