BetaCitation Atlas

RESEARCH PAPER · ACS Catalysis · 2019

High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane

Thanh Nhat Nguyen, Thuy Phuong Nhat Tran, Ken Takimoto, Ashutosh Thakur, Shun Nishimura, Junya Ohyama, Itsuki Miyazato, Lauren Takahashi, Jun Fujima, KEISUKE G. TAKAHASHI, Toshiaki Taniike

Read original paper ↗

See where this research leads.

176 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of MethaneOriginalCatalyst Acquisition by Data Science (CADS): a web-based catalyst informatics platform for discovering catalysts51High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery179Synthesis and molecular structure of model silica-supported tungsten oxide catalysts for oxidative coupling of methane (OCM)53A Primer about Machine Learning in Catalysis – A Tutorial with Code33Accurate prediction of binding energies for two‐dimensional catalytic materials using machine learning18Structure-performance relationship of Mg(OEt)2-based Ziegler-Natta catalysts33Multidimensional Classification of Catalysts in Oxidative Coupling of Methane through Machine Learning and High-Throughput Data30From catalyst to process: bridging the scales in modeling the OCM reaction15Machine learning in experimental materials chemistry66Revisiting Machine Learning Predictions for Oxidative Coupling of Methane (OCM) based on Literature Data35High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials127Towards Experimental Handbooks in Catalysis62Direct design of active catalysts for low temperature oxidative coupling of methane via machine learning and data mining31Catalyst design and tuning for oxidative dehydrogenation of propane – A review153Direct Design of Catalysts in Oxidative Coupling of Methane via High‐Throughput Experiment and Deep Learning21Reaction condition optimization for non-oxidative conversion of methane using artificial intelligence35A combined computational and experimental study of methane activation during oxidative coupling of methane (OCM) by surface metal oxide catalysts16Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts26First-Principles Calculations of Stability, Electronic Structure, and Sorption Properties of Nanoparticle Systems1High-throughput development of highly active catalyst system to convert bioethanol to 1,3-butadiene13Representing Catalytic and Processing Space in Methane Oxidation Reaction via Multioutput Machine Learning13Data science assisted investigation of catalytically active copper hydrate in zeolites for direct oxidation of methane to methanol using H2O223Learning Catalyst Design Based on Bias-Free Data Set for Oxidative Coupling of Methane54Design and tailoring of advanced catalytic process for light alkanes upgrading18A study on active sites of A2BO4 catalysts with perovskite-like structures in oxidative coupling of methane26Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of Methane23Unveiling gas‐phase oxidative coupling of methane via data analysis7Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts77Continuous methane to ethane conversion using gaseous oxygen on ceria-based Pd catalysts at low temperatures7Mechanochemical Route for Preparation of MFI-Type Zeolites Containing Highly Dispersed and Small Ce Species and Catalytic Application to Low-Temperature Oxidative Coupling of Methane11Hybrid catalysts containing Ba, Ti, Mn, Na, and W for the low-temperature oxidative coupling of methane19Photocatalytic Conversion of Methane: Recent Advancements and Prospects251Photocatalytic Conversion of Methane: Recent Advancements and Prospects95Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts12Productivity Enhancement for the Oxidative Coupling of Methane in Adiabatic Layered-Bed Reactors5Materials Informatics for 2D Materials Combined with Sparse Modeling and Chemical Perspective: Toward Small-Data-Driven Chemistry and Materials Science52Size‐Distribution Control of Exfoliated Nanosheets Assisted by Machine Learning: Small‐Data‐Driven Materials Science Using Sparse Modeling18Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters53Exploring Metal Cluster Catalysts Using Swarm Intelligence: Start with Hydrogen Adsorption5Waste-derived calcium oxide catalysts for oxidative coupling of methane6Factors to influence low-temperature performance of supported Mn–Na2WO4 in oxidative coupling of methane11Automated synthesis and data accumulation for fast production of high-performance Ni nanocatalysts10Engineering Thermally Resistant Catalytic Particles for Oxidative Coupling of Methane Using Spray-Drying and Incorporating SiC15Oxidative Coupling of Methane for Ethylene Production: Reviewing Kinetic Modelling Approaches, Thermodynamics and Catalysts11Hydrodynamic Characteristics of an Internal Recycle Berty Catalytic Reactor in Batch/Continuous or Packed/Fluidized Bed Modes8Sparse modeling for small data: case studies in controlled synthesis of 2D materials26High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La 2 O 3 -based catalysts for the oxidative coupling