PUBLICATIONS

  1. Özdemir, P; Yıldırım, R, Current state of the art and future perspectives for photocatalytic CO2 reduction, Journal of Energy Chemistry, 2026, 116, 914-932, https://doi.org/10.1016/j.jechem.2026.01.055
  2. Günay, ME; Yıldırım, R, Machine Learning for Catalytic Reaction Systems: A Framework for Complex Chemical Processes, ACS Engineering Au, 2026, 6, 48-67, https://doi.org/10.1021/acsengineeringau.5c00093
  3. Yılmaz, B; Yıldırım, R, Analysis of photocatalytic water splitting and CO2 reduction activity of halide perovskites: A machine learning approach, Applied Catalysis B: Environment and Energy, 2026, 385, 126275, https://doi.org/10.1016/j.apcatb.2025.126275
  4. Fırtın, A; Yılmaz, A; Eroğlu, I; Yıldırım, R; Eroğlu, D, Text-mining and bibliometric analyses for proton exchange membrane electrolyzers, International Journal of Hydrogen Energy, 2026, 211, 153666, https://doi.org/10.1016/j.ijhydene.2026.153666
  5. Mendes, PSF; López, N; De, S; Fushimi, R; Yıldırım, R; Trunschke, A, Data as a Key Resource in Catalysis: A Community Account, ChemCatChem, 2025, 17, e01226, https://doi.org/10.1002/cctc.202501226
  6. Yüksel, K; Eroğlu, D; Yıldırım, R, Key Aspects of Sustainable and High-Performance K-Ion Batteries: A Machine Learning Approach, Journal of Power Sources, 2025, 657, 238215, https://doi.org/10.1016/j.jpowsour.2025.238215
  7. Özdemir, P; Yıldırım, R, ML@ ChemE: Past, Present, and Future of Machine Learning in Chemical Engineering, ChemBioEng Reviews, 2025, 12, 4, e70012, https://doi.org/10.1002/cben.70012
  8. Tiras, K; Oral, B; Arslan, NK; Alemdar, S; Yıldırım, R; Uzun, A, Using machine learning to guide the synthesis of supported palladium catalysts with desired palladium dispersion, Journal of Catalysis, 2025, 448, 116176, https://doi.org/10.1016/j.jcat.2025.116176
  9. Zırhlıoglu, İG; Yıldırım, R, Machine learning analysis of photocatalytic CO2 reduction on perovskite materials, Materials Research Bulletin, 2025, 188, 113436, https://doi.org/10.1016/j.materresbull.2025.113436
  10. Oral, B; Karakoyun, R; Bilgin, E; Yıldırım, R, Machine learning analysis of photocatalytic glycerol reforming for hydrogen production, International Journal of Hydrogen Energy, 2025, 142, 1014–1025, https://doi.org/10.1016/j.ijhydene.2025.04.027
  11. Kilic, A; Uzun, A; Yıldırım, R; Eroglu, D, Ionic liquid electrolytes for metal-air batteries: High-throughput screening and machine learning modeling, Electrochimica Acta, 2025, 524, 145997, https://doi.org/10.1016/j.electacta.2025.145997
  12. Yılmaz, B; Ceylan, HB; Yaz, G; Yıldırım, R, Performance Assessment of Catalyst Materials for CO2 Hydrogenation to Methanol Using Explainable Machine Learning, Energy & Fuels, 2025, 39, 21, 9956–9967, https://doi.org/10.1021/acs.energyfuels.5c00792
  13. Coşgun, A; Günay, ME; Yıldırım, R, Explainable machine learning analysis of tri-reforming of biogas for sustainable syngas production, International Journal of Hydrogen Energy, 2025, 127, 595–607, https://doi.org/10.1016/j.ijhydene.2025.04.213
  14. Oral, B; Coşgun, A; Kılıç, A; Eroğlu, D; Günay, ME; Yıldırım, R, Machine learning for a sustainable energy future, Chemical Communications, 2025, 61, 1342-1370, https://doi.org/10.1039/D4CC05148C
  15. Coşgun, A; Oral, B; Günay, ME; Yıldırım, R, Machine Learning–Based Analysis of Sustainable Biochar Production Processes, BioEnergy Research, 2024, 75, 540-546, https://doi.org/ 10.1007/s12155-024-10796-7
