Vitaliy Robert Yurkiv

Interests

Research

Multi-physics modeling and machine learning calculation of energy storage and conversion technologies.Ab-initio density functional theory (DFT) calculations. Thermal measurements of cylindrical and pouch batteries.

Teaching

ThermodynamicsEnergy storage and conversionRechargeable batteriesElectric vehicles (EVs)

Courses

Scholarly Contributions

Journals/Publications

• Borshon, I. Z., Ragone, M., Phakatkar, A. H., Long, L., Shahbazian-Yassar, R., Mashayek, F., & Yurkiv, V. (2024). Predicting column heights and elemental composition in experimental transmission electron microscopy images of high-entropy oxides using deep learning. npj Computational Materials, 10(1), 275.
• Das, G., Abdisobbouhi, Y., Du, H., Mashayek, F., Kingston, T. A., & Yurkiv, V. (2024). Advancing battery safety: Integrating multiphysics and machine learning for thermal runaway prediction in lithium-ion battery module. Journal of Power Sources, 614, 235015.
• Das, G., Mastrogiorgio, M., Ragone, M., Jabbari, V., Shahbazian-Yassar, R., Mashayek, F., & Yurkiv, V. (2024). A combined multiphysics modeling and deep learning framework to predict thermal runaway in cylindrical Li-ion batteries. Journal of Power Sources, 595, 234065.
• Ghorbani, A., Yurkiv, V., Gon??alves, J. M., Amiri, A., Ritter, T. G., Tamadoni, S. M., & Shahbazian-Yassar, R. (2024). Role of CO2 and Glycerol in the Formation of Urchin-Like Strontium Carbonate Particles. ACS Sustainable Chemistry & Engineering, 12(8), 3185-3195.
• Granda, R., Yurkiv, V., Mashayek, F., & Yarin, A. L. (2024). Impact of drops of epoxy resin and hardener, silicone and turpentine oils onto balsa wood and polypropylene substrates. Physics of Fluids, 36(5), 053312.
• Ritter, T. G., Il, K. Y., Bezerra, D., Wang, X., Pan, Y., Yurkiv, V., Yarin, A. L., & Shahbazian-Yassar, R. (2024). Composite PEDOT:PSS-PEO Layers for Improving Lithium Batteries. ChemElectroChem, 11(20), e202400458.
• Yurkiv, V., Wang, X., Kim, Y., Pan, Y., Mashayek, F., & Yarin, A. L. (2024). Ab initio modeling and experimental analysis of electronic conductivity in PEDOT:PSS-PEO films for extrusion-based manufacturing. Journal of Colloid and Interface Science, 674, 128-138.
• Amiri, A., Ghildiyal, P., Phakatkar, A. H., Shahbazian-Yassar, R., Shokuhfar, T., Sorokina, L. V., Wang, Y., Yurkiv, V., & Zachariah, M. R. (2023).

  In Situ Microscopic Studies on the Interaction of Multi-Principal Element Nanoparticles and Bacteria

  . ACS Nano, 17(6), 5880-5893. doi:10.1021/acsnano.2c12799
  More info

  Multi-principal element nanoparticles are an emerging class of materials with potential applications in medicine and biology. However, it is not known how such nanoparticles interact with bacteria at nanoscale. In the present work, we evaluated the interaction of multi-principal elemental alloy (FeNiCu) nanoparticles with Escherichia coli (E. coli) bacteria using the in situ graphene liquid cell (GLC) scanning transmission electron microscopy (STEM) approach. The imaging revealed the details of bacteria wall damage in the vicinity of nanoparticles. The chemical mappings of S, P, O, N, C, and Cl elements confirmed the cytoplasmic leakage of the bacteria. Our results show that there is selective release of metal ions from the nanoparticles. The release of copper ions was much higher than that for nickel while the iron release was the lowest. In addition, the binding affinity of bacterial cell membrane protein functional groups with Cu, Ni, and Fe cations is found to be the driving force behind the selective metal cations’ release from the multi-principal element nanoparticles. The protein functional groups driven dissolution of multielement nanoparticles was evaluated using the density functional theory (DFT) computational method, which confirmed that the energy required to remove Cu atoms from the nanoparticle surface was the least in comparison with those for Ni and Fe atoms. The DFT results support the experimental data, indicating that the energy to dissolve metal atoms exposed to oxidation and/or the to presence of oxygen atoms at the surface of the nanoparticle catalyzes metal removal from the multielement nanoparticle. The study shows the potential of compositional design of multi-principal element nanoparticles for the controlled release of metal ions to develop antibacterial strategies. In addition, GLC-STEM is a promising approach for understanding the nanoscale interaction of metallic nanoparticles with biological structures.
• Das Goswami, B. R., Jabbari, V., Shahbazian-Yassar, R., Mashayek, F., & Yurkiv, V. (2023).

