Research graph
References from Machine learning boosted optimal parameters identification for higher alcohols synthesis over CuZn-based catalysts with experimental validation. Local targets link to admitted publications; unresolved targets remain external evidence.
Mechanistic insights into the synthesis of higher alcohols from syngas on CuCo alloys
10.1021/acscatal.8b01596 · 2018 · External reference
Contemporary pretreatment strategies for bioethanol production from corncobs: a comprehensive review
10.1007/s12649-020-00983-w · 2021 · External reference
Investigation of the role of oxygen vacancies in CuZn catalysts for the formation of higher alcohols from syngas
10.1016/j.fuel.2023.130595 · 2024 · External reference
Alternative distillation configurations for bioethanol purification: simulation, optimization and techno-economic assessment
10.1016/j.cherd.2022.06.036 · 2022 · External reference
New horizon in C1 chemistry: breaking the selectivity limitation in transformation of syngas and hydrogenation of CO2 into hydrocarbon chemicals and fuels
10.1039/c8cs00502h · 2019 · External reference
Ammonium salts modified silica supported Rh-Mn-Li catalyst for CO hydrogenation to C2 oxygenates
10.1007/s11144-020-01885-7 · 2020 · External reference
CO adsorption and CO and O coadsorption on Rh(111) studied by reflection absorption infrared spectroscopy and density functional theory
10.1063/1.2184308 · 2006 · External reference
Selective adsorption of manganese onto rhodium for optimized Mn/Rh/SiO2 alcohol synthesis catalysts
10.1002/cctc.201300479 · 2013 · External reference
Nickel/molybdenum bimetallic alloy for dry reforming of methane: a coverage-dependence microkinetic model simulation based on the first-principles calculation
10.1021/acs.jpcc.1c03417 · 2021 · External reference
Synthesis of higher alcohols from CO2 hydrogenation over Mo-CO-K sulfide-based catalysts
10.1016/j.jtice.2017.04.007 · 2017 · External reference
Insights into the mechanism of ethanol formation from syngas on cu and an expanded prediction of improved cu-based catalyst
10.1016/j.jcat.2013.05.028 · 2013 · External reference
The active site of methanol synthesis over cu/ZnO/Al2O3 industrial catalysts
10.1126/science.1219831 · 2012 · External reference
Promotional effects of cesium promoter on higher alcohol synthesis from syngas over cesium-promoted cu/ZnO/Al2O3 catalysts
10.1021/acscatal.6b00935 · 2016 · External reference
Cesium-induced active sites for C-C coupling and ethanol synthesis from CO2 hydrogenation on cu/ZnO(0001̅) surfaces
10.1021/jacs.1c03940 · 2021 · External reference
The active site of syngas conversion into ethanol over cu/ZnO/Al2O3 ternary catalysts in slurry bed
10.1016/j.jcat.2019.10.002 · 2019 · External reference
Oxygen vacancy control of catalytic activity of cu/ZnO for higher alcohols synthesis via incorporating Ga
10.1021/acssuschemeng.3c03091 · 2023 · External reference
Size-dependent and sensitivity of copper particle in ternary CuZnAl catalyst for syngas to ethanol
10.1016/j.apcatb.2023.122949 · 2023 · External reference
CO hydrogenation to higher alcohols over CuZnAl catalysts without promoters: effect of pH value in catalyst preparation
10.1016/j.fuproc.2017.08.012 · 2017 · External reference
Machine learning-driven high-throughput screening of alloy-based catalysts for selective CO2 hydrogenation to methanol
10.1021/acsami.1c16696 · 2021 · External reference
A data-driven high-throughput workflow applied to promoted in-oxide catalysts for CO2 hydrogenation to methanol
10.1039/d3cy00148b · 2023 · External reference
Machine-learning-guided prediction of cu-based electrocatalysts towards ethylene production in CO2 reduction
2023 · External reference
Designing catalyst descriptors for machine learning in oxidative coupling of methane
