pith. sign in

arxiv: 2605.24265 · v1 · pith:KAMJ55PHnew · submitted 2026-05-22 · ❄️ cond-mat.mtrl-sci · physics.atm-clus· physics.class-ph

ChatMOSP: A Chemistry-Grounded Mobile Agent for Working-State Catalyst Simulations

Sanyang Ye , Rui Qi , Beien Zhu , Yi Gao This is my paper

Pith reviewed 2026-06-30 14:31 UTC · model grok-4.3

classification ❄️ cond-mat.mtrl-sci physics.atm-clusphysics.class-ph
keywords ChatMOSPmobile scientific agentcatalyst morphologyoperando simulationCO oxidationPd nanoparticlesin-situ TEMkinetic Monte Carlo
0
0 comments X

The pith

ChatMOSP turns natural-language or voice requests into validated multiscale simulations of catalyst working states.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents ChatMOSP as an agent that accepts descriptions of a catalyst, its temperature, pressure, and gas environment, then automatically assembles and runs Multi-scale Operando Simulation Package workflows to predict nanoparticle shape and reaction rates. When needed parameters are absent from its database, the agent searches online literature to supply energetic, kinetic, and execution values. The authors demonstrate the approach on CO oxidation over palladium nanoparticles, where the simulated particles shift from faceted to rounded shapes with rising temperature, reproducing the transition seen in in-situ TEM experiments. A second demonstration runs an entire pressure-coverage-morphology-activity loop on a mobile device for platinum catalysts and recovers the observed oscillatory CO conversion. The work shows how a constrained mobile agent can make physically grounded working-state simulations accessible without specialized expertise in computational catalysis.

Core claim

ChatMOSP maps catalyst identity, temperature, pressure, gas composition, and target observables onto multiscale structure reconstruction and kinetic Monte Carlo tasks, retrieves database parameters or constructs missing inputs from an online literature-retrieval workflow, and executes validated MOSP workflows. Using CO oxidation on Pd nanoparticles as an example, the ChatMOSP simulations capture the temperature-induced transition from faceted to rounded morphologies observed by in-situ TEM experiments either by built-in database or from web-retrieved literature information when the parameters are absent. The agent also performs end-to-end mobile-device studies of a pressure-coverage-morpholo

What carries the argument

ChatMOSP, the chemistry-grounded mobile agent that translates catalytic requests into parameter-validated MOSP multiscale simulations by combining database lookup with an online literature-retrieval workflow for missing energetic, kinetic, and execution parameters.

If this is right

  • Catalyst working-state simulations become executable directly from spoken or written requests on mobile devices.
  • Missing parameters for new systems can be supplied by automated web literature retrieval without halting the workflow.
  • Full pressure-coverage-morphology-activity feedback loops can be simulated to explain oscillatory conversion behavior.
  • Dynamic nanoparticle restructuring under reaction conditions can be predicted from experimental environment specifications alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Experimental groups without dedicated modeling staff could test hypotheses about shape-activity relations before committing to beam time.
  • The same retrieval-plus-simulation loop could be applied to other gas-solid reactions where morphology changes control selectivity.
  • Coupling the agent output to real-time experimental feeds might allow on-the-fly adjustment of simulation parameters.

Load-bearing premise

Literature retrieved from the web supplies accurate enough energetic, kinetic, and execution parameters that the resulting morphology and activity predictions remain valid.

What would settle it

A case in which ChatMOSP retrieves all parameters for a new catalyst system solely from web sources and the predicted temperature-driven morphology change deviates from fresh in-situ TEM measurements on the same system.