  Unraveling Ion Diffusion Pathways and Energetics in Polycrystalline SEI of Lithium-Based Batteries: Combined Cryo-HRTEM and DFT Study

  . The Journal of Physical Chemistry C, 127(45), 21971-21979. doi:10.1021/acs.jpcc.3c05395
  More info

  The solid-electrolyte interphase (SEI) in lithium-based batteries has been extensively studied regarding its composition, structure, and formation mechanisms. However, an understanding of the ion transport through the SEI remains incomplete. Revealing the underlying ion diffusion processes across the SEI holds great potential for enhancing battery performance and improving safety. In this study, we present the outcomes of first-principles density functional theory (DFT) calculations based on cryogenic high-resolution transmission electron microscopy (cryo-HRTEM) imaging, which elucidate the dominant diffusion pathways, energetics, and diffusion coefficients associated with lithium (Li) diffusion through the polycrystalline SEI. Specifically, we focus on Li diffusion through the grain boundaries (GBs) formed by the three primary inorganic components of the SEI, namely, Li2O, LiF, and Li2CO3. Our findings reveal that Li diffusion primarily occurs through numerous open channels created by the GBs. The energetics and potential barriers reveal significant variations depending upon the structural characteristics of these channels, with a distinguished trend being faster Li diffusion within the GB compared to neighboring crystalline regions within the grain interiors. The analysis of the charge density in GBs revealed that Li dendrite formation occurs in GBs with less Li diffusion kinetics.
• Jabbari, V., Yurkiv, V., Ghorbani, A., Mashayek, F., & Shahbazian-Yassar, R. (2023).

  Fast rate lithium metal batteries with long lifespan enabled by graphene oxide confinement

  . Energy Advances, 2(5), 712-724. doi:10.1039/d3ya00083d
  More info

  Dendritic growth of lithium (Li) is hindering potential applications of Li-metal batteries, and new approaches are needed to address this challenge. The confinement effect of two-dimensional materials triggered by strong molecular interactions between parallelly-aligned graphene oxide (GO) at Li metal interface is proposed here as a new strategy to suppress the dendritic growth of Li. The effectiveness of aligned GO for Li-metal cells is shown for two different polymer separator cells:liquid electrolytes with porous propylene (PP) separators and solid polyethylene oxide (PEO) electrolytes. For the case of liquid electrolytes, PP separators were modified with plasma treatment to induce the alignment of GO layers. The Li‖Li cells with aligned GO illustrate a stable Li platting/stripping (up to 1000 cycles). The Li‖lithium iron phosphate (LFP) battery cells with aligned GO could cycle at 5C for 1000 cycles (∼90% capacity retention). For solid polymer electrolyte (SPE) cells, GO–Li confinement effect is also effective in Li dendrites suppression enhancing the stability and lifespan of Li-metal batteries. The Li‖LFP cell with the GO-modified SPE showed ∼85% capacity retention after 200 cycles at 1C. Such combined high rate capability and number of cycles exceeds the previously reported performances for both liquid and SPE-based Li‖LFP cells. This points to a new opportunity for utilizing the confinement effect of two-dimensional materials for the development of next generation, fast rate rechargeable Li batteries.
• Ragone, M., Shahabazian-Yassar, R., Mashayek, F., & Yurkiv, V. (2023).