10.1021/acscatal.2c03142 · 2022 · External reference
Identifying catalyst property descriptors for CO2 hydrogenation to methanol via big-data analysis
10.1021/acscatal.3c01683 · 2023 · External reference
Methanol synthesis from CO2 /CO mixture on cu–Zn catalysts from microkinetics-guided machine learning pathway search
10.1021/jacs.2c06044 · 2022 · External reference
Identifying descriptors for promoted rhodium-based catalysts for higher alcohol synthesis via machine learning
10.1021/acscatal.2c04349 · 2022 · External reference
Revealing property-performance relationships for efficient CO2 hydrogenation to higher hydrocarbons over Fe-based catalysts: statistical analysis of literature data and its experimental validation
10.1016/j.apcatb.2020.119554 · 2021 · External reference
Advancing C5+ hydrocarbons fuels production: an interpretable machine learning framework for co-catalyzed syngas conversion
10.1016/j.fuel.2023.130658 · 2024 · External reference
Active learning streamlines development of high performance catalysts for higher alcohol synthesis
10.1038/s41467-024-50215-1 · 2024 · External reference
Ensemble learning predicts glass-forming ability under imbalanced datasets
10.1016/j.commatsci.2024.113601 · 2025 · External reference
Unresolved reference
External reference
Unresolved reference
2013 · External reference
Enhancing shale gas EUR predictions with TPE optimized SMOGN: a comparative study of machine learning algorithms in the marcellus shale with an imbalanced dataset
10.1016/j.jgsce.2024.205475 · 2024 · External reference
SMOTE-kTLNN: a hybrid re-sampling method based on SMOTE and a two-layer nearest neighbor classifier
10.1016/j.eswa.2023.121848 · 2024 · External reference
HCAB-SMOTE: a hybrid clustered affinitive borderline SMOTE approach for imbalanced data binary classification
10.1007/s13369-019-04336-1 · 2020 · External reference
RN-SMOTE: reduced noise SMOTE based on DBSCAN for enhancing imbalanced data classification
2022 · External reference
Speed up grid-search for parameter selection of support vector machines
10.1016/j.asoc.2019.03.037 · 2019 · External reference
Applied machine learning to analyze and predict CO2 adsorption behavior of metal-organic frameworks
2023 · External reference
Neural network analysis of selective CO oxidation over copper-based catalysts for knowledge extraction from published data in the literature
10.1021/ie2013955 · 2011 · External reference
A survey of optimization by building and using probabilistic models
2000 · External reference
A machine learning model on simple features for CO2 reduction Electrocatalysts
10.1021/acs.jpcc.0c05964 · 2020 · External reference
A novel machine learning framework for designing high-performance catalysts for production of clean liquid fuels through Fischer-Tropsch synthesis
10.1016/j.energy.2023.130061 · 2024 · External reference
Machine learning-aided catalyst screening and multi-objective optimization for the indirect CO2 hydrogenation to methanol and ethylene glycol process
10.1039/d3gc01865b · 2023 · External reference
Drug discovery with explainable artificial intelligence
10.1038/s42256-020-00236-4 · 2020 · External reference
Hydrogen adsorption on defective nitrogen-doped carbon nanotubes explained via machine learning augmented DFT calculations and game-theoretic feature attributions
10.1021/acs.jpcc.1c03858 · 2021 · External reference
Insight into the structural sensitivity of CuZnAl catalysts for CO hydrogenation to alcohols
10.1016/j.fuel.2022.124265 · 2022 · External reference
Unraveling the roles of cu0 and Cu+ in CuZnAl catalyst for CO hydrogenation to higher alcohols
10.1016/j.ijhydene.2024.07.240 · 2024 · External reference
CuZnAlOOH catalysts with cu0/Cu+ constructed by two-step hydrolysis for ethanol production from syngas
10.1016/j.fuel.2022.124111 · 2022 · External reference