of methane16Machine learning for design principles for single atom catalysts towards electrochemical reactions116Operando Infrared Imaging of Ozone-Assisted Catalysis for High-Throughput Screening of Catalytic Activity5Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46The Rise of Catalysts Informatics1A chemistry‐inspired neural network kinetic model for oxidative coupling of methane from high‐throughput data12Effect of B Site Substitution on the Catalytic Activity of La‐Based Perovskite for Oxidative Coupling of Methane17High‐throughput and High‐speed Absorbance Measurements in Microfluidic Droplets using Hyperspectral Imaging9Transition-Metal-Doping of CaO as Catalyst for the OCM Reaction, a Reality Check12Oxidative Coupling of Methane: Examining the Inactivity of the MnO x ‐Na 2 WO 4 /SiO 2 Catalyst at Low Temperature10Oxidative Coupling of Methane: Examining the Inactivity of the MnO x ‐Na 2 WO 4 /SiO 2 Catalyst at Low Temperature63Autonomous high-throughput computations in catalysis29Comparative evaluation of artificial neural networks for the performance prediction of Pt‐based catalysts in water gas shift reaction12Thermochemical Conversion of Plastic Waste into Fuels, Chemicals, and Value‐Added Materials: A Critical Review and Outlooks172Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter12Trendbericht Physikalische Chemie 2022: In‐situ‐Spektroskopie und Katalyse0Autonomous chemical science and engineering enabled by self-driving laboratories72A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation182Sulfur oxidative coupling of methane process development and its modeling via machine learning3Recent progress toward catalyst properties, performance, and prediction with data-driven methods17Data in Materials and Catalysts Informatics4Metal Ions (Li, Mg, Zn, Ce) Doped into La2O3 Nanorod for Boosting Catalytic Oxidative Coupling of Methane21Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation26Improving the predictive power of microkinetic models via machine learning15Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane51High-Throughput Experimentation for Resource-Efficient Discovery of Methane Functionalization Catalysts1A deep neural network for oxidative coupling of methane trained on high-throughput experimental data4Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol Synthesis via Machine Learning53Electrochemical Screening of Au/Pt Catalysts for the Thermocatalytic Synthesis of Hydrogen Peroxide Based on Their Oxygen Reduction and Hydrogen Oxidation Activities Probed via Voltammetric Scanning Electrochemical Microscopy22Effects of Rare Earth Metal Promotion over Zeolite-Supported Fe–Cu-Based Catalysts on the Light Olefin Production Performance in Fischer–Tropsch Synthesis15Surface and Interface Coordination Chemistry Learned from Model Heterogeneous Metal Nanocatalysts: From Atomically Dispersed Catalysts to Atomically Precise Clusters278New paradigms for exploiting parallel experiments in Bayesian optimization45A modular low-cost automated synthesis machine demonstrated by ring-opening metathesis polymerization5Capacity-prediction models for organic anode-active materials of lithium-ion batteries: advances in predictors using small data10Catalysts informatics: paradigm shift towards data-driven catalyst design61High-Throughput Experimentation for Photocatalytic Water Purification in Practical Environments15Indirect design of OCM catalysts through machine learning of catalyst surface oxygen species7Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane15Prediction of suitable catalysts for the OCM reaction by combining an evolutionary approach and machine learning5Role and dynamics of transition metal carbides in methane coupling20Bridging the complexity gap in computational heterogeneous catalysis with machine learning280The value of negative results in data-driven catalysis research72Impacts of catalyst and process parameters on Ni-catalyzed methane dry reforming via interpretable machine learning58Addressing complexity in catalyst design: From volcanos and scaling to more sophisticated design strategies77High-throughput screening of multimetallic catalysts for three-way catalysis1Toward the Golden Age of Materials Informatics: Perspective and Opportunities23Exploration of Ethanol-to-Butadiene Catalysts by High-Throughput Experimentation and Machine Learning22Machine Learning Descriptors for Data‐Driven Catalysis Study154Oxidative coupling of biogas to ethylene over a trilobe-shaped Mn-Na2WO4/α-Al2O3 catalyst in a single-pellet reactor1Multi-fidelity data-driven design and analysis of reactor and tube simulations22Increasing Ethene Yield via Oxidative Coupling of Methane at Low Temperature: An Application of Machine Learning and DFT in the Design and Innovation of Effective Catalyst Compositions5Concepts of Methane Activation0Insight into role of surface Sr‐sites in Sr/LOC‐HL catalyst for promoting low‐temperature oxidative coupling of methane8Computational Study on the Effect of