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Rachel Cook (Assoc)
National Research Council Postdoctoral Fellow
Dr. Rachel E. Cook joined the Infrastructure Materials Group led by Dr. Aron Newman in the Fall of 2022 as an NRC postdoctoral fellow. Prior to joining NIST, Rachel completed her Ph.D. at Missouri University of Science and Technology (Missouri S&T) in Ceramic Engineering in 2020. Her dissertation investigated compositional- and physiochemical- property relationships in the context of early age hydration of Portland and pure-phase (i.e., tricalcium silicate, belite, etc.) cements.
Rachel first arrived at NIST in 2021 through the NIST Professional Research Experience Program (PREP). As an NRC postdoctoral researcher, she aims to explore the viability of plastic waste as a construction material additive.
Research interests
- Sustainable Construction Materials
- Alternative Binders
- Supplementary Cementitious Materials (SCMs)
- Waste Materials
- Local Materials
Publications
Han, T., Ponduru, S.A., Cook, R., Huang, J., Sant, G., Kumar, A., 2022. A Deep Learning Approach to Design and Discover Sustainable Cementitious Binders: Strategies to Learn From Small Databases and Develop Closed-form Analytical Models. Front. Mater. 8, 574. https://doi.org/10.3389/fmats.2021.796476
Cook, R., Han, T., Childers, A., Ryckman, C., Khayat, K., Ma, H., Huang, J., Kumar, A., 2021. Machine learning for high-fidelity prediction of cement hydration kinetics in blended systems. Mater. Des. 208, 109920. https://doi.org/10.1016/j.matdes.2021.109920
Cook, R., Ma, H., Okoronkwo, M., Sant, G., Kumar, A., 2021. Influence of water activity on belite (β-C2S) hydration. J. Am. Ceram. Soc. 104, 1831–1840. https://doi.org/10.1111/jace.17608
Kittikunakorn, N., Paul, S., Koleng, J.J., Liu, T., Cook, R., Yang, F., Bi, V., Durig, T., Sun, C.C., Kumar, A., Zhang, F., 2021. How Does the Dissimilarity of Screw Geometry Impact Twin-screw Melt Granulation? Eur. J. Pharm. Sci. 157, 105645. https://doi.org/10.1016/j.ejps.2020.105645
Cook, R., Monyake, K.C., Hayat, M.B., Kumar, A., Alagha, L., 2020. Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model. Eng. Rep. 2, e12167. https://doi.org/10.1002/eng2.12167
Cook, R., Ma, H., Kumar, A., 2019. Influence of size‐classified and slightly soluble mineral additives on hydration of tricalcium silicate. J. Am. Ceram. Soc. 103, 2764–2779. https://doi.org/10.1111/jace.16936
Cook, R., Lapeyre, J., Ma, H., Kumar, A., 2019. Prediction of Compressive Strength of Concrete: Critical Comparison of Performance of a Hybrid Machine Learning Model with Standalone Models. J. Mater. Civ. Eng. 31, 04019255. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002902
Cook, R., Ma, H., Kumar, A., 2019. Mechanism of tricalcium silicate hydration in the presence of polycarboxylate polymers. SN Appl. Sci. 1, 145. https://doi.org/10.1007/s42452-018-0153-1
Ley-Hernandez, A.M., Lapeyre, J., Cook, R., Kumar, A., Feys, D., 2018. Elucidating the Effect of Water Content on Hydration Mechanisms of Cement. ACS Omega 3, 5092–5105. https://doi.org/10.1021/acsomega.8b00097