  16. Oral, B; Maddah, HA; Yıldırım, R, Predictive Modeling and SHAP (SHapley Additive ExPlanations) Analysis for Enhancing Natural Dye‐Sensitized Solar Cell Performance, Solar RRL, 2024, 8, 2400432, https://doi.org/10.1002/solr.202400432
  17. Özcan, EC; Uner, D; Yıldırım, R, Effects of dead volume and inert sweep gas flow on photocatalytic hydrogen evolution over Pt/TiO2, International Journal of Hydrogen Energy, 2024, 1, 17, https://doi.org/10.1016/j.ijhydene.2024.03.218
  18. Kılıç, A; Abdelaty, O; Zeeshan, M; Uzun, A; Yıldırım, R; Eroğlu, D, Selection of ionic liquid electrolytes for high-performing lithium-sulfur batteries: an experiment-guided high-throughput machine learning analysis, Chemical Engineering Journal, 2024, 490, 151562, https://doi.org/10.1016/j.cej.2024.151562
  19. Oral, B; Coşgun, A; Günay, ME; Yıldırım, R, Machine learning-based exploration of biochar for environmental management and remediation, Journal of Environmental Management, 2024, 360, 121162, https://doi.org/10.1016/j.jenvman.2024.121162
  20. Bienkowski, K; Solarska, R; Trinh, L; Widera-Kalinowska, J; Al-Anesi, B; Liu, M; Grandhi, GK; Vivo, P; Oral, B; Yılmaz, B; Yıldırım, R, Halide Perovskites for Photoelectrochemical Water Splitting and CO2 Reduction: Challenges and Opportunities, ACS catalysis, 2024, 14, 6603-6622, https://doi.org/10.1021/acscatal.3c06040
  21. Özdemir, P; Yıldırım, R, Photocatalytic glycerol reforming on Pt, Au and Cu supported by reduced TiO2 under visible light irradiation, International Journal of Hydrogen Energy, 2023, https://doi.org/10.1016/j.ijhydene.2023.05.089
  22. Özsoysal, S; Oral, B; Yıldırım, R, Analysis of photocatalytic CO 2 reduction over MOFs using machine learning, Journal of Materials Chemistry A, 2024, 12, 5748-5759, https://doi.org/10.1039/D3TA07001H
  23. Genc, DE; Ozbek, O; Oral, B; Yıldırım, R; Ercan, NI, Phytochemicals in Pancreatic Cancer Treatment: A Machine Learning Study, ACS Omega, 2024, 9, 1, 413–421, https://doi.org/10.1021/acsomega.3c05861
  24. Kılıç, A; Oral, B; Yıldırım, R; Eroğlu, D, Machine learning for beyond Li-ion batteries: Powering the research, Journal of Energy Storage, 2023, 73, 109057, https://doi.org/10.1016/j.est.2023.109057
  25. Oral, B; Tekin, B; Eroğlu, D; Yıldırım, R, Assessment of Na-Ion Battery Performance Using Machine Learning, Electrochemical Society Meeting Abstracts, 2023, 243, 882-882, https://doi.org/10.1149/MA2023-015882mtgabs
  26. Yılmaz, B; Oral, B; Yıldırım, R, Machine learning analysis of catalytic CO2 methanation, 2023, https://doi.org/10.1016/j.ijhydene.2022.12.197
  27. Coşgun, A; Günay, ME; Yıldırım, R, Machine learning for algal biofuels: A critical review and perspective for future, Green Chemistry, 2023, 25, 3311-3321 https://doi.org/10.1039/D3GC00389B
  28. Oral, B; Tekin, B; Eroğlu, D; Yıldırım, R, Performance analysis of Na-ion batteries by machine learning, 2022, 549, 232126, https://doi.org/10.1016/j.jpowsour.2022.232126
  29. Kılıç, A; Yıldırım, R; Eroğlu, D, Assessment of ionic liquid electrolytes for high‐performance lithium‐sulfur batteries using machine learning, International Journal of Energy Research, 2022, https://doi.org/10.1002/er.8611
  30. Saadetnejad, D; Oral, B; Can, E; Yıldırım, R, Machine learning analysis of gas phase photocatalytic CO2 reduction for hydrogen production, International Journal of Hydrogen Energy, 2022, https://doi.org/10.1016/j.ijhydene.2022.02.030
  31. Oral, B; Can, E; Yıldırım, R, Analysis of photoelectrochemical water splitting using machine learning, International Journal of Hydrogen Energy, 2022, 45, 19633-19654, https://doi.org/10.1016/j.ijhydene.2022.01.011