  Deep learning modeling in microscopy imaging: A review of materials science applications

  . Progress in Materials Science, 138, 101165. doi:10.1016/j.pmatsci.2023.101165
  More info

  The accurate analysis of microscopy images representing various materials obtained in scanning probe microscopy, scanning tunneling microscopy, and transmission electron microscopy, is in general time consuming as it requires the inspection of multiple data bases for the correct interpretation of the observed crystal structures. This task is especially demanding in microscopy video analysis involving a vast amount of image data. The recent development of deep learning (DL) algorithms has paved the way for cutting-edge microscopy studies in materials science, often outperforming conventional image analysis methods. This paper reviews the state-of-the-art in DL-based synthetic data generation, materials structure identification, three-dimensional structural reconstruction, and physical properties evaluation for different types of microscopy images. First, the fundamental concepts of DL relevant to materials science applications are reviewed. Subsequently, the combined experimental measurements and numerical simulations for preparing dedicated microscopy image for DL analysis are discussed. Then, the review concentrates on the core topic of the paper, that is the critical assessment of DL advances in materials’ structural and physical properties evaluation. We believe that the future development and deployment of DL for practical microscopy data analysis will rely on the progress and improvement of advanced algorithms and innovative methods for training data generation.
• Saray, M. T., Yurkiv, V., & Shahbazian‐Yassar, R. (2023).

  Role of Kinetics and Thermodynamics in Controlling the Crystal Structure of Nickel Nanoparticles Formed on Reduced Graphene Oxide: Implications for Energy Storage and Conversion Applications

  . ACS Applied Nano Materials, 6(12), 10033-10043. doi:10.1021/acsanm.2c05528
  More info

  Ultrafast heating has emerged recently to speed up the synthesis processes of nanoparticles and control their morphology. However, it is not clear how the heating rate affects the formation of metal nanoparticles, particularly those formed on substrates. Here, we explored the formation of nickel (Ni) nanoparticles on graphene oxide (GO) substrates under slow (20 °C/min) and ultrafast (103 °C/s) heating rates. The experiments were performed in situ on heating microchip devices using an aberration-corrected transmission electron microscope. Interestingly, the GO structure was the most effective in controlling the stability of nanoparticles when ultrafast heating was employed, leading to a hexagonally close-packed Ni phase (hcp-Ni) because of less lattice mismatch with the graphitic substrate. On the contrary, fcc-Ni nanoparticles formed under a slow heating process where no strong correlation with the GO crystal structure was observed. Additionally, ultrafast heating resulted in smaller-size nanoparticles which could be ascribed to rapid reduction, nucleation rate, and higher diffusion barrier of hcp-Ni crystals on rGO. Nevertheless, the stability of the crystal structure of the nickel nanoparticles remains unaffected by their size. These results indicate the crucial role of the substrate on crystal structure during the nonequilibrium processing of materials and the competing effects of thermodynamics versus kinetics in creating novel phases of materials for energy storage and conversion applications.
• Tamadoni Saray, M., Yurkiv, V., & Shahbazian‐Yassar, R. (2023).

  In Situ Thermolysis of a Ni Salt on Amorphous Carbon and Graphene Oxide Substrates