Thermal Boundary Conditions and Axial Aspect Ratio on Catalytic Oxidative Coupling of Methane2Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane69Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers3High-throughput screening of multimetallic catalysts for three-way catalysis3Interpretable Machine Learning with Physics-Based Feature Engineering: Application to the Catalytic Cracking Reaction of Polycyclic Aromatic Hydrocarbons8Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective76Accelerating active catalyst discovery: a probabilistic prediction-based screening methodology with applications in dry reforming of methane4Parallel Catalyst Synthesis Protocol for Accelerating Heterogeneous Olefin Polymerization Research7Carbon-efficient reaction optimization of nonoxidative direct methane conversion based on the integrated reactor system7Active site for syngas production by direct partial oxidation of CH 4 over ZrO 27Artificial intelligence in catalysis2Intelligent chemometric modelling of Al 2 O 3 supported mixed metal oxide catalysts for oxidative dehydrogenation of n -butane using simple features6Machine learning approaches for screening of materials in flexible electronic devices0Machine learning enabled exploration of multicomponent metal oxides for catalyzing oxygen reduction in alkaline media26Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis60Machine learning for catalyst design: data matters3Machine Learning-Accelerated Disentanglement of Activity–Selectivity Trade-off of Multielement Oxide Denitration Catalysts13Atomic Design of Alkyne Semihydrogenation Catalysts via Active Learning66Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane14Recent developments and current trends on catalytic dry reforming of Methane: Hydrogen Production, thermodynamics analysis, techno feasibility, and machine learning80Embracing data science in catalysis research117A quantitative investigation on pyrolysis behaviors of metal ion-exchanged coal macerals by interpretable machine learning algorithms17Model-based catalyst screening and optimal experimental design for the oxidative coupling of methane3Active learning streamlines development of high performance catalysts for higher alcohol synthesis75From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design190Phosphate-containing alkali metal-doped TbOx/MgO catalysts for the oxidative coupling of methane2Site heterogeneity and broad surface-binding isotherms in modern catalysis: Building intuition beyond the Sabatier principle16The influence of the catalyst preparation methods on performance of Mn/Na2WO4/SiO2 in oxidative coupling of methane5A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery37Small-dataset-orientated data-driven screening for catalytic propane activation6Advances in Palladium-Based Membrane Research: High-Throughput Techniques and Machine Learning Perspectives11Recent Advances in Single‐Atom Catalyst for Solar Energy Conversion: A Comprehensive Review and Future Outlook99Multidimensional Catalysis Data Representation for Designing Oxidative Coupling of Methane Catalysts1A comparative analysis of machine learning predictive models for the oxidative coupling of methane reaction1Unbiased dataset for methane dry reforming and catalyst design guidelines obtained by high-throughput experimentation and machine learning12Accelerated perovskite discovery: screening new catalysts for photocatalytic methylene blue degradation4Efficient low-temperature thermochemical CO 2 splitting enabled by Gibbs free energy engineering4Unveiling the knowledge of a RAFT polymerization database obtained from an automated parallel synthesizer1Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering6Redox CeO2 substituted Na2WO4-Mn catalyst for high-throughput chemical looping oxidative coupling of methane9Enhancing Catalyst Performance Prediction with Hybrid Quantum Neural Networks: A Comparative Study on Data Consistency Variation4Innovative Catalysis Approaches for Methane Utilization8High-Throughput Optimization of a High-Pressure Catalytic Reaction5Data‐Driven Review on Aerogels and Polymeric Aerogels2Data‐Driven Review on Aerogels and Polymeric Aerogels45Aggregated Catalyst Physicochemical Descriptor-Driven Machine Learning for Catalyst Optimization: Insights into Oxidative-Coupling-of-Methane Dynamics and C2 Yields3Transfer learning from first-principles calculations to experiments with chemistry-informed domain transformation4Development of stabilizer formulations for Poly(4-methyl-1-pentene) via high-throughput chemiluminescence imaging and genetic algorithm1A Data-Science Approach to Experimental Catalyst Discovery: Integrating Exploration, Exploitation, and Serendipity5The integration of artificial intelligence and high-throughput experiments: An innovative driving force in catalyst design24Catalyst Phylogenetic Tree: A Visualization Tool for Exploring Catalyst Research and Development from Tabular Data0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

