About

Professor Anatoly I. Frenkel is a faculty member in the Department of Materials Science and Chemical Engineering. His research focuses on the physico-chemical properties and mechanistic studies of functional nanomaterials. He investigates heterogeneous and photo-catalysis involving nanoparticles, clusters, and single atoms. Professor Frenkel employs multimodal methodologies for materials characterization and integrates machine learning methods for materials research and discovery. Additionally, he is involved in the development of synchrotron-based techniques and data analysis methods. For recent research highlights, his work is featured on the web page of the Structure and Dynamics of Applied Nanomaterials group at Brookhaven National Laboratory.

Research topics

• Chemistry
• Computational chemistry
• Chemical engineering
• Materials science
• Organic chemistry
• Metallurgy
• Combinatorial chemistry
• Nanotechnology
• Physical chemistry
• Chemical physics
• Inorganic chemistry
• Crystallography

Selected publications

• Mitigating tin ion migration and reinforcing buried interface via synergistic polymer-modified tin oxide

  Research Square · 2026-03-17

  preprintOpen access
  PublisherOA PDFDOI

• Electrostatic solvation reshapes electrocatalyst nanoparticles under reaction conditions

  ChemRxiv · 2026-03-05

  articleOpen access

  Electrocatalysis underpins sustainable energy conversion, yet electrostatic solvation at the electrolyte-catalyst interface has not been resolved as a tunable control parameter. Here we show that minute concentrations of weakly coordinating ions reversibly reshape Pt nanoparticles under operando conditions. Sub-second operando X-ray absorption and surface-enhanced infrared spectroscopy during the hydrogen evolution and ammonia oxidation reactions reveal that electric-field shielding stabilizes distinct morphologies. Weakly coordinating anions and more negative potentials favor compact, spherical particles, whereas weakly coordinating cations and less negative potentials promote flattening. Density functional theory and coordination-based energetic analyses support the thermodynamic plausibility of these electrostatically biased shapes. The kinetic consequences are reaction dependent, reflecting distinct structure sensitivities, as hydrogen evolution tracks interfacial water structure, whereas ammonia oxidation tracks particle geometry. Electrolyte-controlled electrostatic solvation thus provides a reversible, reaction-dependent lever to tune catalytic performance without altering catalyst composition.

  PublisherOA PDFDOI

• Evaluating Ru-RuO ₂ @BN as a Bifunctional Electrocatalyst for the Nitrogen Reduction Reaction and the Hydrogen Evolution Reaction

  ACS Applied Energy Materials · 2026-02-19 · 1 citations

  article

  Electrochemical approaches toward clean energy production have been the focus of significant attention. The nitrogen (N2) reduction reaction (NRR) and the hydrogen (H2) evolution reaction (HER) offer a promising method for producing NH3 and H2, respectively. Nevertheless, practical obstacles that must be overcome in creating optimal catalysts are the sluggish kinetics and low selectivity of NRR and HER. Herein, we report on the synthesis of a Ru–RuO2-decorated boron nitride (BN) catalyst that shows excellent activity toward NRR. A rate of NH3 formation (VNH3) of 16.8 μg h–1 mg–1 and a corresponding Faradaic efficiency (FE) of 52.9% were noted at a potential of −0.5 V in 0.1 M HCl. However, the HER activity of Ru–RuO2@BN was found to be highly suppressed in 0.1 M HCl and did not yield a reasonable overpotential value. Thus, the capability of this material toward NRR suggests its viability as a promising catalyst for clean NH3 production.

  PublisherDOI

• Morphological descriptors of nanoparticles: The link between atomistic structures and x-ray absorption spectra

  The Journal of Chemical Physics · 2026-01-22

  articleSenior author

  Understanding and quantifying the morphology of nanoparticles are essential for linking their atomic structure to diverse applications and verifying theoretical models. While experimental information on the structure of nanoparticles in the size range below ∼5 nm can be extracted from x-ray absorption spectroscopy using a small number of descriptors-most commonly coordination numbers-developing an understanding of morphology descriptors from experimental data remains a challenge. Here, we introduce NanoGene, a genetic algorithm-based method for generating structurally diverse nanoparticle models guided by user-defined descriptors. We establish correlations among structural, size-related, and morphological descriptors and demonstrate how experimentally accessible parameters, such as coordination numbers, can be leveraged to infer otherwise inaccessible ones, such as the generalized coordination number or particle oblateness. Principal component and clustering analyses reveal the relative importance of descriptors, with the number of atoms emerging as a key discriminant of the nanoparticle structure. By providing both the methodology and an extensive dataset of nanoparticle geometries, this work offers a practical foundation for descriptor-based analysis and interpretation of experimental observations, bridging the gap between local atomic coordinates and global morphological characterization.