  . Advanced Functional Materials, 33(28). doi:10.1002/adfm.202213747
  More info

  Abstract Understanding the thermal decomposition of metal salt precursors on carbon structures is essential for the controlled synthesis of metal‐decorated carbon nanomaterials. Here, the thermolysis of a Ni precursor salt, NiCl 2 ·6H 2 O, on amorphous carbon (a‐C) and graphene oxide (GO) substrates is explored using in situ transmission electron microscopy. Thermal decomposition of NiCl 2 ·6H 2 O on GO occurs at higher temperatures and slower kinetics than on a‐C substrate. This is correlated to a higher activation barrier for Cl 2 removal calculated by the density functional theory, strong Ni‐GO interaction, high‐density oxygen functional groups, defects, and weak van der Waals using GO substrate. The thermolysis of NiCl 2 ·6H 2 O proceeds via multistep decomposition stages into the formation of Ni nanoparticles with significant differences in their size and distribution depending on the substrate. Using GO substrates leads to nanoparticles with 500% smaller average sizes and higher thermal stability than a‐C substrate. Ni nanoparticles showcase the fcc crystal structure, and no size effect on the stability of the crystal structure is observed. These findings demonstrate the significant role of carbon substrate on nanoparticle formation and growth during the thermolysis of carbon–metal heterostructures. This opens new venues to engineer stable, supported catalysts and new carbon‐based sensors and filtering devices.
• Yurkiv, V., Rasul, G., Phakatkar, A. H., Mashayek, F., & Jabbari, V. (2023).

  In situ formation of stable solid electrolyte interphase with high ionic conductivity for long lifespan all-solid-state lithium metal batteries

  . Energy Storage Materials, 57, 1-13. doi:10.1016/j.ensm.2023.02.009
  More info

  Parasitic reactions inevitably occur at the interface of lithium (Li) metal and polymer electrolytes due to ultrahigh Li reducibility coupled with poor interfacial stability or ionic conductivity. This leads to significant capacity loss and inferior lifespan of Li metal batteries (LMBs). Herein, we engineered a stable solid electrolyte interphase (SEI) layer at the interface of Li metal and polyethylene oxide (PEO) electrolyte via incorporation of phosphazene molecules. The phosphazene-solid polymer electrolyte (P-SPE) shows a significantly higher long-term stability against Li metal anode when compared with non-modified SPE. Using cryogenic transmission electron microscopy (cryo-TEM) and X-ray photon spectroscopy (XPS), Li3N, LiF, Li3P and Li3PO4 nanocrystals were identified in the SEI layer. The Li|Li cell with P-SPE cycle for 1800 cycles at 0.2 mA cm‒2. The Li||LFP cells with P-SPE deliver a specific capacity of ∼150 mAh g−1 and ∼120 mAh g−1 at 1C and 2C charge/discharge rates, respectively, with up to 80% capacity retention after 500 and 1000 cycles, respectively. Critical role of phosphazene-modified SEI in improving electrochemical performance is further investigated by density function theory (DFT) and ab-initio molecular dynamic (AIMD) calculations. This study offers a promising approach for engineering a stable and ion-conductive Li|polymer electrolyte interface for long lifespan LMBs.
• Halder, S., Granda, R., Wu, J., Sankaran, A. N., Yurkiv, V., Yarin, A. L., & Mashayek, F. (2022).

  Air bubble entrapment during drop impact on solid and liquid surfaces

  . International Journal of Multiphase Flow, 149, 103974. doi:10.1016/j.ijmultiphaseflow.2022.103974
  More info