176 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Catalyst Acquisition by Data Science (CADS): a web-based catalyst informatics platform for discovering catalysts

    Reaction Chemistry & Engineering · 2020-01-01

    Jun Fujima, Yuzuru Tanaka, Itsuki Miyazato, Lauren Takahashi, KEISUKE G. TAKAHASHI

    Read original paper ↗ ·

  2. High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery

    Physical Chemistry Chemical Physics · 2020-01-01

    Katherine McCullough, Travis Williams, Kathleen Mingle, Pooyan Jamshidi, Jochen A. Lauterbach

    Read original paper ↗ ·

  3. Synthesis and molecular structure of model silica-supported tungsten oxide catalysts for oxidative coupling of methane (OCM)

    Catalysis Science & Technology · 2020-01-01

    Daniyal Kiani, Sagar Sourav, Israel E. Wachs, Jonas Baltrušaitis

    Read original paper ↗ ·

  4. A Primer about Machine Learning in Catalysis – A Tutorial with Code

    ChemCatChem · 2020-03-18

    Stefan Palkovits

    Read original paper ↗ ·

  5. Accurate prediction of binding energies for two‐dimensional catalytic materials using machine learning

    ChemCatChem · 2020-06-02

    Julia Melisande Theresa Agatha Fischer, Michelle A. Hunter, Marlies Hankel, Debra J. Searles, Amanda J. Parker, Amanda Susan Barnard

    Read original paper ↗ ·

  6. Structure-performance relationship of Mg(OEt)2-based Ziegler-Natta catalysts

    Journal of Catalysis · 2020-07-02

    Toru Wada, Toshiki Funako, Patchanee Chammingkwan, Ashutosh Thakur, Akanksha Matta, Minoru Terano, Toshiaki Taniike

    Read original paper ↗ ·

  7. Multidimensional Classification of Catalysts in Oxidative Coupling of Methane through Machine Learning and High-Throughput Data

    The Journal of Physical Chemistry Letters · 2020-07-30

    KEISUKE G. TAKAHASHI, Lauren Takahashi, Thanh Nhat Nguyen, Ashutosh Thakur, Toshiaki Taniike

    Read original paper ↗ ·

  8. From catalyst to process: bridging the scales in modeling the OCM reaction

    Catalysis Today · 2020-08-01

    Laura Pirro, Pedro S.F. Mendes, Bram Kemseke, Bart D. Vandegehuchte, Guy B Marin, Joris W. Thybaut

    Read original paper ↗ ·

  9. Machine learning in experimental materials chemistry

    Catalysis Today · 2020-08-21

    Balaranjan Selvaratnam, Ranjit T. Koodali

    Read original paper ↗ ·

  10. Revisiting Machine Learning Predictions for Oxidative Coupling of Methane (OCM) based on Literature Data

    ChemCatChem · 2020-08-28

    Shun Nishimura, Junya Ohyama, Takaaki Kinoshita, Son Dinh Le, KEISUKE G. TAKAHASHI

    Read original paper ↗ ·

  11. High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

    Advanced Materials · 2020-09-21

    Ivan G. Clayson, Daniel Hewitt, Martin Hutereau, Tom Pope, Ben Slater

    Read original paper ↗ ·

  12. Towards Experimental Handbooks in Catalysis

    Topics in Catalysis · 2020-10-06

    Annette Trunschke, Giulia Bellini, Maxime Boniface, Spencer J. Carey, Jinhu Dong, Ezgi Erdem, Lucas Foppa, Wiebke Frandsen, Michael Geske, Luca M. Ghiringhelli, Frank Girgsdies, Rania Hanna, Maike Hashagen, Michael Hävecker, Gregory S. Huff, Axel Knop‐Gericke, Gregor Koch, Peter Kraus, Jutta Kröhnert, Pierre Kube, Stephen Lohr, Thomas Lunkenbein, Liudmyla Masliuk, Raoul Naumann d’Alnoncourt, Toyin Omojola, Christoph Pratsch, Sven Richter, Christian Rohner, Frank Rosowski, Frederik Rüther, Matthias Scheffler, Robert Schlögl, Andrey Tarasov, Detre Teschner, Olaf Timpe, Philipp Trunschke, Yuanqing Wang, Sabine Wrabetz

    Read original paper ↗ ·

  13. Direct design of active catalysts for low temperature oxidative coupling of methane via machine learning and data mining