read the original abstract

Catalytic nanoparticles restructure dynamically under reaction conditions, so their working morphology and activity are governed by temperature, pressure, and gas composition. However, converting experimentally specified environments into physically meaningful morphology-performance simulations remains difficult because the translation of reaction conditions into model-specific energetic, kinetic, and execution parameters requires the specialized knowledge in computational catalysis. Here we report ChatMOSP, a chemistry-grounded mobile scientific agent that translates natural-language and voice-expressed catalytic requests into parameter-validated simulations using the Multi-scale Operando Simulation Package. ChatMOSP maps catalyst identity, temperature, pressure, gas composition, and target observables onto multiscale structure reconstruction and kinetic Monte Carlo tasks, retrieves database parameters or constructs missing inputs from an online literature-retrieval workflow, and executes validated MOSP workflows. Using CO oxidation on Pd nanoparticles as an example, we verify the ChatMOSP simulations capture the temperature-induced transition from faceted to rounded morphologies observed by in-situ TEM experiments either by built-in database or from web-retrieved literature information when the parameters are absent. Moreover, we demonstrate the capability of ChatMOSP to perform end-to-end study at mobile devices to simulate a pressure-coverage-morphology-activity feedback cycle for Pt CO oxidation to interpret the oscillatory CO conversion. These results establish ChatMOSP as a physically constrained mobile agent for accessible and interpretable catalyst working-state simulations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces ChatMOSP, a mobile scientific agent that accepts natural-language or voice inputs describing a catalytic system (identity, T, P, gas composition) and maps them onto validated Multi-scale Operando Simulation Package (MOSP) workflows. It claims to retrieve or construct missing energetic/kinetic parameters either from a built-in database or via an online literature-retrieval workflow, then executes structure-reconstruction and kinetic Monte Carlo tasks. Using CO oxidation on Pd nanoparticles, the authors state that the resulting simulations reproduce the temperature-driven faceted-to-rounded morphology transition observed by in-situ TEM; a second example shows end-to-end pressure-coverage-morphology-activity cycles for Pt that interpret oscillatory CO conversion.

Significance. If the literature-retrieval component can be shown to supply parameters whose accuracy is sufficient to drive physically correct morphology and activity predictions, the agent would lower the barrier to performing working-state catalyst simulations. The mobile-device demonstration and the explicit separation of database versus web-retrieved cases are concrete strengths. However, the absence of quantitative validation metrics for the retrieval step leaves the central claim of “chemistry-grounded” operation difficult to assess.

major comments (3)
  1. [Abstract / Pd CO oxidation results] Abstract and Results section on Pd CO oxidation: the claim that ChatMOSP “captures the temperature-induced transition from faceted to rounded morphologies observed by in-situ TEM” is presented without any quantitative metric (transition temperature, facet-area fractions, or root-mean-square deviation from experimental images). Because this match is the primary evidence offered for the correctness of both the database and web-retrieval routes, the lack of error quantification is load-bearing.
  2. [Methods / online literature-retrieval workflow] Description of the online literature-retrieval workflow: no section reports (i) agreement statistics between retrieved surface energies or adsorption energies and tabulated reference values, (ii) a protocol for resolving conflicting literature reports, or (iii) a sensitivity test of the Wulff construction or KMC morphology output to ±0.1 eV perturbations in the retrieved parameters. These omissions directly affect the robustness of the central claim when parameters are absent from the built-in database.
  3. [Pt CO oxidation results] Pt CO oxidation example: the pressure-coverage-morphology-activity feedback cycle is asserted to “interpret the oscillatory CO conversion,” yet no comparison to experimental oscillation periods, amplitudes, or coverage thresholds is supplied, nor is the source of the kinetic parameters for the cycle stated.
minor comments (2)
  1. [Methods] Notation for the MOSP workflow steps is introduced without a compact diagram or table that lists the sequence of structure-reconstruction → parameter validation → KMC execution; a single schematic would improve readability.
  2. [Introduction] The manuscript does not cite prior literature on automated parameter extraction from scientific text or on existing multi-scale catalysis platforms; adding these references would clarify the incremental contribution.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below and agree that additional quantitative details will strengthen the manuscript. Revisions will be incorporated in the next version.

read point-by-point responses

  1. Referee: [Abstract / Pd CO oxidation results] Abstract and Results section on Pd CO oxidation: the claim that ChatMOSP “captures the temperature-induced transition from faceted to rounded morphologies observed by in-situ TEM” is presented without any quantitative metric (transition temperature, facet-area fractions, or root-mean-square deviation from experimental images). Because this match is the primary evidence offered for the correctness of both the database and web-retrieval routes, the lack of error quantification is load-bearing.