  32. Coşgun, A; Günay, ME; Yıldırım, R, Analysis of lipid production from Yarrowia lipolytica for renewable fuel production by machine learning, Fuel, 2022, 315, 122817, https://doi.org/10.1016/j.fuel.2021.122817
  33. Suvarna, M; Jahirul, MI; Aaron-Yeap, WH; Augustine, CV; Umesh, A; Rasul, MG; Günay, ME; Yıldırım, R; Janaun, J, Predicting biodiesel properties and its optimal fatty acid profile via explainable machine learning, Renewable Energy, 2022, 189, 245-258, https://doi.org/10.1016/j.renene.2022.02.124
  34. Yılmaz, B; Odabaşı, Ç; Yıldırım, R, Efficiency and Stability Analysis of 2D/3D Perovskite Solar Cells Using Machine Learning, Energy Technology, 2022, 10, 2100948, https://doi.org/10.1002/ente.202100948
  35. Oral, B; Saadetnejad, D; Yıldırım, R, Correction to: Photocatalytic hydrogen production on chemically etched strontium titanate surfaces, Reaction Kinetics, Mechanisms and Catalysis, 2021, 134, 1091, https://doi.org/10.1007/s11144-021-02096-4
  36. Can, E; Uralcan, B; Yıldırım, R, Enhancing Charge Transfer in Photocatalytic Hydrogen Production over Dye-Sensitized Pt/TiO2 by Ionic Liquid Coating, Applied Energy Materials, 2021, 4,10931-10939, https://doi.org/10.1021/acsaem.1c01553
  37. Kilic, A; Eroğlu, D; Yıldırım, R, Determining the Key Performance Factors in Lithium-Oxygen Batteries Using Machine Learning, Journal of The Electrochemical Society, 2021, 168, 090544, https://doi.org/10.1149/1945-7111/ac2662
  38. Kilic, A; Yıldırım, R; Eroğlu, D, Machine Learning Analysis of Ni/SiC Electrodeposition Using Association Rule Mining and Artificial Neural Network, Journal of The Electrochemical Society, 2021, 168, 062514, https://doi.org/10.1149/1945-7111/ac0aaa
  39. Can, E; Jalal, A; Zırhlıoğlu, IG; Uzun, A; Yıldırım, R, Predicting water solubility in ionic liquids using machine learning towards design of hydro-philic/phobic ionic liquids, Journal of Molecular Liquids, 2021, 332, 115848, https://doi.org/10.1016/j.molliq.2021.115848
  40. Altıntaş, Ç; Altundal, OF; Keskin, S; Yıldırım, R, Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation, Journal of Chemical Information and Modeling, 2021, 61, 2131-2146, https://doi.org/10.1021/acs.jcim.1c00191
  41. Gül, G; Yıldırım, R; İleri-Ercan, N, Cytotoxicity analysis of nanoparticles by association rule mining, Environmental Science: Nano, 2021, 8, 937-949, https://doi.org/10.1039/D0EN01240H
  42. Gunay, ME; Yıldırım, R, Recent advances in knowledge discovery for heterogeneous catalysis using machine learning, Catalysis Reviews, 2021, 63, 120-164, https://doi.org/10.1080/01614940.2020.1770402
  43. Yılmaz, B; Yıldırım, R, Critical review of machine learning applications in perovskite solar research, Nano Energy, 2021, 80, 105546, https://doi.org/10.1016/j.nanoen.2020.105546
  44. Coşgun, A; Günay, ME; Yıldırım, R, Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning, Renewable Energy, 2021, 163, 1299-1317, https://doi.org/10.1016/j.renene.2020.09.034
  45. Oral, B; Saadetnejad, D; Yıldırım, R, Photocatalytic hydrogen production on chemically etched strontium titanate surfaces, Reaction Kinetics, Mechanisms and Catalysis, 2020, 131, 953–963, https://doi.org/10.1007/s11144-020-01872-y
  46. Odabasi, C; Yildirim, R, Assessment of Reproducibility, Hysteresis, and Stability Relations in Perovskite Solar Cells Using Machine Learning, ENERGY TECHNOLOGY, 2020, 8, 1901449, https://doi.org/10.1002/ente.201901449
  47. 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, https://doi.org/10.1016/j.cej.2020.124117