  PublisherDOI

• Transparent Reporting for Agentic Catalysis Enabled by Artificial Intelligence (TRACE-AI): Community Guidelines and A Publication Checklist

  Digital Repository at the University of Maryland (University of Maryland College Park) · 2026-03-25

  articleOpen access

  Artificial intelligence (AI) is increasingly integrated into catalysis science, enabling agentic workflows in which AI systems perceive inputs, reason under constraints, plan, and autonomously execute in silico or physical experiments with minimal human intervention. While these closed-loop capabilities hold promise to accelerate knowledge generation and technological innovation, they inevitably introduce new sources of variability in data lineage, model specification, and agent policies that can undermine FAIRness, rigor, and reproducibility. These risks are particularly pronounced in heterogeneous catalysis, where subtleties in catalyst synthesis and pretreatment, dynamic restructuring under operating conditions, and transport-mediated local environments can largely determine catalytic outcomes. To address these challenges, we introduce TRACE-AI (Transparent Reporting for Agentic Catalysis Enabled by Artificial Intelligence) as a set of community guidelines paired with a publication checklist. TRACE-AI emphasizes end-to-end traceability across the full lifecycle of an agentic catalysis campaign, linking research objectives to data and models, agent reasoning and action, and the knowledge acquired. By promoting standardized and accountable reporting, TRACE-AI aims to cultivate a shared foundation for accelerating scientific discovery while reinforcing safety and trust as autonomous catalysis laboratories continue to emerge.

  PublisherDOI

• Seed-Mediated Colloidal Synthesis of Multimetallic and High-Entropy Alloy Nanocrystal Libraries with Enhanced Catalytic Performance

  Journal of the American Chemical Society · 2026-04-09

  article

  Engineering colloidally stable multimetallic nanocrystals offers many benefits in a wide range of applications and allows manipulation of physical, chemical, and electronic properties of materials at the nanoscale. Synthesis routes are challenged by the chemical complexity required to temporally and spatially coordinate the reduction and alloying of multiple metal species, which has hampered the development of tunable libraries of colloidal materials to date. In this work, we demonstrate a seed-mediated synthesis method to incorporate five or more metal elements into uniform, colloidally stable nanocrystals. By integrating machine learning-accelerated simulations, the synthesis of shortlisted high-entropy alloy nanocrystals was demonstrated. Multiple seed materials can be used, leading to a library of multimetallic nanocrystals with tunable electronic, physical, and alloy structures. The advantage of this synthetic protocol is highlighted in the preparation of catalytic materials that showed 2 orders of magnitude higher reaction rates than monometallic catalysts and outstanding thermal stability, thus highlighting the promise of this approach for high-performance materials in many areas.

  PublisherDOI

• Transparent Reporting for Agentic Catalysis Enabled by Artificial Intelligence (TRACE-AI): Community Guidelines and A Publication Checklist

  ChemRxiv · 2026-03-25

  articleOpen access

  Artificial intelligence (AI) is increasingly integrated into catalysis science, enabling agentic workflows in which AI systems perceive inputs, reason under constraints, plan, and autonomously execute in silico or physical experiments with minimal human intervention. While these closed-loop capabilities hold promise to accelerate knowledge generation and technological innovation, they inevitably introduce new sources of variability in data lineage, model specification, and agent policies that can undermine FAIRness, rigor, and reproducibility. These risks are particularly pronounced in heterogeneous catalysis, where subtleties in catalyst synthesis and pretreatment, dynamic restructuring under operating conditions, and transport-mediated local environments can largely determine catalytic outcomes. To address these challenges, we introduce TRACE-AI (Transparent Reporting for Agentic Catalysis Enabled by Artificial Intelligence) as a set of community guidelines paired with a publication checklist. TRACE-AI emphasizes end-to-end traceability across the full lifecycle of an agentic catalysis campaign, linking research objectives to data and models, agent reasoning and action, and the knowledge acquired. By promoting standardized and accountable reporting, TRACE-AI aims to cultivate a shared foundation for accelerating scientific discovery while reinforcing safety and trust as autonomous catalysis laboratories continue to emerge.

