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| 1 | 直流磁控溅射Cr/Cr_2O_3金属陶瓷选择吸收薄膜的研究显示文摘光谱选择吸收薄膜的制备是太阳能集热器高效吸收太阳能的关键技术。本文首先研究了磁控溅射Cr/Cr2O3金属陶瓷选择吸收膜中,氧气流量、溅射靶电流等基本参数对靶电压的影响,然后对不同氧气流量和靶电流条件下制备的Cr/Cr2O3金属陶瓷选择吸收膜的光学常数采用椭偏仪进行了研究,得到了不同工艺条件下的Cr/Cr2O3金属陶瓷薄膜的光学常数,最后经过膜系设计和试验镀制,制备出了室温下吸收率α≥95%、发射率ε≤5%的高性能太阳能选择吸收膜。 | 潘永强 Y.Yin | 2006 | 真空科学与技术学报2006，26，6： | 6 |
| 2 | 大面积应用的RF-PECVD技术研究显示文摘本文探讨采用小电极与大面积基片相对移动的方法来制造大面积薄膜的可行性,提出了采用小电极等离子体源在大面积基片上移动工作的新方法,可用于沉积(或刻蚀)均匀大面积薄膜或根据需求设计的大面积上非均匀膜厚分布的薄膜,从原理上避免大电极带来的不均匀性。介绍了这种方法中由两个电极构成的等离子体增强化学气相沉积(PECVD)系统。分析了当电极移动时,电极与真空室壁相对位置发生变化时对等离子体参数的影响。我们发现当两个射频电极之间的相位差为定值时,等离子体的分布随电极与真空室壁的距离(极地距)变化而变化。当极地距小于80mm时,随极地距的增加,等离子体的悬浮电位和基片的自偏压下降,离子密度变化不明显。当极地距大于80mm时,等离子体的分布呈稳定状态,各参数变化不明显。采用PECVD方法镀制了大面积薄膜厚度呈均匀分布和非均匀分布的两种薄膜,提供了膜厚呈线性渐变和抛物线变化的两种薄膜样片,显示了该方法的灵活性和可行性。 | 杭凌侠 Y.Yin | 2005 | 真空科学与技术学报2005，25，3： | 4 |
| 3 | 非平衡磁控溅射DLC薄膜应力研究显示文摘类金刚石(DLC) 薄膜可用作红外增透保护膜,高的薄膜残余应力造成薄膜附着力下降是目前应用中存在的主要问题之一.本文从DLC薄膜作为红外增透保护膜的需求出发,采用非平衡磁控溅射技术生长DLC薄膜.实测了薄膜的残余应力,分析研究了薄膜残余应力在不同工艺条件下的变化情况.探讨了薄膜残余应力与薄膜厚度、光学透过率、离子能量、沉积速率以及能流密度之间的关系.研究结果表明,薄膜残余应力平衡值在0.9~2.2GPa之间,相应的单面镀膜样片的透过率在4μm波长处为69% ~ 63%,随工艺的不同而变化.工艺优化后薄膜残余应力显著下降.硅基底上薄膜与基底剥离的力的临界值大于2160GPa·nm,最大薄膜厚度≥ 2400nm;锗基底上最大薄膜厚度≥ 2000nm,可以满足整个红外波段的需求. | 杭凌侠 Y.YIN 徐均琪 李建超 | 2005 | 光电工程2005，32，10： | 2 |
| 4 | Rapid screening of 2-[ 18 F ]-fluoro-2-deoxy-D-glucose infusions for volatile organic compound contaminants by solid phase microextraction with gas chromatography—selective ion monitoring mass spectrometry (SPME-GC-SIMMS)显示文摘 | A.B Kanu M Dixon Y.Yin Zu J Bailey J.M Gillies J Zweit C.L.P Thomas | 2003 | Applied Radiation and Isotopes2003，，2： | 1 |
| 5 | One‐Dimensional Nanostructures: Synthesis, Characterization, and Applications显示文摘 | Y.Sun Y.Wu B.Mayers B.Gates Y.Yin F.Kim H.Yan | 2003 | Adv Mater2003，，5： | 1 |
| 6 | Monodispersed Colloidal Spheres: Old Materials with New Applications显示文摘 | Y.Xia B.Gates Y.Yin Y.Lu | 2000 | Adv Mater2000，，10： | 1 |
| 7 | 单纯腭裂并全面性癫伴热性惊厥附加症家系的基因定位及GABRD基因测序研究显示文摘目的对单纯腭裂并全面性癫伴热性惊厥附加症家系进行基因定位,并对候选基因GABRD进行测序研究。方法对家系进行全基因组扫描两点间连锁分析,设计GABRD基因9对外显子-内含子交界处内含子引物,PCR扩增患者及健康对照GABRD基因组序列,测序采用Sanger双脱氧链终止法,PCR产物直接测序,对比分析患者的突变序列及健康对照序列。结果两点连锁分析在1p36区域最大LOD值为1.68。GABRD基因测序显示第4外显子患者的序列分别为T/C和T/T,健康对照的序列分别为T/T和A/C。编码序列分析GGT、GGC及GGA均为甘氨酸。患者GABRD基因mRNA碱基序列为C425T多态性。GABRD第7外显子患者的序列分别为C/T和T/T,健康对照分别为T/T和C/C;异常序列位于mRNA第911碱基处,编码分析AGT及AGC均编码丝氨酸。患者的碱基序列出现了T911C多态性。结论单纯腭裂并全面性癫伴热性惊厥附加症致病基因在1p36区域取得一定的连锁关系,在该家系未发现突变基因;所发现的C425T和T911C单个碱基多态性为其他腭裂及癫家系的连锁分析、关联分析及分子遗传学研究提供了依据。 | 谢晓华 李丽芳 黄希顺 尹景岗 Michael Y.Yin Robyn H.Wallace 郭学鹏 王家勤 | 2010 | 实用儿科临床杂志2010，25，20： | 1 |