    Catalysis Science & Technology · 2020-10-29

    Junya Ohyama, Takaaki Kinoshita, Eri Funada, Hiroshi Yoshida, Masato Machida, Shun Nishimura, Takeaki Uno, Jun Fujima, Itsuki Miyazato, Lauren Takahashi, KEISUKE G. TAKAHASHI

    Read original paper ↗ ·

  14. Catalyst design and tuning for oxidative dehydrogenation of propane – A review

    Applied Catalysis A General · 2020-11-07

    Yahya Gambo, Sagir Adamu, A.A. Abdulrasheed, Rahima A. Lucky, Mohammed S. Ba‐Shammakh, Mohammad Mozahar Hossain

    Read original paper ↗ ·

  15. Direct Design of Catalysts in Oxidative Coupling of Methane via High‐Throughput Experiment and Deep Learning

    ChemCatChem · 2020-11-12

    Kanami Sugiyama, Thanh Nhat Nguyen, Sunao Nakanowatari, Itsuki Miyazato, Toshiaki Taniike, KEISUKE G. TAKAHASHI

    Read original paper ↗ ·

  16. Reaction condition optimization for non-oxidative conversion of methane using artificial intelligence

    Reaction Chemistry & Engineering · 2020-12-17

    Hyun Woo Kim, Sung‐Woo Lee, Gyoung S. Na, Seung Ju Han, Seok Ki Kim, Jung H. Shin, Hyunju Chang, Yong Tae Kim

    Read original paper ↗ ·

  17. A combined computational and experimental study of methane activation during oxidative coupling of methane (OCM) by surface metal oxide catalysts

    Chemical Science · 2021-01-01

    Daniyal Kiani, Sagar Sourav, Israel E. Wachs, Jonas Baltrušaitis

    Read original paper ↗ ·

  18. Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts

    Chemical Science · 2021-01-01

    Lauren Takahashi, Thanh Nhat Nguyen, Sunao Nakanowatari, Aya Fujiwara, Toshiaki Taniike, KEISUKE G. TAKAHASHI

    Read original paper ↗ ·

  19. First-Principles Calculations of Stability, Electronic Structure, and Sorption Properties of Nanoparticle Systems

    Journal of Computer Chemistry Japan · 2021-01-01

    Gerardo Valadez Huerta, Yūsuke Nanba, Nor Diana Binti Zulkifli, David Samuel Rivera Rocabado, Takayoshi Ishimoto, Michihisa Koyama

    Read original paper ↗ ·

  20. High-throughput development of highly active catalyst system to convert bioethanol to 1,3-butadiene

    Reaction Chemistry & Engineering · 2021-01-01

    Yu Shinke, Tomohisa Miyazawa, Misao Hiza, Isao Nakamura, Tadahiro Fujitani

    Read original paper ↗ ·

  21. Representing Catalytic and Processing Space in Methane Oxidation Reaction via Multioutput Machine Learning

    The Journal of Physical Chemistry Letters · 2021-01-08

    Itsuki Miyazato, Thanh Nhat Nguyen, Lauren Takahashi, Toshiaki Taniike, KEISUKE G. TAKAHASHI

    Read original paper ↗ ·

  22. Data science assisted investigation of catalytically active copper hydrate in zeolites for direct oxidation of methane to methanol using H2O2

    Scientific Reports · 2021-01-22

    Junya Ohyama, Airi Hirayama, Nahoko Kondou, Hiroshi Yoshida, Masato Machida, Shun Nishimura, Kenji Hirai, Itsuki Miyazato, KEISUKE G. TAKAHASHI

    Read original paper ↗ ·

  23. Learning Catalyst Design Based on Bias-Free Data Set for Oxidative Coupling of Methane

    ACS Catalysis · 2021-01-22

    Thanh Nhat Nguyen, Sunao Nakanowatari, Thuy Phuong Nhat Tran, Ashutosh Thakur, Lauren Takahashi, KEISUKE G. TAKAHASHI, Toshiaki Taniike

