Hydroisomerization performance of platinum supported on ZSM-22/ZSM-23 intergrowth zeolite catalyst

Hydroisomerization catalysts Pt/ZSM-22, Pt/ZSM-23, and Pt/ZSM-22/ZSM-23 were prepared by supporting Pt on ZSM-22, ZSM-23, and intergrowth zeolite ZSM-22/ZSM-23, respectively. The typical physicochemical properties of these catalysts were characterized by X-Ray Diffraction （XRD）, N2 absorption-desorption, Pyridine-Fourier Transform Infrared （Py-FTIR）, Transmission Electron Microscopy （TEM）, X-Ray Fluorescence （XRF）, Scanning Electron Microscopy （SEM） and NH3- Temperature Programmed Desorption （NH3-TPD）, and the performance of these catalysts in n-dodecane hydroisomerization was evaluated in a continuous down-flow fixed bed with a stainless steel tubular reactor. The characterization results indicated that the intergrowth zeolite ZSM-22/ZSM-23 possessed the dual structure of ZSM-22 and ZSM-23, and the catalyst Pt/ZSM-22/ZSM-23 had similar pores and weak acidity to Pt/ZSM-22 and Pt/ZSM-23 catalysts. Moreover, Pt/ZSM-22/ZSM-23 catalyst showed a high selectivity in hydroisomerization of long chain n-alkanes to mono-branched isomers. The evaluation results for n-dodecane hydroisomerization indicated that the activity of Pt/ZSM-22/ZSM-23 was the lowest, while the hydroisomerization selectivity was the highest among the three catalysts. The maximum yield of i-dodecane product was 68.3% over Pt/ZSM-22/ZSM-23 at 320 ℃.

基于电子病历的预训练模型在急性呼吸道感染识别中的应用

目的评价基于电子病历的预训练模型对急性呼吸道感染(ARI)的识别效果。方法收集重庆大学附属三峡医院2021年12月1至31日就诊于呼吸与危重症科、发热门诊、急诊门诊、儿科、感染科的所有患者的病历资料。使用基于Transformer的双向编码器表征(BERT)预训练模型对病历进行ARI病例识别与判断,以医学专业人员根据ARI病例定义判断的结果作为“金标准”,计算模型识别ARI的灵敏度、特异度、与“金标准”的一致性及受试者工作特征曲线下面积(AUC),评价模型对ARI病例识别的准确性。结果含有3817条电子医疗记录的测试集中,共有1200条经人工判定的ARI病历。模型判定ARI共计1205例,灵敏度为92.67%(1112/1200),特异度96.45%(2524/2617),与“金标准”的一致性Kappa值为0.89,AUC为0.95。模型在男性和女性中识别ARI的准确性相近(AUC分别为0.95和0.94),且对未成年ARI病例识别较18~59岁及≥60岁(AUC分别为0.94,0.89和0.89)更准确。相较于住院患者,目前模型对门诊患者的ARI识别更好,AUC分别为0.74和0.95。结论使用基于电子病历的预训练模型对ARI病例判定具有良好的效果,特别是针对门诊患者及未成年患者。该模型在利用电子病历进行医疗机构ARI病例监测方面表现出良好的潜力。