  48. Leba, A; Yildirim, R, Determining most effective structural form of nickel-cobalt catalysts for dry reforming of methane, INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2020, 45, 7, 4268-4283, https://doi.org/10.1016/j.ijhydene.2019.12.020
  49. Odabasi, C; Yildirim, R, Machine learning analysis on stability of perovskite solar cells, SOLAR ENERGY MATERIALS AND SOLAR CELLS, 2020, 205, https://doi.org/10.1016/j.solmat.2019.110284
  50. 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, https://doi.org/10.1038/s41560-019-0529-5
  51. Jalal, A; Can, E; Keskin, S; Yildirim, R; Uzun, A, Selection rules for estimating the solubility of C-4-hydrocarbons in imidazolium ionic liquids determined by machine-learning tools, JOURNAL OF MOLECULAR LIQUIDS, 2019, 284, 511-521, https://doi.org/10.1016/j.molliq.2019.03.182
  52. 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, https://doi.org/10.1016/j.jcou.2019.02.010
  53. 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, https://doi.org/10.1021/acscombsci.8b00150
  54. 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, https://doi.org/10.1016/j.apcatb.2018.09.104
  55. Odabasi, C; Yildirim, R, Performance analysis of perovskite solar cells in 2013-2018 using machine-learning tools, NANO ENERGY, 2019,56, 770-791, https://doi.org/10.1016/j.nanoen.2018.11.069
  56. Saadetnejad, D; Yildirim, R, Photocatalytic hydrogen production by water splitting over Au/Al-SrTiO3, INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2018, 43, 2, 1116-1122, https://doi.org/10.1016/j.ijhydene.2017.10.154
  57. 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, https://doi.org/10.1016/j.cattod.2017.05.012
  58. Uzunoglu, C; Leba, A; Yildirim, R, Oxidative coupling of methane over Mn-Na2WO4 catalyst supported by monolithic SiO2, APPLIED CATALYSIS A-GENERAL, 2017, 547, 22-29, https://doi.org/10.1016/j.apcata.2017.08.020
  59. Baysal, M; Gunay, ME; Yildirim, R, Decision tree analysis of past publications on catalytic steam reforming to develop heuristics for high performance: A statistical review, INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2017, 42, 1, 243-254, https://doi.org/10.1016/j.ijhydene.2016.10.003
  60. 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, https://doi.org/10.1002/bbb.1650
  61. Ozyonum, GN; Yildirim, R, Water gas shift activity of Au Re catalyst over microstructured cordierite monolith wash-coated by ceria, INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2016, 41, 12, 5513-5521, https://doi.org/10.1016/j.ijhydene.2016.02.025
  62. Tapan, NA; Gunay, ME; Yildirim, R, Constructing global models from past publications to improve design and operating conditions for direct alcohol fuel cells, CHEMICAL ENGINEERING RESEARCH & DESIGN, 2016, 105, 162-170, https://doi.org/10.1016/j.cherd.2015.11.018
  63. Odabasi, C; Gunay, ME; Yildirim, R, Knowledge extraction for water gas shift reaction over noble metal catalysts from publications in the literature between 2002 and 2012, INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2014, 39, 11, 5733- 5746, https://doi.org/10.1016/j.ijhydene.2014.01.160
  64. Gunay, ME; Yildirim, R, Developing global reaction rate model for CO oxidation over Au catalysts from past data in literature using artificial neural networks, APPLIED CATALYSIS A-GENERAL, 2013, 468, 395-402, https://doi.org/10.1016/j.apcata.2013.08.056
  65. 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, https://doi.org/10.1002/cctc.201200665