    Read original paper ↗ ·

  24. Design and tailoring of advanced catalytic process for light alkanes upgrading

    EcoMat · 2021-03-16

    Wenyao Chen, Xuezhi Duan, Xinggui Zhou, De Chen

    Read original paper ↗ ·

  25. A study on active sites of A2BO4 catalysts with perovskite-like structures in oxidative coupling of methane

    Molecular Catalysis · 2021-04-01

    Dahye Kwon, Inchan Yang, Sun-A An, JeongHyun Cho, Jeong‐Myeong Ha, Ji Chul Jung

    Read original paper ↗ ·

  26. Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of Methane

    ChemCatChem · 2021-05-17

    Sunao Nakanowatari, Thanh Nhat Nguyen, Hiroki Chikuma, Aya Fujiwara, Kalaivani Seenivasan, Ashutosh Thakur, Lauren Takahashi, KEISUKE G. TAKAHASHI, Toshiaki Taniike

    Read original paper ↗ ·

  27. Unveiling gas‐phase oxidative coupling of methane via data analysis

    Journal of Computational Chemistry · 2021-05-20

    Sora Ishioka, Itsuki Miyazato, Lauren Takahashi, Thanh Nhat Nguyen, Toshiaki Taniike, KEISUKE G. TAKAHASHI

    Read original paper ↗ ·

  28. Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts

    ChemCatChem · 2021-05-31

    Shinya Mine, Motoshi Takao, Taichi Yamaguchi, Takashi Toyao, Zen Maeno, S. M. A. Hakim Siddiki, Satoru Takakusagi, Ken‐ichi Shimizu, Ichigaku Takigawa

    Read original paper ↗ ·

  29. Continuous methane to ethane conversion using gaseous oxygen on ceria-based Pd catalysts at low temperatures

    Applied Catalysis A General · 2021-06-08

    Gihun Kwon, Gunjoo Kim, Hyunjoo J. Lee

    Read original paper ↗ ·

  30. Mechanochemical Route for Preparation of MFI-Type Zeolites Containing Highly Dispersed and Small Ce Species and Catalytic Application to Low-Temperature Oxidative Coupling of Methane

    Industrial & Engineering Chemistry Research · 2021-07-07

    Mizuho Yabushita, Motohiro Yoshida, Ryota Osuga, Fumiya Muto, Shoji Iguchi, Shuhei Yasuda, Atsushi Neya, Mami Horie, Sachiko Maki, Kiyoshi Kanie, Ichiro Yamanaka, Toshiyuki Yokoi, Atsushi Muramatsu

    Read original paper ↗ ·

  31. Hybrid catalysts containing Ba, Ti, Mn, Na, and W for the low-temperature oxidative coupling of methane

    Applied Catalysis B: Environmental · 2021-07-21

    Lien Thi Do, Jae-Wook Choi, Dong Jin Suh, Chun‐Jae Yoo, Hyunjoo Lee, Jeong‐Myeong Ha

    Read original paper ↗ ·

  32. Photocatalytic Conversion of Methane: Recent Advancements and Prospects

    Angewandte Chemie International Edition · 2021-07-26

    Qi Li, Yuxing Ouyang, Hongliang Li, Liangbing Wang, Jie Zeng

    Read original paper ↗ ·

  33. Photocatalytic Conversion of Methane: Recent Advancements and Prospects

    Angewandte Chemie · 2021-07-26

    Qi Li, Yuxing Ouyang, Hongliang Li, Liangbing Wang, Jie Zeng

    Read original paper ↗ ·

  34. Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts

    The Journal of Physical Chemistry Letters · 2021-07-30

    KEISUKE G. TAKAHASHI, Jun Fujima, Itsuki Miyazato, Sunao Nakanowatari, Aya Fujiwara, Thanh Nhat Nguyen, Toshiaki Taniike, Lauren Takahashi

    Read original paper ↗ ·

  35. Productivity Enhancement for the Oxidative Coupling of Methane in Adiabatic Layered-Bed Reactors

    ACS Engineering Au · 2021-08-17

    Laura Pirro, Pedro S.F. Mendes, Joris De Keulenaer, Bart D. Vandegehuchte, Guy B Marin, Joris W. Thybaut

    Read original paper ↗ ·

  36. Materials Informatics for 2D Materials Combined with Sparse Modeling and Chemical Perspective: Toward Small-Data-Driven Chemistry and Materials Science

    Bulletin of the Chemical Society of Japan · 2021-08-27

    Yuya Oaki, Yasuhiko Igarashi

    Read original paper ↗ ·

  37. Size‐Distribution Control of Exfoliated Nanosheets Assisted by Machine Learning: Small‐Data‐Driven Materials Science Using Sparse Modeling

