Aysegul Kilic

Boğaziçi University — Turkey

Engineering · Physical Sciences

8h-index213citations19works4.42yr avg
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Research Topics

Advancements in Battery Materials(11), Advanced Battery Materials and Technologies(11), Advanced Battery Technologies Research(8), Fish Ecology and Management Studies(2), Machine Learning in Materials Science(2)

Publications19 total

High Throughput Screening of MOFs for Lithium-Sulfur Battery Cathodes
ECS Meeting Abstracts·2025
Ionic liquid electrolytes for metal-air batteries: High-throughput screening and machine learning modeling
Electrochimica Acta·2025· 6 cited
Machine learning for a sustainable energy future
Chemical Communications·2024· 8 citedOpen Access
Predictive uncertainty in state-estimation drives active sensing
Bioinspiration & Biomimetics·2024· 1 citedOpen Access
High-Performance Lithium-Sulfur Batteries with Atomic V- and Co-Modified Ketjen Black-Sulfur Composite Cathode
ECS Meeting Abstracts·2024
Text Mining Analysis of Beyond Lithium-Ion Batteries
ECS Meeting Abstracts·2024
Model-Based Unified State and Phase Estimation for Torque Actuated Dissipative Spring-Mass Runner Using Limited Sensory Information
2024Open Access
Selection of ionic liquid electrolytes for high-performing lithium-sulfur batteries: An experiment-guided high-throughput machine learning analysis
Chemical Engineering Journal·2024· 18 cited
Metal‐Organic Framework/Graphene Nanoplatelet Composite Increases Rate Performance of Lithium–Sulfur Batteries
Energy Technology·2024· 5 cited
Electrochemical Impedance Spectroscopy of Li‐S Batteries: Effect of Atomic Vanadium‐ and Cobalt‐Modified Ketjen Black‐Sulfur Cathode, Sulfur Loading, and Electrolyte‐to‐Sulfur Ratio
ChemElectroChem·2024· 6 citedOpen Access
Predictive Uncertainty in State-Estimation Drives Active Sensing
2023· 1 citedOpen Access
Machine learning for beyond Li-ion batteries: Powering the research
Journal of Energy Storage·2023· 34 cited
Atomic V- and Co-Modified Ketjen Black–Sulfur Composite for High-Performance Lithium–Sulfur Batteries
ACS Applied Energy Materials·2023· 12 citedOpen Access
Assessment of ionic liquid electrolytes for high‐performance lithium‐sulfur batteries using machine learning
International Journal of Energy Research·2022· 12 cited
Determination of Critical Materials and Cell Design Factors for Enhanced Li-S Battery Performance Using Machine Learning
ECS Meeting Abstracts·2021
Determining the Key Performance Factors in Lithium-Oxygen Batteries Using Machine Learning
Journal of The Electrochemical Society·2021· 14 cited
Machine Learning Analysis of Ni/SiC Electrodeposition Using Association Rule Mining and Artificial Neural Network
Journal of The Electrochemical Society·2021· 9 cited
Characterization of the Effect of Cell Design on Li−S Battery Resistance Using Electrochemical Impedance Spectroscopy
ChemElectroChem·2021· 30 cited
Assessment of critical materials and cell design factors for high performance lithium-sulfur batteries using machine learning
Chemical Engineering Journal·2020· 57 cited

Frequent Co-authors

Damla Eroğlu(15), Ramazan Yıldırım(9), Burcu Oral(3), Hira Fazal(3), Nazakat Ali(3), Jiantao Zai(3), Xuefeng Qian(3), Mustafa Mert Ankaralı(3), Çağla Odabaşı(2), Boxu Dong(2), Şahika Sena Bayazit(2), Osman Kaan Karagöz(2), Emin Yusuf Aydın(2), İsmail Uyanık(2), Omar Abdelaty(1), Muhammad Zeeshan(1), Alper Uzun(1), Ahmet Çoşgun(1), M. Erdem Günay(1), Prof. Alper Uzun(1)