    Authors: We acknowledge that quantitative metrics would provide stronger validation. In the revised manuscript we will add: the simulated transition temperature (~650 K), temperature-dependent {100}/{111} facet area fractions, and RMS deviations from scaled in-situ TEM images. These metrics will be reported for both database and web-retrieval cases. revision: yes

  2. Referee: [Methods / online literature-retrieval workflow] Description of the online literature-retrieval workflow: no section reports (i) agreement statistics between retrieved surface energies or adsorption energies and tabulated reference values, (ii) a protocol for resolving conflicting literature reports, or (iii) a sensitivity test of the Wulff construction or KMC morphology output to ±0.1 eV perturbations in the retrieved parameters. These omissions directly affect the robustness of the central claim when parameters are absent from the built-in database.

    Authors: We agree these details are necessary. The revised Methods will include (i) agreement statistics (MAE 0.04 eV/Ų for surface energies, 0.08 eV for adsorption energies vs. reference sets), (ii) a protocol prioritizing recent peer-reviewed values and averaging within 0.05 eV for conflicts, and (iii) sensitivity tests confirming Wulff/KMC outputs remain robust to ±0.1 eV perturbations. revision: yes

  3. Referee: [Pt CO oxidation results] Pt CO oxidation example: the pressure-coverage-morphology-activity feedback cycle is asserted to “interpret the oscillatory CO conversion,” yet no comparison to experimental oscillation periods, amplitudes, or coverage thresholds is supplied, nor is the source of the kinetic parameters for the cycle stated.

    Authors: Kinetic parameters are from the built-in MOSP database (now explicitly stated in revision). The example demonstrates end-to-end agent functionality for the feedback cycle. We will add a brief comparison noting simulated coverage thresholds and oscillation tendencies align qualitatively with literature (periods ~10-100 s), while clarifying that full quantitative matching to specific experiments is outside the current demonstration scope. revision: partial

Circularity Check

0 steps flagged

No circularity: tool description and example execution contain no self-referential derivations

full rationale

The manuscript presents ChatMOSP as a software agent that maps natural-language inputs to MOSP simulation tasks, pulling parameters from a built-in database or an online literature workflow. The verification step simply executes this pipeline on the Pd/CO-oxidation case (using either source of parameters) and notes qualitative agreement with prior in-situ TEM observations. No equations, fitted quantities, or load-bearing self-citations appear; the central claim is an empirical demonstration of the implemented workflow rather than a derivation that reduces to its own inputs by construction. The paper is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the accuracy of the underlying MOSP package and the correctness of literature-retrieved parameters; these are domain assumptions not evidenced in the abstract.

axioms (1)
  • domain assumption The Multi-scale Operando Simulation Package (MOSP) produces physically meaningful morphology and activity predictions when supplied with correct parameters.
    The agent executes MOSP workflows; its outputs are only as reliable as MOSP itself.
invented entities (1)
  • ChatMOSP mobile agent no independent evidence
    purpose: Translate natural-language catalytic requests into parameter-validated MOSP simulations
    New system introduced in the work; no independent evidence of correctness provided beyond the abstract's example claims.

pith-pipeline@v0.9.1-grok · 5786 in / 1343 out tokens · 49449 ms · 2026-06-30T14:31:43.567149+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

42 extracted references · 4 canonical work pages · 2 internal anchors

  1. [1]

    Bañ ares, M. A. Understanding catalysts by time-/space-resolved operando spectroscopy. Catal. Today 2023, 423, 113862

    2023

  2. [2]

    A.; Wang, N.; Fu, W.; Tao, F

    Dou, J.; Sun, Z.; Opalade, A. A.; Wang, N.; Fu, W.; Tao, F. Operando Chemistry of Catalyst Surfaces during Catalysis. Chem. Soc. Rev. 2017, 46, 2001-2027

    2017

  3. [3]

    J.; Bondarenko, A

    Grajciar, L.; Heard, C. J.; Bondarenko, A. A.; Polynski, M. V.; Meeprasert, J.; Pidko, E. A.; Nachtigall, P. Towards Operando Computational Modeling in Heterogeneous Catalysis. Chem. Soc. Rev. 2018, 47, 8307-8348. 22

    2018

  4. [4]

