In-situ synthesis of TiO2 nanostructures on Ti foil for enhanced and stable photocatalytic performance

TiO_2 nanostructures with strong interfacial adhesion and diverse morphologies have been in-situ grown on Ti foil substrate through a multiple-step method based on conventional plasma electrolytic oxidation(PEO) technology, hydrothermal reaction and ion exchange process. The PEO process is critical to the formation of TiO_2 seeding layer for the nucleation of Na_2Ti_3O_7 and H_2Ti_3O_7 mediates that are strongly attached to the Ti foil. An ion exchange reaction can finally lead to the formation of H_2Ti_3O_7 nanostructures with diverse morphologies and the calcination process can turn the H_2Ti_3O_7 nanostructures into TiO_2 nanostructures with enhanced crystallinity. The morphology of the TiO_2 nanostructures including nanoparticles(NP), nanowhiskers(NWK), nanowires(NW) and nanosheets(NS) can be easily tailored by controlling the NaOH concentration and reaction time during hydrothermal process. The morphology, composition and optical properties of TiO_2 photocatalysts were analyzed using scanning electron microscope(SEM), X-ray diffraction(XRD), photoluminescence(PL) spectroscopy and UV–vis absorption spectrum. Photocatalytic tests indicate that the TiO_2 nanosheets calcined at 500?C show good crystallization and the best capability of decomposing organic pollutants. The decoration of Ag cocatalyst can further improve the photocatalytic performance of the TiO_2 nanosheets as a result of the enhanced charger separation efficiency. Cyclic photocatalytic test using TiO_2 nanostructures grown on Ti foil substrate demonstrates the superior stability in the photodegradation of organic pollutant, suggesting the promising potential of in-situ growth technology for industrial application.     

Erratum to: High-purity helical carbon nanotubes by trace-waterassisted chemical vapor deposition: Large-scale synthesis and growth mechanism

Nano Research - The first author, Fanbin Meng, and the second author, Ying Wang, contributed equally to this work was unfortunately forgotten to write on the first pages of the main text and the...     

Entrapping multifunctional dendritic nanoparticles into a hydrogel for local therapeutic delivery and synergetic immunochemotherapy

Nano Research - Developing multifunctional nanoparticles to support new therapy models is a promising and challenging task to address the current dilemma on antitumor treatment. Herein, we...     

Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.     

Escherichia coli prlC gene encodes a trypsin-like proteinase regulating the cell cycle

BACKGROUND AND PURPOSE: Mood disorders are common after stroke and may impede physical, functional, and cognitive recovery, making early identification and treatment of potential importance. We aimed to compare the accuracy of the General Health Questionnaire (GHQ-30) and the Hospital Anxiety and Depression (HAD) Scale in detecting psychiatric morbidity after stroke and to determine the most suitable cutoff points for different purposes. METHODS: One hundred five hospital-referred stroke patients completed both the GHQ-30 and HAD Scale 6 months after onset before a blinded psychiatric assessment in which the Schedule for Affective Disorders and Schizophrenia with some supplementary questions was used to determine a DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition) diagnosis. Measures were compared in terms of sensitivity, specificity, and receiver operating characteristic curves. RESULTS: No significant differences were found between the GHQ-30 and the HAD Scale in identifying those patients with any DSM-IV diagnosis (P=0.95), grouped depression (P=0.56), or anxiety (P=0.25) disorders. The previously recommended cutoff points for identifying "cases" for the GHQ (4/5) and for the HAD Scale (8/9 and 11/12) were found to be suboptimal in this population. CONCLUSIONS: The GHQ-30 and HAD scale exhibited similar levels of sensitivity and specificity. Data are presented, taking into account the "cost" of false-positives and negatives, to allow a choice of cutoff points suitable for differing situations.     

小波预处理与语音识别

利用小波的带通性质 ,给出了一种新的小波预处理方法 .实验表明 ,经过小波预处理后提取的特征 ,提高了语音的识别率     

二溴对甲基偶氮甲磺光度法测定钢中微量锶

研究了二溴对甲基偶氮甲磺与锶生成2:1兰色络合物进行钢中微量锶的测定.该法λ_(max)=638nm,摩尔吸光系数E_(638)=5.01×10~4,锶量在0.02～0.34μg/L范围内服从比尔定律.方法已用于钢中微量锶的测定,结果满意.     

5种药用植物挥发油对仓库害虫生物活性的研究

研究了香薷、鹤虱等５种药用植物挥发油对仓虫的生物活性作用。结果表明,香薷挥发油的作用最为明显,它（剂量０２μＬ／ｇ）及其粗分离１＃～５＃样品（剂量００５μＬ／ｇ）均有效地抑制了试虫卵、幼虫及成虫的生长发育,从而减少了试虫Ｆ１代种群的发生,其中粗分离１＃样品作用尤为突出。香薷挥发油１＃～５＃样品的主要化学成分为４,５,６,７,８,８ａ－六氢－８ａ－甲基－２［１Ｈ］－奥酮（５２６４％）、百里香酚（３０５％）和一未定成分（２１９２％）。