Efficient and selective oxidation of tertiary benzylic C H bonds with O2 catalyzed by metalloporphyrins under mild and solvent-free conditions

Shen, Hai‐Min; Hu, Mengyun; Liu, Lei; Qi, Bei; Ye, Hong-Liang; She, Yuanbin

doi:10.1016/j.apcata.2020.117599

Cited by 27 publications

(6 citation statements)

References 63 publications

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“…Among them, 16 MpPs structures were selected from Chang et al's study 68 with an E_gap between 1.83-2.21 eV, which is favorable for the photoreduction of CO 2 . 28 In addition, 69 A 4 -type MpPs previously synthesized by our group 69…”

Section: Resultsmentioning

confidence: 99%

“…Among them, 16 MpPs structures were selected from Chang et al 's study 68 with an E_gap between 1.83–2.21 eV, which is favorable for the photoreduction of CO 2 . 28 In addition, 69 A 4 -type MpPs previously synthesized by our group 69 were selected. Furthermore, inspired by the above two types of MpPs, we designed 292 additional A 2 B 2 -type MpPs based on 11 side groups combined with central metals such as Fe, Ni, Cu, Mn, and Co.…”

Section: Resultsmentioning

confidence: 99%

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Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts

Zhang

et al. 2023

Phys. Chem. Chem. Phys.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts

Zhang

et al. 2023

Phys. Chem. Chem. Phys.

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“…MpPs structures were selected from Chang et al's study 52 with an energy gap between 1.72-2.21 eV, which is favorable for the photoreduction of CO2 12 . In addition, 69 A4-type MpPs were selected, which were previously synthesized by our group 53 . Furthermore, inspired by the above two types of MpPs, we designed 292 additional A2B2-type MpPs, which are based on 11 side groups combined with central metals such as Fe, Ni, Cu, Zn, Mn, and Co.…”

Section: Resultsmentioning

confidence: 99%

Data-driven design of porphyrin photocatalysts using deep transfer learning from quantum chemical databases

Zhang

et al. 2022

Preprint

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Metalloporphyrins and Porphyrins (MpPs) have received increasing attention as potential photocatalysts for hydrogen and oxygen evolution and the reduction of carbon dioxide (CO2). Recent studies have found that both HOMO/LUMO energy levels and energy gaps are important factors controlling the performance of MpPs photocatalysts. Since pure quantum chemistry methods consume a large amount of computational resources and cannot learn directly from previously accumulated data, in this study, we proposed a deep transfer learning approach to rapidly predict the HOMO/LUMO energy levels and energy gaps based on two quantum chemistry databases. To complement the open-source Porphyrin-based Dyes Database (PBDD), which contains porphyrin structures designed as photosensitizers, we curated a new database, the Metalloporphyrins and Porphyrins Database (MpPD), in which MpPs were specifically designed as potential photocatalysts. We also adapted two state-of-the-art chemical deep learning models to accommodate more complex systems of MpPs. The PorphyBERT model and PorphyDMPNN model, both pretrained with PBDD and fine-tuned with MpPD, performed satisfactorily in predicting HOMO/LUMO level and energy gap. We concluded the paper by recommending 14 MpPs as potential photocatalysts for CO2 reduction, whose structures have similar value in out-of-sample model prediction and DFT calculations.

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“…The transformation of benzylic compounds into ketones is significant in producing value-added chemicals and intermediates in the chemical industry [1,2]. Traditionally, stoichiometric amounts of oxidants (e.g., Cr 2 O 3 , KMnO 4 , I(III), or Se(IV)) [3,4] and various homogeneous catalysts (e.g., cobalt(II) salts combined with N-hydroxyphthalimide (Co(II)-NHPI), Fe(NO 3 ) 3 , Pd, Pt, Ir, or Ru complexes) have been employed for the oxidation of benzyl compounds [5].…”

Section: Introductionmentioning

confidence: 99%

Boosting Solvent-Free Aerobic Oxidation of Benzylic Compounds into Ketones over Au-Pd Nanoparticles Supported by Porous Carbon

Sun,

Peng,

Guo

et al. 2024

Catalysts

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The exploitation of highly efficient solvent-free catalytic systems for the selective aerobic oxidation of benzylic compounds to produce corresponding ketones with molecular oxygen under mild conditions remains a great challenge in the chemical industry. In this work, Au-Pd nanoparticles supported on porous carbon catalysts were fabricated by the borax-mediated hydrothermal carbonization method and the chemical reduction method. The physicochemical properties of Au-Pd bimetallic samples were examined by XRD, N2 sorption, SEM, TEM, and XPS techniques. The Au-Pd nanoparticles have successfully immobilized on the spherical carbon support with a porous structure and large surface area. A solvent-free catalytic oxidation system was constructed to selectively convert indane into indanone with Au-Pd nanocatalysts and O2. In contrast with a monometallic Au or Pd catalyst, the resulting bimetallic Au-Pd catalyst could effectively activate O2 and exhibit improved catalytic activity in the controlled oxidation of indane into indanone under 1 bar O2. A total of 78% conversion and >99% selectivity toward indanone can be achieved under optimized conditions. The synergistic effect of Au and Pd and porous carbon support contributed to the high catalytic activity for aerobic benzylic compound oxidation. This work offers a promising application prospect of efficient and recyclable Au-Pd nanocatalysts in functional benzylic ketone production.

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Efficient and selective oxidation of tertiary benzylic C H bonds with O2 catalyzed by metalloporphyrins under mild and solvent-free conditions

Cited by 27 publications

References 63 publications

Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts

Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts

Data-driven design of porphyrin photocatalysts using deep transfer learning from quantum chemical databases

Boosting Solvent-Free Aerobic Oxidation of Benzylic Compounds into Ketones over Au-Pd Nanoparticles Supported by Porous Carbon

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