    Advanced Theory and Simulations · 2021-09-06

    Yuri Haraguchi, Yasuhiko Igarashi, Hiroaki Imai, Yuya Oaki

    Read original paper ↗ ·

  38. Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters

    International Journal of Intelligent Systems · 2021-10-01

    Chonghyo Joo, Hyundo Park, Jongkoo Lim, Hyungtae Cho, Junghwan Kim

    Read original paper ↗ ·

  39. Exploring Metal Cluster Catalysts Using Swarm Intelligence: Start with Hydrogen Adsorption

    Topics in Catalysis · 2021-10-01

    Yuta Tsuji, Yuta Yoshioka, Mikiya Hori, Kazunari Yoshizawa

    Read original paper ↗ ·

  40. Waste-derived calcium oxide catalysts for oxidative coupling of methane

    Molecular Catalysis · 2021-10-29

    Dahye Kwon, Inchan Yang, JeongHyun Cho, Ji Chul Jung

    Read original paper ↗ ·

  41. Factors to influence low-temperature performance of supported Mn–Na2WO4 in oxidative coupling of methane

    Molecular Catalysis · 2021-11-01

    Thanh Nhat Nguyen, Kalaivani Seenivasan, Sunao Nakanowatari, Priyank Mohan, Thuy Phuong Nhat Tran, Shun Nishimura, KEISUKE G. TAKAHASHI, Toshiaki Taniike

    Read original paper ↗ ·

  42. Automated synthesis and data accumulation for fast production of high-performance Ni nanocatalysts

    Journal of Industrial and Engineering Chemistry · 2021-11-17

    Kyung Hee Oh, Hack‐Keun Lee, Shin Wook Kang, Jung‐Il Yang, Gyeongjin Nam, Taewaen Lim, Sang‐Ho Lee, Chang Seop Hong, Ji Chan Park

    Read original paper ↗ ·

  43. Engineering Thermally Resistant Catalytic Particles for Oxidative Coupling of Methane Using Spray-Drying and Incorporating SiC

    Industrial & Engineering Chemistry Research · 2021-11-18

    Gontzal Lezcano, Vijay Kumar Velisoju, Shekhar R. Kulkarni, Adrián Ramírez, Pedro Castaño

    Read original paper ↗ ·

  44. Oxidative Coupling of Methane for Ethylene Production: Reviewing Kinetic Modelling Approaches, Thermodynamics and Catalysts

    Processes · 2021-12-06

    Simoní Da Ros, Tahyná Barbalho Fontoura, Marcio Schwaab, Normando Jesus, José Carlos Pinto

    Read original paper ↗ ·

  45. Hydrodynamic Characteristics of an Internal Recycle Berty Catalytic Reactor in Batch/Continuous or Packed/Fluidized Bed Modes

    ACS Engineering Au · 2021-12-15

    Mengmeng Cui, Shekhar R. Kulkarni, Stefan Wagner, Claudia Berger‐Karin, Anton J. Nagy, Pedro Castaño

    Read original paper ↗ ·

  46. Sparse modeling for small data: case studies in controlled synthesis of 2D materials

    Digital Discovery · 2021-12-17

    Yuri Haraguchi, Yasuhiko Igarashi, Hiroaki Imai, Yuya Oaki

    Read original paper ↗ ·

  47. High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La 2 O 3 -based catalysts for the oxidative coupling of methane

    Catalysis Science & Technology · 2022-01-01

    Shun Nishimura, Son Dinh Le, Itsuki Miyazato, Jun Fujima, Toshiaki Taniike, Junya Ohyama, KEISUKE G. TAKAHASHI

    Read original paper ↗ ·

  48. Machine learning for design principles for single atom catalysts towards electrochemical reactions

    Journal of Materials Chemistry A · 2022-01-01

    Mohsen Tamtaji, Hanyu Gao, Md Delowar Hossain, Patrick Ryan Galligan, Hoilun Wong, Zhenjing Liu, Hongwei Liu, Yuting Cai, William A. Goddard, Zhengtang Luo

    Read original paper ↗ ·

  49. Operando Infrared Imaging of Ozone-Assisted Catalysis for High-Throughput Screening of Catalytic Activity

    SSRN Electronic Journal · 2022-01-01

    Ayman A. Abdelaziz, Yoshiyuki Teramoto, Tomohiro Nozaki, Hyun‐Ha Kim

    Read original paper ↗ ·

  50. Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

    Catalysis Science & Technology · 2022-01-01

    Annette Trunschke

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.