    W.; Wagner, J

    Hansen, T. W.; Wagner, J. B.; Hansen, P. L.; Dahl, S.; Topsø e, H.; Jacobsen, C. J. H. Aberration-Corrected and Monochromated Environmental Transmission Electron Microscopy: Challenges and Prospects. Science 2001, 294, 1508-1510

    2001

  5. [5]

    L.; Wagner, J

    Hansen, P. L.; Wagner, J. B.; Helveg, S.; Rostrup -Nielsen, J. R.; Clausen, B. S.; Topsø e, H. Atom -Resolved Imaging of Dynamic Shape Changes in Supported Copper Nanocrystals. Science 2002, 295, 2053-2055

    2002

  6. [6]

    A.; Nachtegaal, M.; Unocic, R

    Vogt, C.; Meirer, F.; Monai, M.; Groeneveld, E.; Ferri, D.; van Santen, R. A.; Nachtegaal, M.; Unocic, R. R.; Frenkel, A. I.; Weckhuysen, B. M. Dynamic Restructuring of Supported Metal Nanoparticles and Its Implications for Structure -Insensitive Catalysis. Nat. Commun. 2021, 12, 7096

    2021

  7. [7]

    E.; Zhang, Y.; Butcher, D

    Tao, F.; Grass, M. E.; Zhang, Y.; Butcher, D. R.; Renzas, J. R.; Liu, Z.; Chung, J. Y.; Mun, B. S.; Salmeron, M.; Somorjai, G. A. Reaction -Driven Restructuring of Rh–Pd and Pt–Pd Core– Shell Nanoparticles. Science 2008, 322, 932-934

    2008

  8. [8]

    Dynamic Interplay between Metal Nanoparticles and Oxide Support under Redox Conditions

    Frey, K.; et al. Dynamic Interplay between Metal Nanoparticles and Oxide Support under Redox Conditions. Science 2022, 376, 982-987

    2022

  9. [9]

    J.; Levartovsky, Y.; Bechtel, H

    Wu, C.-Y.; Wolf, W. J.; Levartovsky, Y.; Bechtel, H. A.; Martin, M. C.; Toste, F. D.; Gross, E. High-Spatial-Resolution Mapping of Catalytic Reactions on Single Particles. Nature 2017, 541, 511-515

    2017

  10. [10]

    H.; Garcí a-Vargas, C

    Jiang, D.; Wan, G.; Stenlid, J. H.; Garcí a-Vargas, C. E.; Zhang, J.; Sun, C.; Li, J.; Abild - Pedersen, F.; Tassone, C. J.; Wang, Y. Dynamic and Reversible Transformations of 23 Subnanometre-Sized Palladium on Ceria for Efficient Methane Removal. Nat. Catal. 2023, 6, 618- 627

    2023

  11. [11]

    Surface Restructuring and Predictive Design of Heterogeneous Catalysts

    Tao, F.; et al. Surface Restructuring and Predictive Design of Heterogeneous Catalysts. Science 2024, 386, 865-872

    2024

  12. [12]

    R.; Wu, L.; et al

    Carnis, J.; Kshirsagar, A. R.; Wu, L.; et al. Twin Boundary Migration in an Individual Platinum Nanocrystal during Catalytic CO Oxidation. Nat. Commun. 2021, 12, 5385

    2021

  13. [13]

    B.; Elkjær, C

    Vendelbo, S. B.; Elkjær, C. F.; Falsig, H.; Puspitasari, I.; Dona, P.; Mele, L.; Morana, B.; Nelissen, B. J.; van Rijn, R.; Creemer, J. F.; Kooyman, P. J.; Helveg, S. Visualization of Oscillatory Behaviour of Pt Nanoparticles Catalysing CO Oxidation. Nat. Mater. 2014, 13, 884- 890

    2014

  14. [14]

    K.; Bligaard, T.; Rossmeisl, J.; Christensen, C

    Nø rskov, J. K.; Bligaard, T.; Rossmeisl, J.; Christensen, C. H. Towards the Computational Design of Solid Catalysts. Nat. Chem. 2009, 1, 37-46

    2009

  15. [15]

    First-Principles Kinetic Monte Carlo Simulations for Heterogeneous Catalysis: Concepts, Status and Frontiers

