Negative inductive effect enhances charge transfer driving in sulfonic acid functionalized graphitic carbon nitride with efficient visible-light photocatalytic performance

3-Hydroxythiophenol-Formaldehyde Resin Microspheres Modulated by Sulfhydryl Groups for Highly Efficient Photocatalytic Synthesis of H2O2

Resorcinol-formaldehyde (RF) resin represents a promising visible-light responding photocatalyst for oxygen reduction reaction (ORR) toward H2O2 production. However, its photocatalytic ORR activity toward H2O2 generation is still unsatisfied for practical application. Herein, 3-hydroxythiophenol-formaldehyde (3-HTPF) resin microspheres synthesized through polycondensation reaction between 3-HTP and formaldehyde at room temperature and subsequent hydrothermal treatment exhibit enhanced photocatalytic ORR activity is reported. The experimental results show that the partial substitution of hydroxy group (─OH) by sulfhydryl one (─SH) through using 3-HTP to replace resorcinol could slow the rates of nucleation and growth of the resin particles and lead to strongly π-stacked architecture in 3-HTPF. The introduction of ─SH group can also improve adsorption ability of 3-HTPF to O2 molecules and enhance ORR catalytic activity of the photocatalysts. Stronger built-in electric field, better adsorption ability to O2 molecules, and increased surface catalytic activity collectively boost photocatalytic activity of 3-HTPF microspheres. As a result, H2O2 production rate of 2010 µm h-1 is achieved over 3-HTPF microspheres at 273 K, which is 3.4 times larger than that obtained using RF submicrospheres (591 µm h-1). The rational substituent group modulation provides a new strategy for designing polymeric photocatalysts at the molecular level toward high-efficiency artificial photosynthesis.

Explainable Graph Neural Networks with Data Augmentation for Predicting pKa of C-H Acids

The pKa of C-H acids is an important parameter in the fields of organic synthesis, drug discovery, and materials science. However, the prediction of pKa is still a great challenge due to the limit of experimental data and the lack of chemical insight. Here, a new model for predicting the pKa values of C-H acids is proposed on the basis of graph neural networks (GNNs) and data augmentation. A message passing unit (MPU) was used to extract the topological and target-related information from the molecular graph data, and a readout layer was utilized to retrieve the information on the ionization site C atom. The retrieved information then was adopted to predict pKa by a fully connected network. Furthermore, to increase the diversity of the training data, a knowledge-infused data augmentation technique was established by replacing the H atoms in a molecule with substituents exhibiting different electronic effects. The MPU was pretrained with the augmented data. The efficacy of data augmentation was confirmed by visualizing the distribution of compounds with different substituents and by classifying compounds. The explainability of the model was studied by examining the change of pKa values when a specific atom was masked. This explainability was used to identify the key substituents for pKa. The model was evaluated on two data sets from the iBonD database. Dataset1 includes the experimental pKa values of C-H acids measured in DMSO, while dataset2 comprises the pKa values measured in water. The results show that the knowledge-infused data augmentation technique greatly improves the predictive accuracy of the model, especially when the number of samples is small.

Organic Molecule Bifunctionalized Polymeric Carbon Nitride for Enhanced Photocatalytic Hydrogen Peroxide Production

Modifying the polymeric carbon nitride (CN) with organic molecules is a promising strategy to enhance the photocatalytic activity. However, most previously reported works show that interchain embedding and edge grafting of the organic molecule can hardly be achieved simultaneously. Herein, we successfully synthesized organic molecule bifunctionalized CN (MBCN) through copolymerization of melon and sulfanilamide at a purposely elevated temperature of 550 °C. In MBCN, the edge grafted and interchain embedded benzene rings act as the electron-donating group and charge-transfer channel, respectively, rendering efficient photocatalytic H2 O2 production. The optimal MBCN exhibits a significantly improved non-sacrificial photocatalytic H2 O2 generation rate (54.0 μmol g-1  h-1 ) from pure water, which is 10.4 times that of pristine CN. Experimental and density functional theory (DFT) calculation results reveal that the enhanced H2 O2 production activity of MBCN is mainly attributed to the improved photogenerated charge separation/transfer and decreased formation energy barrier (▵G) from O2- to the intermediate 1,4-endoperoxide (⋅OOH). This work suggests that simultaneous formation of electron donating group and charge transfer channel via organic molecule bifunctionalization is a feasible strategy for boosting the photocatalytic activity of CN.

