Subnano-Fe (Co, Ni) clusters anchored on halloysite nanotubes: an efficient Fenton-like catalyst for the degradation of tetracycline.

Iron-based catalysts are environmentally friendly, and iron minerals are abundant in the earth's crust, with great potential advantages for PMS-based advanced oxidation process applications. However, homogeneous Fe2+/PMS systems suffer from side reactions and are challenging to reuse. Therefore, developing catalysts with improved stability and activity is a long-term goal for practical Fe-based catalyst applications. In this study, we prepared Fe-HNTs nanoreactors by encapsulating a nitrogen-doped carbon layer with one-dimensional halloysite nanotubes (HNTs) using the molten salt-assisted method. Subsequently, Fe (Co, Ni) nanoclusters were anchored onto the nitrogen-doped carbon layer at a relatively low temperature (550℃), resulting in stable and uniform distribution of metal nanoclusters on the surface of HNTs carriers in the form of Fe-Nx coordination. The results showed that the dissolution of the molten salt and leaching of post-treated metal oxides generated numerous mesopores within the Fe-HNTs nanoreactor, leading to a specific surface area more than 10 times that of HNTs. This enhanced mass transfer capability facilitates rapid pollutant removal while exposing more active sites. Remarkably, Fe-HNTs adsorbed up to 97% of tetracycline within 60 min. In the Fe-HNTs/PMS system, the predominant reactive oxygen species has been shown to be 1O2, and the added tetracycline was degraded by more than 98% within 5 min. The removal of tetracycline was maintained above 96% in the presence of interfering factors such as wide pH (3-11) and inorganic anions (5 mM Cl-, HCO3-, NO3-, and SO42-). The investigated mechanism suggests that efficient degradation and interference resistance of the Fe-HNTs/PMS system is attributed to the synergistic effect between the rapid adsorption of porous structure and the non-radical (1O2)-dominated degradation pathway.

ChemReco: automated recognition of hand-drawn carbon-hydrogen-oxygen structures using deep learning.

Chemical molecular structures are a direct and convenient means of expressing chemical knowledge, playing a vital role in academic communication. In chemistry, hand drawing is a common task for students and researchers. If we can convert hand-drawn chemical molecular structures into machine-readable formats, like SMILES encoding, computers can efficiently process and analyze these structures, significantly enhancing the efficiency of chemical research. Furthermore, with the progress of educational technology, automated grading is gaining popularity. When machines automatically recognize chemical molecular structures and assess the correctness of the drawings, it offers great convenience to teachers. We created ChemReco, a tool designed to identify chemical molecular structures involving three atoms: C, H, and O, providing convenience for chemical researchers. Currently, there are limited studies on hand-drawn chemical molecular structures. Therefore, the primary focus of this paper is constructing datasets. We propose a synthetic image method to rapidly generate images resembling hand-drawn chemical molecular structures, enhancing dataset acquisition efficiency. Regarding model selection, the hand-drawn chemical molecule structural recognition model developed in this article achieves a final recognition accuracy of 96.90%. This model employs the encoder-decoder architecture of EfficientNet + Transformer, demonstrating superior performance compared to other encoder-decoder combinations.