Half-Heusler(HH)合金由于其本身具有较为优异的力学性能和高温热稳定性,已成为目前最具有应用前景的中高温热电材料之一。然而,其本身较高的本征晶格热导率阻碍了热电性能的进一步提升。本文以P型Zr Co Sb0.85Sn0.15合金为研究对象,基...Half-Heusler(HH)合金由于其本身具有较为优异的力学性能和高温热稳定性,已成为目前最具有应用前景的中高温热电材料之一。然而,其本身较高的本征晶格热导率阻碍了热电性能的进一步提升。本文以P型Zr Co Sb0.85Sn0.15合金为研究对象,基于同构合金化具有优异P型热电性能的(Nb0.8Ta0.2)0.8Ti0.2Fe Sb,通过磁悬浮熔炼和放电等离子烧结设计并制备出一种(Zr Co Sb0.85Sn0.15)1-x[(Nb0.8Ta0.2)0.8Ti0.2Fe Sb]x(x=0,0.2,0.3,0.4,0.5)高熵HH合金。微观组织分析表明,同构合金化这一策略引入了大量多尺度多衬度的第二相,这将有效增强对声子的散射。其中,当同构合金化含量为0.3时,晶格热导率在923 K时从Zr Co Sb0.85Sn0.15的4.72 W·m-1·K-1降至3.07 W·m-1·K-1,降低了35%。然而,由于多位点合金化元素间存在较为复杂的掺杂效果,使其电导率和塞贝克系数同时降低,最终导致热电优值存在一定的降低。本研究工作表明,高熵合金设计思想是一种降低HH热电合金晶格热导率的有力措施。展开更多
Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Metho...Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.展开更多
文摘Half-Heusler(HH)合金由于其本身具有较为优异的力学性能和高温热稳定性,已成为目前最具有应用前景的中高温热电材料之一。然而,其本身较高的本征晶格热导率阻碍了热电性能的进一步提升。本文以P型Zr Co Sb0.85Sn0.15合金为研究对象,基于同构合金化具有优异P型热电性能的(Nb0.8Ta0.2)0.8Ti0.2Fe Sb,通过磁悬浮熔炼和放电等离子烧结设计并制备出一种(Zr Co Sb0.85Sn0.15)1-x[(Nb0.8Ta0.2)0.8Ti0.2Fe Sb]x(x=0,0.2,0.3,0.4,0.5)高熵HH合金。微观组织分析表明,同构合金化这一策略引入了大量多尺度多衬度的第二相,这将有效增强对声子的散射。其中,当同构合金化含量为0.3时,晶格热导率在923 K时从Zr Co Sb0.85Sn0.15的4.72 W·m-1·K-1降至3.07 W·m-1·K-1,降低了35%。然而,由于多位点合金化元素间存在较为复杂的掺杂效果,使其电导率和塞贝克系数同时降低,最终导致热电优值存在一定的降低。本研究工作表明,高熵合金设计思想是一种降低HH热电合金晶格热导率的有力措施。
文摘Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.