  66. Gunay, ME; Yildirim, R, Modeling preferential CO oxidation over promoted Au/Al2O3 catalysts using decision trees and modular neural networks, CHEMICAL ENGINEERING RESEARCH & DESIGN, 2013, 91, 5, 874-882, https://doi.org/10.1016/j.cherd.2012.08.017
  67. Leba, A; Davran-Candan, T; Onsan, ZI; Yildirim, R, DRIFTS study of selective CO oxidation over Au/gamma- Al2O3 catalyst, CATALYSIS COMMUNICATIONS, 2012, 29, 6-10, https://doi.org/10.1016/j.catcom.2012.09.010
  68. Gunay, ME; Davran-Candan, T; Yildirim, R, Analysis of O2 Adsorption Stability and Strength Over Gold Clusters Using DFT and Logistic Regression, JOURNAL OF CLUSTER SCIENCE, 2012, 23, 2, 221-235, https://doi.org/10.1007/s10876-011-0422-2
  69. Gunay, ME; Akpinar, F; Onsan, ZI; Yildirim, R, Investigation of water gas-shift activity of Pt-MOx-CeO2/Al2O3 (M = K, Ni, Co) using modular artificial neural networks, INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2012, 37, 3, 2094-2102, https://doi.org/10.1016/j.ijhydene.2011.09.148
  70. Ozdemir, S; Onsan, ZI; Yildirim, R, Selective CO oxidation over monolithic Au:MgO/Al2O3 catalysts, JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY, 2012, 87, 1, 58-64, https://doi.org/10.1002/jctb.2682
  71. Davran-Candan, T; Demir, M; Yildirim, R, Analysis of reaction mechanisms and kinetics of preferential CO oxidation over Au/gamma-Al2O3, REACTION KINETICS MECHANISMS AND CATALYSIS, 2011, 104, 2, 389-398, https://doi.org/10.1007/s11144-011-0370-8
  72. Gunay, ME; Yildirim, R, Neural network Analysis of Selective CO Oxidation over Copper-Based Catalysts for Knowledge Extraction from Published Data in the Literature, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2011, 50, 22, 12488- 12500, https://doi.org/10.1021/ie2013955
  73. Davran-Candan, T; Tezcanli, ST; Yildirim, R, Selective CO oxidation over promoted Au/gamma- Al2O3 catalysts in the presence of CO2 and H2O in the feed, CATALYSIS COMMUNICATIONS , 2011, 12, 12, 1149-1152, https://doi.org/10.1016/j.catcom.2011.04.007
  74. Davran-Candan, T; Gunay, ME; Yildirim, R, Structure and activity relationship for CO and O2 adsorption over gold nanoparticles using density functional theory and artificial neural networks, JOURNAL OF CHEMICAL PHYSICS, 2010, 132, 17, https://doi.org/10.1063/1.3369007
  75. Gunay, ME; Yildirim, R, Analysis of selective CO oxidation over promoted Pt/ Al2O3 catalysts using modular neural networks: Combining preparation and operational variables, APPLIED CATALYSIS A-GENERAL, 2010, 377, 174-180, https://doi.org/10.1016/j.apcata.2010.01.033
  76. Gunes, H; Yildirim, R, Low Temperature Water-Gas Shift Reaction on Au-CeO2/ Al2O3 Catalysts, INTERNATIONAL JOURNAL OF CHEMICAL REACTOR ENGINEERING, 2010, 8, https://doi.org/10.2202/1542-6580.2424
  77. Davran-Candan, T; Aksoylu, AE; Yildirim, R, Reaction pathway analysis for CO oxidation over anionic gold hexamers using DFT, JOURNAL OF MOLECULAR CATALYSIS A-CHEMICAL, 2009, 306, 118-122, https://doi.org/10.1016/j.molcata.2009.02.034
  78. Uguz, KE; Yildirim, R, Comparative study of selective CO oxidation over Pt-Co-M/ Al2O3 catalysts (M = Ce, Mg, Mn, Zr, Fe) in hydrogen-rich streams: effects of a second promoter, TURKISH JOURNAL OF CHEMISTRY, 2009, 33, 4, 545-553, https://doi.org/10.3906/kim-0809-36
  79. Gunay, ME; Nikerel, IE; Oner, ET; Kirdar, B; Yildirim, R, Simultaneous modeling of enzyme production and biomass growth in recombinant Escherichia coli using artificial neural networks, BIOCHEMICAL ENGINEERING JOURNAL ,2008, 42, 3, 329- 335, https://doi.org/10.1016/j.bej.2008.08.002