Vacancy-Modified Porous g-C3N4 Nanosheets Controlled by Physical Activation for Highly Efficient Visible-Light-Driven Hydrogen Evolution and Organics Degradation

As a promising photocatalyst material, g-C3N4 has great application potential in energy production and environmental improvement. In this work, surface-modified g-C3N4 nanosheets with excellent stability and high photocatalytic activity were successfully synthesized by physical steam activation. The charge transfer rate of carbon nitride was improved due to the synergistic effect of nitrogen defect and oxygen doping caused by steam activation. Meanwhile, the specific surface area and pore volume of the optimized sample reached 124.3 m2 g-1 and 0.42 cm3 g-1, respectively, which increased the exposed reaction sites of reactants, enhancing the photocatalytic activity of g-C3N4. In addition, this novel g-C3N4 displayed a great H2 evolution rate of 5889.39 μmol h-1 g-1 with a methylene blue degradation rate up to 6.52 × 10-3 min-1, which was 3.7 and 2.1 times of original g-C3N4, respectively. This study provided a simple and economical method to develop a highly efficient g-C3N4 photocatalyst for solar energy conversion.

Preparation of Structure Vacancy Defect Modified Diatomic-Layered g-C3 N4 Nanosheet with Enhanced Photocatalytic Performance

Structure self-modification of graphitic carbon nitride (g-C3 N4 ) without the assistance of other species has attracted considerable attention. In this study, the structure vacancy defect modified diatomic-layered g-C3 N4 nanosheet (VCN) is synthesized by thermal treatment of bulk g-C3 N4 in a quartz tube with vacuum atmosphere that will generate a pressure-thermal dual driving force to boost the exfoliation and formation of structure vacancy for g-C3 N4 . The as-prepared VCN possesses a large specific surface area with a rich pore structure to provide more active centers for catalytic reactions. Furthermore, the as-formed special defect level in VCN sample can generate a higher exciton density at photoexcitation stage. Meanwhile, the photogenerated charges will rapidly transfer to VCN surface due to the greatly shortened transfer path resulting from the ultrathin structure (≈1.5 nm), which corresponds to two graphite carbon nitride atomic layers. In addition, the defect level alleviates the drawback of enlarged bandgap caused by the quantum size effect of nano-scaled g-C3 N4 , resulting in a well visible-light utilization. As a result, the VCN sample exhibits an excellent photocatalytic performance both in hydrogen production and photodegradation of typical antibiotics.

One-step thermal polymerization synthesis of nitrogen-rich g-C3N4 nanosheets enhances photocatalytic redox activity

Graphitic carbon nitride (g-C₃N₄) has attracted enormous attention as a visible-light-responsive carbon-based semiconductor photocatalyst. However, fast charge recombination seriously limits its application. Therefore, it is urgent to modify the electronic structure of g-C₃N₄ to obtain excellent photocatalytic activity. Herein, we reported a one-step thermal polymerization synthesis of nitrogen-rich g-C₃N₄ nanosheets. Benefiting from the N self-doping and the ultrathin structure, the optimal CN-70 exhibits its excellent performance. A 6.7 times increased degradation rate of rhodamine B (K = 0.06274 min^-1), furthermore, the hydrogen evolution efficiency also reached 2326.24 μmol h^-1 g^-1 (λ > 420 nm). Based on a series of characterizations and DFT calculations, we demonstrated that the N self-doping g-C₃N₄ can significantly introduce midgap states between the valence band and conduction band, which is more conducive to the efficient separation of photogenerated carriers. Our work provides a facile and efficient method for self-atom doping into g-C₃N₄, providing a new pathway for efficient photocatalysts.

Alkyl group-decorated g-C3N4 for enhanced gas-phase CO2 photoreduction

With excellent physical/chemical stability and feasible synthesis, g-C₃N₄ has attracted much attention in the field of photocatalysis. However, its weak photoactivity limits its practical applications. Herein, by easily planting hydrophobic alkyl groups onto g-C₃N₄, the hydrophilicity of g-C₃N₄ can be well regulated and its specific surface area be enlarged simultaneously. Such a modification ensures enhanced CO₂ capture and increased active sites. In addition, the introduction of alkyl groups endows g-C₃N₄ with abundant charge density and efficient separation of photoinduced excitons. All these advantages synergistically contribute to the enhanced photocatalytic CO₂ reduction performance over the optimized catalyst (DCN90), and the total CO₂ conversion is 7.4-fold that of pristine g-C₃N₄ (CN).