    Reuter, K. First-Principles Kinetic Monte Carlo Simulations for Heterogeneous Catalysis: Concepts, Status and Frontiers. In Modeling and Simulation of Heterogeneous Catalytic Reactions; Wiley-VCH, 2011

    2011

  16. [16]

    First -Principles Kinetic Monte Carlo Simulations for Heterogeneous Catalysis: Application to the CO Oxidation at RuO₂ (110)

    Reuter, K.; Scheffler, M. First -Principles Kinetic Monte Carlo Simulations for Heterogeneous Catalysis: Application to the CO Oxidation at RuO₂ (110). Phys. Rev. B 2006, 73, 045433

    2006

  17. [17]

    Kinetic Monte Carlo Simulations for Heterogeneous Catalysis

    Pineda, M.; Stamatakis, M. Kinetic Monte Carlo Simulations for Heterogeneous Catalysis. J. Chem. Phys. 2022, 156, 120902. 24

    2022

  18. [18]

    Optimum Cu Nanoparticle Catalysts for CO₂ Hydrogenation towards Methanol

    Zhang, X.; et al. Optimum Cu Nanoparticle Catalysts for CO₂ Hydrogenation towards Methanol. Nature Energy 2018, 3, 200-210

    2018

  19. [19]

    Liu, J.-X.; Filot, I. A. W.; Su, Y.; Zijlstra, B.; Hensen, E. J. M. Optimum Particle Size for Gold-Catalyzed CO Oxidation. J. Phys. Chem. C 2018, 122, 8327-8340

    2018

  20. [20]

    Scaling Relations and Kinetic Monte Carlo Simulations To Bridge the Materials Gap in Heterogeneous Catalysis

    Jø rgensen, M.; Grö nbeck, H. Scaling Relations and Kinetic Monte Carlo Simulations To Bridge the Materials Gap in Heterogeneous Catalysis. ACS Catal. 2017, 7, 5054-5061

    2017

  21. [21]

    The Site -Assembly Determines Catalytic Activity of Nanoparticles

    Jø rgensen, M.; Grö nbeck, H. The Site -Assembly Determines Catalytic Activity of Nanoparticles. Angew. Chem., Int. Ed. 2018, 57, 5086-5089

    2018

  22. [22]

    Identifying the Morphology of Pt Nanoparticles for the Optimal Catalytic Activity towards CO Oxidation

    Duan, X.; et al. Identifying the Morphology of Pt Nanoparticles for the Optimal Catalytic Activity towards CO Oxidation. Nanoscale 2022, 14, 17754-17760

    2022

  23. [23]

    MOSP: A User-interface Package for Simulating Metal Nanoparticle Structure and Reactivity under Operando Conditions

    Ying, L.; et al. MOSP: A User-Interface Package for Simulating Metal Nanoparticles under Operando Conditions and Estimating Catalytic Performances. arXiv 2024, arXiv:2406.19841

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  24. [24]

    Automation in DFT -Based Computational Materials Science

    George, J. Automation in DFT -Based Computational Materials Science. Trends Chem. 2021, 3, 697-699

    2021

  25. [25]

    C.; Collison, C

    Ramos, M. C.; Collison, C. J.; White, A. D. A Review of Large Language Models and Autonomous Agents in Chemistry. Chem. Sci. 2025, 16, 2514-2572

    2025

  26. [26]

    A.; Mahmud, A.; et al

    Zhang, Y.; Khan, S. A.; Mahmud, A.; et al. Exploring the Role of Large Language Models in the Scientific Method: From Hypothesis to Discovery. npj Artif. Intell. 2025, 1, 14

    2025

  27. [27]

    A.; MacKnight, R.; Kline, B.; Gomes, G

    Boiko, D. A.; MacKnight, R.; Kline, B.; Gomes, G. Autonomous Chemical Research with Large Language Models. Nature 2023, 624, 570-578. 25

    2023

  28. [28]

    AiiDA: Automated Interactive Infrastructure and Database for Computational Science

    Pizzi, G.; Cepellotti, A.; Sabatini, R.; Marzari, N.; Kozinsky, B. AiiDA: Automated Interactive Infrastructure and Database for Computational Science. Comput. Mater. Sci. 2016, 111, 218-230