  The phase-field modeling (PFM) of water drop impact onto a dielectric hydrophobic parafilm surface is performed to explore air entrapment and its influence on deposition and rebound phenomena. Local and global characteristics of the drop impact are taken into account by using the combined Cahn-Hilliard and Navier-Stokes equations. The modeling results of water drop impact are directly compared with our experimental measurements in terms of maximum spreading distance, and air bubble size. The simulation results reveal that air can be trapped under the liquid drop during the initial impact as well as during the retraction phase at the center of the drop due to the closure of the liquid layer above a cavity. It is found that the drop diameter and the impact velocity play significant roles in the air entrapment phenomena. The probability of air bubble formation is higher at lower impact velocity and for larger drop size. The model is also capable of simulating the case of drop impact onto a water surface, and the results are validated using prior literature data. In addition, the influence of the phase-field variables and the mesh adaptation scheme on the PFM is studied and discussed. Thus, our findings provide new qualitative and quantitative insights into the influence of air entrapment on drop deposition onto hydrophobic and liquid surfaces.
• Mashayek, F., Shahbazian-Yassar, R., Tamadoni, M., Yurkiv, V., Long, L., & Ragone, M. (2022). "Deep Learning for Mapping Element Distribution of High-Entropy Alloys in Scanning Transmission Electron Microscopy Images". Computational Materials Science, 201, 110905. doi:https://doi.org/10.1016/j.commatsci.2021.110905
• Mashayek, F., Yarin, A. L., Yurkiv, V., Sankaran, A., Wu, J., Granda, R., & Halder, S. (2022). “Air Bubble Entrapment during Drop Impact on Solid and Liquid Surfaces”. International Journal of Multiphase Flow, 149, 103974. doi:https://doi.org/10.1016/j.ijmultiphaseflow.2022.103974
• Mashayek, F., Yurkiv, V., Granda, R., & Yarin, A. L. (2022). Paint drop spreading on wood and its enhancement by an in-plane electric field. Physics of Fluids, 34(12), 122112. doi:10.1063/5.0130871
• Mashayek, F., Yurkiv, V., Ramasubramanian, A., Ragone, M., & Kashir, B. (2021). "Application of Fully Convolutional Neural Networks for Feature Extraction in Fluid Flow". Journal of Visualization, 24, 771-785. doi:https://doi.org/10.1007/s12650-020-00732-0
• Shahbazian-Yassar, R., Mashayek, F., Griffin, P., Cheng, M., Rasul, M. G., Yurkiv, V., & Jabbari, V. (2021). “A Smart Lithium Battery with Shape Memory Function”. Small. doi:https://doi.org/10.1002/smll.202102666
• Cheng, M., Ramasubramanian, A., Rasul, M. G., Jiang, Y., Yuan, Y., Foroozan, T., Deivanayagam, R., Tamadoni Saray, M., Rojaee, R., Song, B., Yurkiv, V., Pan, Y., Mashayek, F., & Shahbazian-Yassar, R. (2020). “Direct Ink Writing of Polymer Composite Electrolytes with Enhanced Thermal Conductivities”. Advanced Functional Materials, 31(4), 2006683. doi:http://doi.org/10.1002/adfm.202006683
• Lu, J., Shahbazian-Yassar, R., Mashayek, F., Song, B., Liu, T., Yurkiv, V., Yao, W., & Yuan, Y. (2020). “Beyond volume variation: anisotropic and protrusive lithiation in bismuth” . ACS Nano, 14(11), 15669-15677. doi:http://doi.org/10.1021/acsnano.0c06597
• Mashayek, F., Shahbazian-Yassar, R., Paoli, R., Sharifi-Asl, S., Ragone, M., Ramasubramanian, A., Foroozan, T., & Yurkiv, V. (2020). "The Mechanism of ZN Diffusion Through ZnO in Secondary Battery: A Combined Theoretical and Experimental Study”. Journal of Physical Chemstry, 124(29), 15730-15738. doi:http://doi.org/10.1021/acs.jpcc.0c03514
