Fluorescence imaging technology (FI) for high-throughput screening of selenide-modified nano-TiO2 catalysts

Machine learning for mixture toxicity analysis based on high-throughput printing technology

Analysis on mixture toxicity (Mix-tox) of the multi-chemical space is constantly followed with interest for many researchers. Conventional toxicity tests with time-consuming and costly operations make researchers can only establish some toxicity prediction models aiming to a limited sampling dimension. The rapid development of machine learning (ML) algorithm will accelerate the exploration of many fields involving toxicity analysis. Rather than the model calculation capacity, the challenge of this process mainly comes from the lack of toxicology big-data to perform toxicity perception through the ML model. In this paper, a full strategy based a standardized high-throughput experiment was developed for Mix-tox analysis throughout the whole routine, from big-sample dataset design, model building, and training, to the toxicity prediction. Using the concentration variates as input and bio-luminescent inhibition rate as output, it turned out that a well-trained random forest algorithm was successfully applied to assess the mixtures' toxicity effect, suggesting its value in facilitating adoption of Mix-tox analysis.

Fast-developing machine learning support complex system research in environmental chemistry

Machine learning will radically accelerate analysis of complex material networks in environmental chemistry.

Comparison of the photocatalytic performance of TiO2/AC and TiO2/CNT nanocomposites for methyl orange photodegradation

To enhance the photocatalytic degradation efficiency of TiO₂ on methyl orange (MO) removal, TiO₂/AC (activated carbon) and TiO₂/CNT (carbon nanotube) composites were synthesized. The prepared catalysts were characterized by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR). The photocatalytic performance of the obtained composites were investigated by the degradation of MO under UV irradiation (254 nm, 365 nm). The results revealed that the prepared nanocomposite showed higher MO degradation efficiency than pure nano-TiO₂. Additionally, batch experiments of influencing factors, including H₂O₂ dosage, metal dopants, inorganic anions, chloride ion concentration and ultraviolet wavelength on the MO removal efficiency were also conducted. The results demonstrated that metal dopant and the presence of H₂O₂ significantly enhanced MO removal efficiency.