Viologen-Based Covalent Organic Frameworks toward Metal-Free Highly Efficient Photocatalytic Hydrogen Evolution.
Sinem AltınışıkGizem YanalakImren Hatay PatirSermet KoyuncuPublished in: ACS applied materials & interfaces (2023)
Covalent organic frameworks (COFs) have shown promise in the field of photocatalysts for hydrogen evolution. Many studies have been carried out using various electroactive and photoactive moieties such as triazine, imide, and porphyrin to produce COFs with different geometric structures and units. Electron transfer mediators like viologen and their derivatives can accelerate the transfer of electrons from photosensitizers to active sites. Herein, the combination of a biphenyl-bridged dicarbazole electroactive donor skeleton with a viologen acceptor structure is reported for the photocatalytic hydrogen evolution of novel COF structures with various alkyl linkers {TPCBP X-COF [X = ethyl (E), butyl (B), and hexyl (H)]}. The structures became more flexible and exhibited less crystal behavior as the length of the alkyl chain increased according to scanning and transmission electron microscopy images, X-ray diffraction analyses, and theoretical three-dimensional geometric optimization. In comparison, the H 2 evolution rate of the TPCBP B-COF (12.276 mmol g -1 ) is 2.15 and 2.38 times higher than those of the TPCBP H-COF (5.697 mmol h -1 ) and TPCBP E-COF (5.165 mmol h -1 ), respectively, under visible light illumination for 8 h. The TPCBP B-COF structure is one of the best-performing catalysts for the corresponding photocatalytic hydrogen evolution in the literature, producing 1.029 mmol g -1 h -1 with a high apparent quantum efficiency of 79.69% at 470 nm. Our strategy provides new aspects for the design of novel COFs with respect to future metal-free hydrogen evolution by using solar energy conversion.
Keyphrases
- visible light
- electron microscopy
- highly efficient
- electron transfer
- high resolution
- photodynamic therapy
- ionic liquid
- systematic review
- deep learning
- water soluble
- computed tomography
- molecular dynamics
- magnetic resonance
- machine learning
- convolutional neural network
- gold nanoparticles
- metal organic framework
- solar cells