• Mashayek, F., Shahbazian-Yassar, R., Ragone, M., Foroozan, T., Yurkiv, V., & Ramasubramanian, A. (2020). "Stability of Solid-Electrolyte Interphase on Lithium Metal Surface in Lithium Metal Batteries". ACS Applied Energy Materials, 3(11), 10560-10567. doi:http://doi.org/10.1021/acsaem.0c01605
• Mashayek, F., Shahbazian-Yassar, R., Ramsubramanian, A., Song, B., Yurkiv, V., & Ragone, M. (2020). “Atomic Column Heights Detection in Metallic Nanoparticles Using Deep Learning”. Computational Material Science, 180, 109722. doi:http://doi.org/10.1016/j.commatsci.2020.109722
• Shahbazian-Yassar, R., Ganesan, V., Mashayek, F., Pan, Y., Son, S., Cheng, M., Phakatkar, A. H., Sharifi-Asl, S., Rasul, M. G., Foroozan, T., Deivanayagam, R., Yurkiv, V., Wheatle, B. K., Mogurampelly, S., Cavallo, S., & Rojaee, R. (2020). “Highly-Cyclable Room-Temperature Black Phosphorene Polymer Electrolyte Composites for Li Metal Batteries”. Advanced Functional Materials, 1910749. doi:http://doi.org/10.1002/adfm.201910749
• Mashayek, F., Shahbazian-Yassar, R., Khounsary, A., Najafi, A., Nie, A., Yurkiv, V., & Ramasubramanian, A. (2019). “A Numerical Study on Striped Lithiation of Tin Oxide Anodes”. International Journal of Solids and Structures, 159, 163-170. doi:https://doi.org/10.1016/j.ijsolstr.2018.09.027
• Mashayek, F., Shahbazian-Yassar, R., Ragone, M., Foroozan, T., Yurkiv, V., & Ramasubramanian, A. (2019). “Lithium Diffusion Mechanism through Solid-Electrolyte Interphase (SEI) in Rechargeable Lithium Batteries”
  . Journal of Physical Chemstry, 123(16), 10237-10245. doi:https://doi.org/10.1021/acs.jpcc.9b00436
• Shahbazian-Yassar, R., Lu, J., Amine, K., Mashayek, F., Friedrich, C. R., Long, F., Bi, X., Yurkiv, V., Cheng, M., Liu, C., Tan, G., Yuan, Y., & Yao, W. (2019). “Tuning Li₂O₂ Formation Routes by Facet-engineering of MnO2 Cathode Catalysts”. Journal of the American Chemical Society, 141(32), 12832-12838. doi:https://doi.org/10.1021/jacs.9b05992
• Shahbazian-Yassar, R., Mashayek, F., Rojaee, R., Sharifi-Asl, S., Yurkiv, V., & Foroozan, T. (2019). “Non-Dendritic Zn Electrodeposition Enabled by Zincophilic Graphene Substrates”. ACS Applied Materials & Interfaces, 11(47), 44077-44089. doi:https://doi.org/10.1021/acsami.9b13174
• Subramanian, A., Vasudevamurthy, G., Harris, C. T., Yoo, J., Maksud, M., Ramasubramanian, A., Shikder, M. R., Yurkiv, V., & Mashayek, F. (2019). “Plastic Recovery and Self-healing in Longitudinally Twinned SiGe Nanowires”. Nanoscale, 11(18). doi:https://doi.org/10.1039/C9NR02073J
• Unocic, R. R., Shahbazian-Yassar, R., Veith, G. M., Mashayek, F., Yurkiv, V., Shin, D., Sang, X., Baggetto, L., & Gutierrez-Kolar, J. S. (2019). “Interpreting Electrochemical and Chemical Sodiation Mechanisms and Kinetics in Tin Antimony Battery Anodes using in situ TEM and Computational Methods”. ACS Applied Engineering, 2(5), 3578-3586. doi:https://doi.org/10.1021/acsaem.9b00310
• Mashayek, F., Shahbazian-Yassar, R., Ramasubramanian, A., Foroozan, T., & Yurkiv, V. (2018). “The Influence of Stress Field on Li Electrodeposition in Li-metal Battery” . MRS Communications, 8, 1285-1291. doi:https://doi.org/10.1557/mrc.2018.146
• Mashayek, F., Yarin, A. L., & Yurkiv, V. (2018). “Modeling of Droplet Impact onto Polarized and Non-polarized Dielectric Surfaces”. Langmuir, 34(34), 10169-10180. doi:https://doi.org/10.1021/acs.langmuir.8b01443

Presentations

• Yurkiv, V. R., Phakatkar, A., Shahbazian-Yassar, R., Ragone, M., & Mashayek, F. (2022, December).

  Combined machine learning and density functional theory approach to predict element distribution of high-entropy alloys in scanning transmission electron microscopy images

  . 2022 MRS Meeting. Boston: NSF.