  80. Gunay, ME; Yildirim, R, Neural network aided design of Pt-Co-Ce Al2O3catalyst for selective CO oxidation in hydrogen-rich streams, CHEMICAL ENGINEERING JOURNAL, 2008, 140, 324-331, https://doi.org/10.1016/j.cej.2007.09.047
  81. Ersoz, Y; Yidirim, R; Akin, AN, Development of an active platine-based catalyst for the reaction of H2 production from NaBH4, CHEMICAL ENGINEERING JOURNAL, 2007, 134, 282- 287, https://doi.org/10.1016/j.cej.2007.03.059
  82. Ozyonum, GN; Akin, AN; Yildirim, R, Kinetic study of selective CO oxidation over Pt-Co-Ce/ Al2O3 catalyst in hydrogen-rich streams, TURKISH JOURNAL OF CHEMISTRY, 2007, 31, 5, 445-453, https://journals.tubitak.gov.tr/cgi/viewcontent.cgi?article=2444&context=chem
  83. Nikerel, IE; Oner, ET; Kirdar, B; Yildirim, R, Optimization of medium composition for biomass production of recombinant Escherichia coli cells using response surface methodology, BIOCHEMICAL ENGINEERING JOURNAL, 2006, 32, 1, 1-6, https://doi.org/10.1016/j.bej.2006.08.009
  84. Uysal, G; Akin, AN; Onsan, ZI; Yildirim, R, Preferential CO oxidation over Pt-SnO2/ Al2O3 in hydrogen rich streams containing CO2 and H2O (CO removal from H-2 with PROX, CATALYSIS LETTERS, 2006, 111, 173- 176, https://doi.org/10.1007/s10562-006-0143-6
  85. Uysal, G; Akin, AN; Onsan, ZI; Yildirim, R, Hydrogen clean-up by preferential CO oxidation over Pt-Co-Ce/MgO, CATALYSIS LETTERS, 2006, 108, 193-196, https://doi.org/10.1007/s10562-006-0039-5
  86. Ince, T; Uysal, G; Akin, AN; Yildirim, R, Selective low-temperature CO oxidation over Pt-Co-Ce/ Al2O3 in hydrogen-rich streams, APPLIED CATALYSIS A-GENERAL, 2005, 292, 171-176, https://doi.org/10.1016/j.apcata.2005.06.002
  87. Nikerel, IE; Toksoy, E; Kirdar, B; Yildirim, R, Optimizing medium composition for TaqI endonuclease production by recombinant Escherichia coli cells using response surface methodology, PROCESS BIOCHEMISTRY, 2005, 40, 5, 1633-1639, https://doi.org/10.1016/j.procbio.2004.06.017
  88. Ozdemir, C; Akin, AN; Yildirim, R, Low temperature CO oxidation in hydrogen rich streams on Pt-SnO2/ Al2O3 catalyst using Taguchi method, APPLIED CATALYSIS A-GENERAL, 2004, 258, 2, 145-152, https://doi.org/10.1016/j.apcata.2003.08.020
  89. Ozdemir, C; Akin, AN; Yildirim, R, Maidmization of total surface area of Pt-SnO2/ Al2O3 catalyst by the Taguchi method OREAN JOURNAL OF CHEMICAL ENGINEERING, 2003, 20, 5, 840-843, https://doi.org/10.1007/BF02697285
  90. Yildirim, R; Senkan, SM, Formation of high-molecular-weight by-products during the pyrolysis and oxidative pyrolysis of CH3Cl INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 1995, 34, 5, 1842-1852, https://doi.org/10.1021/ie00044a035
  91. Gonenc, ZS; Yildirim, R; Belerbaykal, AB; Önsan, ZI, Adsorption parameters of some C5-C8 paraffinic hydrocarbons on platinum alumina by gas-chromatographic pulse techniques, APPLIED CATALYSIS A-GENERAL, 1993, 103, 1, 35-42, https://doi.org/10.1016/0926-860x(93)85171-k
  92. Yildirim, R; Senkan, SM, Pyrolysis and oxidative pyrolysis of CH3Cl in steam, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 1993, 32, 3, 438-444, https://doi.org/10.1021/ie00015a006
  93. Yildirim, R; Senkan, SM, Experimental-study of the pyrolysis and oxidative pyrolysis of C2H5Cl, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 1992, 31, 1, 75-80, https://doi.org/10.1021/ie00001a011

Location

  • Boğaziçi University
  • Dept. of Chemical Engineering
  • Bebek, 34342 İstanbul - Turkey

Contact Us

  • yildirra@bogazici.edu.tr
  • Telephone :+90 (212) 359 7248

ChE-BIM