Darvaish Khan | Material Science | Research Excellence Award

Dr. Darvaish Khan | Material Science | Research Excellence Award

Dr. Darvaish Khan | Sapienza University of Rome | Italy

Dr. Darvaish Khan is a distinguished postdoctoral researcher at the Department of Chemical Engineering, Materials, and Environment, Sapienza University of Rome, Italy, with an extensive academic and research background in materials science, solid-state physics, and energy materials. He earned his Ph.D. in Materials Science and Engineering from Shanghai Jiao Tong University, China, following a Master’s from Liverpool Hope University, UK, and an M.Sc. in Solid State Physics from the University of Peshawar, Pakistan. Dr. Khan’s research primarily focuses on the design, synthesis, and characterization of metal hydrides, composites, and alloys for advanced hydrogen storage and energy applications. His expertise spans hydrogen-matter interactions, phase transitions in nanostructured environments, and modeling of solid-state metal hydrides using COMSOL Multiphysics. He has developed innovative materials through solid-state mechanochemical, hydrothermal, and wet-impregnation/infiltration methods, utilizing advanced characterization tools such as XRD, SEM, TEM, BET, DSC, TGA, FTIR, XPS, Raman spectroscopy, and Sieverts-type PCT for analyzing structural, thermal, and gas sorption properties. His work significantly contributes to improving the thermodynamics and kinetics of hydrogen sorption in metal hydrides and nanocomposites, addressing global challenges in sustainable hydrogen energy systems. Dr. Khan’s impactful research has been published in top-tier international journals, including Interdisciplinary Materials, Journal of Alloys and Compounds, ACS Applied Materials & Interfaces, Chemical Engineering Journal, and the International Journal of Hydrogen Energy. He has also served as a guest speaker at international conferences, received multiple research excellence awards, and is a reviewer for international scientific journals. As an HEC-approved Ph.D. supervisor and member of the American Chemical Society and International Society of Hydrogen Energy, Dr. Khan continues to advance interdisciplinary innovations in hydrogen storage, nanostructured materials, and sustainable energy technologies, contributing meaningfully to the global transition toward a hydrogen-based clean energy future.

Profiles: Google Scholar

Featured Publications 

Zhu, W., Panda, S., Lu, C., Ma, Z., Khan, D., Dong, J., Sun, F., Xu, H., Zhang, Q., & Zou, J. (2020). Using a self-assembled two-dimensional MXene-based catalyst (2D-Ni@Ti₃C₂) to enhance hydrogen storage properties of MgH₂. ACS Applied Materials & Interfaces, 12(45), 50333–50343.

Ma, Z., Panda, S., Zhang, Q., Sun, F., Khan, D., Ding, W., & Zou, J. (2021). Improving hydrogen sorption performances of MgH₂ through nanoconfinement in a mesoporous CoS nano-boxes scaffold. Chemical Engineering Journal, 406, 126790.

Ma, Z., Zou, J., Khan, D., Zhu, W., Hu, C., Zeng, X., & Ding, W. (2019). Preparation and hydrogen storage properties of MgH₂-trimesic acid-TM MOF (TM = Co, Fe) composites. Journal of Materials Science & Technology, 35(10), 2132–2143.

Khan, D., Zou, J., Zeng, X., & Ding, W. (2018). Hydrogen storage properties of nanocrystalline Mg₂Ni prepared from compressed 2MgH₂–Ni powder. International Journal of Hydrogen Energy, 43(49), 22391–22400.

Ma, Z., Zhang, Q., Panda, S., Zhu, W., Sun, F., Khan, D., Dong, J., Ding, W., & Zou, J. (2020). In situ catalyzed and nanoconfined magnesium hydride nanocrystals in a Ni-MOF scaffold for hydrogen storage. Sustainable Energy & Fuels, 4(9), 4694–4703.

Luis Martin Pomares | Renewable Resources | Best Researcher Award

Dr. Luis Martin Pomares | Renewable Resources | Best Researcher Award

Dr. Luis Martin Pomares | DEWA R&D | United Arab Emirates

A distinguished solar energy expert and research scientist, with over two decades of professional experience spanning solar resource assessment, satellite-based irradiance modeling, and renewable energy forecasting. His career bridges applied research, data analytics, and large-scale solar project development, contributing to the advancement of sustainable energy systems worldwide. Currently serving as a Principal Scientist and Program Director at the Dubai Electricity and Water Authority (DEWA) R&D Center, he leads initiatives in solar resource assessment, satellite image processing, and deep learning-based solar forecasting. His work includes developing data pipelines for solar forecasting using modern AI frameworks such as TensorFlow and Keras, and managing BSRN radiometric stations for high-precision solar measurements. Previously, he was a Scientist at Qatar Foundation’s QEERI, where he directed the development of the Solar Atlas of Qatar and applied remote sensing methods for regional solar mapping. Earlier, as President and Project Developer at Investigaciones y Recursos Solares Avanzados (IrSOLaV), he managed multi-megawatt PV and CSP projects across several continents and pioneered forecasting systems integrating Numerical Weather Prediction (NWP) and satellite-based nowcasting models. His expertise encompasses radiative transfer modeling, atmospheric physics, GIS-based solar mapping, and energy system simulation. He has contributed extensively to IEA Solar Heating and Cooling Tasks 36 and 46, COST Action 1002, and EU FP7 projects on solar nowcasting. He is also an active reviewer for leading journals such as Solar Energy, Journal of Solar Energy Engineering, and Atmospheric Measurement Techniques. Academically, he holds a Ph.D. in Physical Sciences (Cum Laude) from the Complutense University of Madrid, with a dissertation on solar radiation prediction using statistical methods. His publication record includes numerous peer-reviewed papers and book chapters on solar resource modeling, machine learning applications in energy forecasting, and site adaptation of solar datasets.

Profile: Google Scholar

Featured Publications

Valappil, V. K., & Martin-Pomares, L. (2025). Characterizing solar attenuation for concentrated solar power plants in Dubai using AERONET data and libRadtran. Solar Energy, 302, 114082.

Sanfilippo, A., Martin-Pomares, L., & Polo, J. (2025). Solar resources mapping: Fundamentals and applications. Springer Nature.

Nie, Y., Paletta, Q., Scott, A., Martin-Pomares, L. M., Arbod, G., Sgouridis, S., Lasenby, J., et al. (2024). Sky image-based solar forecasting using deep learning with heterogeneous multi-location data: Dataset fusion versus transfer learning. Applied Energy, 369, 123467.

Rezk, M., Tiwari, V. K., Manandhar, P., Krishnan, V., & Martin-Pomares, L. M. (2024). A CNN-based classifier for quality image selection from sky cameras. Proceedings of the 7th International Conference on Signal Processing and Information Technology (ICSPIT).

Manandhar, P., Tiwari, V. K., Rezk, M., Krishnan, V., & Martin-Pomares, L. M. (2024). Convolutional deep learning hierarchical classifier to identify synoptic sky conditions based on sky images. Proceedings of the 7th International Conference on Signal Processing and Information Technology (ICSPIT).

Martin-Pomares, L., Ghaoud, T., Al Khaja, T. T., Alemadi, A., Ahmed, M. S. M. M., et al. (2024). Sea water salinity estimation over Dubai using satellite imagery & WASI empirical model approach: Application to SpaceD DEWASAT-2 nanosatellite. Proceedings of the 7th International Conference on Signal Processing and Information Technology (ICSPIT).