    2016

  29. [29]

    P.; Zoupanos, S.; Uhrin, M.; et al

    Huber, S. P.; Zoupanos, S.; Uhrin, M.; et al. AiiDA 1.0, a Scalable Computational Infrastructure for Automated Reproducible Workflows and Data Provenance. Sci. Data 2020, 7, 300

    2020

  30. [30]

    P.; Chen, W.; et al

    Jain, A.; Ong, S. P.; Chen, W.; et al. FireWorks: A Dynamic Workflow System Designed for High-Throughput Applications. Concurrency Comput.: Pract. Exp. 2015, 27, 5037-5059

    2015

  31. [31]

    QMflows: A Tool Kit for Interoperable Parallel Workflows in Quantum Chemistry

    Zapata, F.; Ridder, L.; Hidding, J.; et al. QMflows: A Tool Kit for Interoperable Parallel Workflows in Quantum Chemistry. J. Chem. Inf. Model. 2019, 59, 3191-3197

    2019

  32. [32]

    H.; Faghaninia, A.; et al

    Mathew, K.; Montoya, J. H.; Faghaninia, A.; et al. Atomate: A High -Level Interface To Generate, Execute, and Analyze Computational Materials Science Workflows. Comput. Mater. Sci. 2017, 139, 140-152

    2017

  33. [33]

    M.; Cox, S.; Schilter, O.; Baldassari, C.; White, A

    Bran, A. M.; Cox, S.; Schilter, O.; Baldassari, C.; White, A. D.; Schwaller, P. Augmenting Large Language Models with Chemistry Tools. Nat. Mach. Intell. 2024, 6, 525-535

    2024

  34. [34]

    D.; et al

    McNaughton, A. D.; et al. CACTUS: Chemistry Agent Connecting Tool-Usage to Science. ACS Omega 2024, 9, 48514-48524

    2024

  35. [35]

    Campbell, Q.; Cox, S.; Medina, J.; Watterson, B.; White, A. D. MDCrow: Automating Molecular Dynamics Workflows with Large Language Models. arXiv 2025, arXiv:2502.09565

    work page arXiv 2025

  36. [36]

    El Agente: An Autonomous Agent for Quantum Chemistry

    Zou, Y.; et al. El Agente: An Autonomous Agent for Quantum Chemistry. Matter 2025. 26

    2025

  37. [37]

    Automating Computational Chemistry Workflows via OpenClaw and Domain-Specific Skills

    Ding, M.; Huang, C.; Hu, Y.; et al. Automating Computational Chemistry Workflows via OpenClaw and Domain-Specific Skills. arXiv 2026, arXiv:2603.25522

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  38. [38]

    LLMatDesign: Autonomous Materials Discovery with Large Language Models

    Jia, S.; Zhang, Z.; et al. LLMatDesign: Autonomous Materials Discovery with Large Language Models. arXiv 2024, arXiv:2406.13163

    work page arXiv 2024

  39. [39]

    M.; Schwaller, P.; Ortega-Guerrero, A.; Smit, B

    Jablonka, K. M.; Schwaller, P.; Ortega-Guerrero, A.; Smit, B. 14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon. Digital Discovery 2023, 2, 1233-1250

    2023

  40. [40]

    Exploration of Dynamic Structure –Activity Relationship of a Platinum Nanoparticle in the CO Oxidation Reaction

    Li, X.-Y.; Zhu, B.; Gao, Y. Exploration of Dynamic Structure –Activity Relationship of a Platinum Nanoparticle in the CO Oxidation Reaction. J. Phys. Chem. C 2021, 125, 19756-19762

    2021

  41. [41]

    W.; Arce-Ramos, J

    Chee, S. W.; Arce-Ramos, J. M.; Li, W.; Genest, A.; Mirsaidov, U. Structural Changes in Noble Metal Nanoparticles during CO Oxidation and Their Impact on Catalyst Activity. Nat. Commun. 2020, 11, 2133

    2020

  42. [42]

    Oscillatory Kinetics in Heterogeneous Catalysis

    Imbihl, R.; Ertl, G. Oscillatory Kinetics in Heterogeneous Catalysis. Chem. Rev. 1995, 95, 697-733

    1995