Iron metal-based aqueous batteries hold great potential for extensive grid-scale energy solutions due to iron’s high theoretical specific capacity, ultralow cost, and abundance. However, rechargeable aqueous iron batteries are inadequately employed since iron redox suffers from parasitic side reactions, resulting in poor efficiency and requiring a better understanding of foundational aspects in their materials sciences. Our investigation using multimodal synchrotron techniques such as operando X-ray scattering, transmission X-ray microscopy, ambient pressure X-ray photoelectron spectroscopy, X-ray absorption spectroscopy, and X-ray fluorescence micro-imaging is beneficial for investigating the redox chemistry of iron electrodes and the structural chemistry of aqueous electrolytes. Such intricate information about the operations of earth-abundant iron for batteries using eco-friendly aqueous electrolytes promises their immense impact on the industrialization of long-duration grid storage technologies. 

My previous research work (during PhD) was on the Fe2+ --> Fe3+ conversion as a sustainable redox. However, the Fe(OH)2 layered double hydroxide (LDH) undergoes two parallel oxidation processes to electrochemically passive spinel (Fe3O4) and reversible FeOOH phase. However, my challenge is overcoming spinel phase formation by various methods, including but not limited to anion-intercalation into the Fe-O interlayers. 

During the discharge (oxidation process), the Fe(OH)2 accommodates anions from the electrolyte in between their interlayers to form an intermediate phase called green rust with a chemical formula [Fe2+1-xFe3+x(HO-)2]X+ (An-x/n)X-, where An- are the anions such as SO42-, Cl-, etc., My former colleague Dr. Fenghua Guo and I worked on the SO4-intercalation, which proved to concede the spinel phase and thus promote FeOOH formation. We published the electrochemical and molecular dynamics results elucidated by the operando synchrotron X-ray diffraction (XRD) in the Journal of American Chemical Society. Even though the divalent nature of SO4 has excellent electrostatic attraction to the positively charged Fe-LDH, their sizeable tetrahedral geometry did not facilitate spontaneous adsorption onto the Fe-O framework as much as the spherical monovalent Cl- ions. We found that the stable adsorption by Cl- ions in between the Fe-LDH resulted in a higher Fe(OH)2/FeOOH conversion (64.7%) and a better cycle life (400 cycles) than the SO4-intercalated conversion (19%). The excellent performance of the chloride insertion report was reported in the Chemistry of Materials and mentioned in the 'Highlights from 2023 and Chemistry of Materials’ 35th Year'.

Another exciting way to promote the Fe(OH)2 --> FeOOH conversion is to modify the water activity in the electrolyte by using additives. During the charging (reduction process), the Fe(OH)2 partially converts to Fe, triggering the HER. Polysilicates (Na2SiO3), in their extremely low concentration (parts per million) in the alkaline electrolyte, have two-fold effectiveness on Fe-redox: (i) on charging (reduction), it strengthens the hydrogen bond networks of the water to passivate the Fe(OH)2 --> Fe conversion and block HER on Fe surface; (ii) the suppressed Fe(OH)2 --> Fe conversion allows a lower reduction potential on charging to improve Fe(OH)2 formation successively benefiting Fe(OH)2 --> FeOOH conversion on discharging. The discharge capacity was improved six times with Na2SiO3 additives compared to the electrolyte without them. The detailed study has been published in the ChemSusChem journal. My Ph.D. advisor and I have also applied for two US patents reporting this mechanism (US Patent App. 63/445,386 and 63/632,588).

Understanding Sodium-Sulfur Conversion Chemistry through Operando Synchrotron Probes 

My postdoctoral research has focused on unraveling the fundamental conversion chemistry of room-temperature sodium–sulfur (Na–S) batteries, which are promising candidates for low-cost, earth-abundant, and high-energy-density grid storage. Despite their attractive theoretical energy density, Na–S batteries suffer from rapid performance decay arising from sluggish sulfur redox kinetics, polysulfide dissolution, and severe chemo-mechanical degradation of the cathode. To directly address these challenges, I employed operando synchrotron techniques—including transmission X-ray microscopy (TXM) and tender X-ray absorption spectroscopy (XAS)—to visualize and chemically track sulfur evolution during battery operation. 

These measurements enabled real-time observation of sulfur dissolution, irreversible volume expansion, particle fracture, and active-material loss within carbon hosts, phenomena that are otherwise inaccessible using ex situ characterization. By correlating nanoscale morphological changes with sulfur speciation and electrochemical signatures, this work provided a mechanistic picture of how polysulfide chemistry and structural instability jointly govern capacity fade in Na–S systems. The insights from this study establish a framework for rationally designing sulfur hosts and electrolyte chemistries that mitigate polysulfide shuttling and improve long-term cycling stability, thereby advancing the practical deployment of sodium–sulfur batteries for stationary energy storage. 

Machine Learning-Guided Design of Battery Electrolytes across Chemistries 

My postdoctoral research has also expanded into the application of machine learning (ML) as a principled tool for accelerating electrolyte discovery and optimization across organic, aqueous, and solid-state battery systems. Electrolytes critically dictate battery safety, efficiency, and lifetime, yet their vast chemical design space renders traditional trial-and-error approaches inefficient and resource-intensive. In a comprehensive review, I analyzed and organized state-of-the-art ML strategies—ranging from supervised regression and classification models to generative and active-learning frameworks—and evaluated their effectiveness in addressing electrolyte-specific challenges such as ionic conductivity, electrochemical stability windows, and interfacial degradation. 

Rather than treating ML as a black-box predictor, this work emphasized the alignment of learning paradigms with electrolyte physics, data quality, and validation strategies, highlighting where ML has delivered tangible predictive value and where it remains exploratory. By linking molecular- and materials-level descriptors to experimentally relevant performance metrics, this research outlines robust ML-assisted workflows that integrate computation, data science, and experimental validation. Such data-driven approaches offer a scalable pathway to rational electrolyte design, reducing development time while enabling the discovery of safer and more sustainable battery chemistries. 

Formulating Cathode-Electrolyte Interphases in Sodium-Ion Cathodes  

My current work focuses on the formation, evolution, and chemical heterogeneity of the cathode–electrolyte interphase (CEI) in layered sodium-ion battery cathodes, particularly under high-voltage operation. Using a suite of synchrotron-based techniques - including transmission X-ray microscopy, X-ray absorption spectroscopy, and operando and ex situ X-ray diffraction.

I investigate how local transition-metal redox, electrolyte decomposition, and surface reconstruction collectively govern CEI chemistry and long-term degradation. By correlating spatially resolved chemical-state information with structural evolution, this research aims to establish fundamental structure–chemistry–performance relationships that inform the design of stable sodium-ion cathodes for next-generation grid-scale energy storage. 

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