  PublisherDOI

• Powder‐to‐Film Conversion of Nickel Single‐Atom Catalysts into Binder‐Free and Resistant Electrodes

  Advanced Materials Interfaces · 2026-02-13

  articleOpen access

  ABSTRACT Although a few binder‐free and self‐supported single‐atom electrodes have been reported, achieving mechanically robust, defect‐engineered, and reproducible films that preserve atomic dispersion under electrochemical operation remains challenging. This work addresses this limitation by presenting a versatile and generalizable strategy to transform powders into standalone, defect‐engineered thin films hosting atomically dispersed Ni centers within conductive 2D frameworks. The physicochemical and electronic properties of these materials are thoroughly characterized using a comprehensive set of spectroscopic and microscopic techniques and confirmed the homogeneous dispersion and monoatomic nature of the Ni centers (0.94 wt.%) on the electrode films. Electrochemical testing via cyclic voltammetry and electrochemical impedance spectroscopy under a range of experimental conditions revealed that integration of Ni single atoms markedly enhanced performance and stability compared to carbon nanotube‐only electrodes, maintaining integrity after 15 h of continuous operation. This improvement is accompanied by a notable reduction in charge transfer resistance (30.50 Ω) and an increase in double‐layer capacitance (295.45 µF). Post‐electrochemical analyses corroborated the structural integrity and robustness of the electrodes. Overall, this work bridges atomically precise catalysis and device‐level electrochemistry, opening a route toward reproducible and scalable single‐atom electrodes for sensing and energy conversion.

  PublisherOA PDFDOI

• Powder‐to‐Film Conversion of Nickel Single‐Atom Catalysts into Binder‐Free and Resistant Electrodes (Adv. Mater. Interfaces 9/2026)

  Advanced Materials Interfaces · 2026-05-01

  articleOpen access

  The cover illustrates the interfacial system at the core of this study, which can be used for electrochemical reactions and sensing. The layered architecture represents an engineered electrochemical interface in which a single-atom catalyst is entrapped within carbon nanotube filaments, while the magnified inset highlights the molecular-scale active site responsible for the observed functionality. More details can be found in the Research Article by Gianvito Vilé and co-workers (DOI: 10.1002/admi.202500984).

  PublisherDOI

• Basal Plane Doping to Activate Colloidal MoS ₂ Nanosheets for Catalytic Hydrodeoxygenation of <i>para-</i> Cresol

  ACS Applied Materials & Interfaces · 2026-04-04

  articleOpen accessCorresponding

  The valorization of biomass into biofuels is a critical process for producing renewable fuels. Hydrodeoxygenation (HDO), particularly over doped molybdenum disulfide (MoS2), a transition metal dichalcogenide (TMD) material, is a common representative catalytic reaction system for converting biomass-derived materials into useful hydrocarbons. However, the location and role of dopants, such as Co, in HDO is not fully understood. The effects of dopant location and oxidation state are often precluded by inhomogeneity in the ensemble properties of nanosheet size and dopant dispersion, as well as difficulty in observing the behavior of atomic site behavior directly. Using a colloidal approach to synthesize cobalt-doped MoS2 nanosheets with controlled dopant concentration, combined with X-ray absorption spectroscopy (XAS) and density functional theory (DFT) calculations, we determine that basal plane doped Co (25% Co:Mo mole ratio) shows peak catalytic activity in HDO of para-cresol, a model biomass-derived compound, and that basal Co sites are demonstrably more active than edge sites. By observing these doping effects in MoS2 catalysts for HDO, we can further optimize not only the production of carbon-neutral fuels but also direct the tailoring of doped TMD catalysts toward their intended applications.

  PublisherDOI

Recent grants

• Collaborative Research: Designing Functional Bioligand Interfaces for Multifunctional Nanomaterials

  NSF · $300k · 2022–2026

• DMREF: Collaborative Research: Toolkit to Characterize and Design Bi-functional Nanoparticle Catalysts

  NSF · $270k · 2015–2017

• NSF/DMR-BSF: Origin of Large Electromechanical Response in Non-Classical Electrostrictors

  NSF · $444k · 2016–2019

• Collaborative Research: Probing Reconfigurable Nanoparticle Biointerfaces using Catalysis

  NSF · $150k · 2019–2022

• International Collaboration in Chemistry: Doping of Colloidal Semiconductor Nanocrystals: Synthesis, Diffusion Mechanisms, Structure and Optoelectronic Properties

  NSF · $375k · 2014–2017

Frequent coauthors

• Ralph G. Nuzzo

  132 shared

• Yuanyuan Li

  Henan University of Technology

  103 shared

• Judith C. Yang

  University of Pittsburgh

  103 shared

• Janis Timoshenko

  Fritz Haber Institute of the Max Planck Society

  87 shared

• Xiaowei Teng

  84 shared

• Nicholas Marcella

  Stony Brook University

  83 shared

• Jonathan C. Hanson

  80 shared

• Igor Lubomirsky

  72 shared

Education

• Ph.D., Physics

  Tel Aviv University

  1995

• M.S., Physics

  St. Petersburg University

  1987

Awards & honors

• Ross Coffin Purdy Award, 2025
• Schulich Visiting Professor Lectureship award, Technion, Isr…
• Fellow of the American Association for the Advancement of Sc…
• Fellow of the American Physical Society, 2017
• Selected for “Top-10 Discoveries and Scientific Achievements…