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In this paper, N, Fe-codoped carbon dots (N, Fe-CDs) were synthesized from β-cyclodextrin, ethylenediamine, and ferric chloride for the first time using a convenient one-step hydrothermal method. The obtained N, Fe-CDs were characterized by various methods including transmission electron microscopy, X-ray photoelectron spectroscopy, and Fourier-transform infrared spectroscopy. The N, Fe-CDs exhibited better catalytic activity than horseradish peroxidase (HRP) and caused an evident color change for 3,3′,5,5′-tetramethylbenzidine in the presence of H2O2. Kinetic experiments show that the apparent Km value for the N, Fe-CDs with TMB (0.40 mM) or H2O2 (0.35 mM) as the substrate was lower than that of HRP (0.43 and 3.70 mM), suggesting that the N, Fe-CDs have a much higher affinity for TMB and H2O2 than HRP. The Km/Vmax value for the N, Fe-CDs (21.74×103·s for H2O2) is significantly lower than that for HRP (42.53×103·s), suggesting that the N, Fe-CDs have a stronger catalytic efficiency for H2O2 than HRP. Furthermore, a highly efficient and sensitive colorimetric detection method for glucose was developed using the N, Fe-CDs as mimic peroxidase to detect the hydrogen peroxide generated by the oxidation of glucose by glucose oxidase. The limit of detection for H2O2 and glucose was found to be 0.52 and 3.0 μM, respectively. The obtained N, Fe-codoped carbon dots, which possess simulated peroxidase activity, can potentially be used in the field of biotechnology.

Ti-doped functionalized carbon nitride nanoparticles and hybrid TiO2/graphitic-C3N4 were prepared stepwise and applied to the detection of free residual chlorine and visible-light photocatalysis.

Translation software has become an important tool for communication between different languages. People's requirements for translation are higher and higher, mainly reflected in people's desire for barrier free cultural exchange. With a large corpus, the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units. Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation, ignoring context. To support the ongoing improvement of translation methods built upon deep learning, we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation. During translation, our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence. The collection of all of the generated sentences forms a topic sequence. Using probabilities and article sequences determined from the training set, our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation. This algorithm improves the accuracy of translation, avoids the combination of invalid words, and enhances the readability and meaning of the resulting translation.

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