| 8 | One‐Dimensional Nanostructures: Synthesis, Characterization, and Applications显示文摘 | Y.Sun Y.Wu B.Mayers B.Gates Y.Yin F.Kim H.Yan | 2003 | Adv. Mater2003，，： | 1 |
| 9 | Prediction of effective diffusivity of porous media using deep learning method based on sample structure information self-amplification显示文摘Effective diffusivity is one of the basic transport coefficients used to describe the mass transport capability of a porous medium.In this study,a deep learning method based on a convolutional neural network(CNN)with sam-ple structure information self-amplification is proposed to predict the effective diffusivity of a porous medium,which is considerably influenced by the morphological and topological parameters of the porous medium.In this method,the geometric structures of three-dimensional(3D)porous media are reproduced via a stochastic reconstruction method.Datasets of the effective diffusivities of the reconstructed porous media were first estab-lished by the pore-scale lattice Boltzmann method(LBM)simulation.A large number of geometric structures of 3D porous media are obtained using the proposed sample structure information self-amplification approach.The 3D geometric structure information and corresponding effective diffusivities are directionally applied to a CNN for training and prediction.The effective diffusivities of media with porosities ranging from 0.48 to 0.58 are employed as training datasets,and the effective diffusivities of media with a broader porosity range of 0.39 to 0.79 are predicted by CNN.The CNN model can achieve a fast and accurate prediction of the effective diffusivity.The relative error between the CNN and LBM is 0.026%–8.95%with porosities ranging from 0.39 to 0.79.For a typical case with a porosity of 0.5,the computation time required by the CNN model is only 3×10^(−4) h,while the computation time for the same case is 16.96 h using the LBM.These findings indicate that the proposed deep learning method has a powerful learning ability;it is time-saving,provides accurate predic-tions,and can serve as a promising and powerful tool to predict the transport coefficients of complex porous media. | H.Wang Y.Yin X.Y.Hui J.Q.Bai Z.G.Qu | 2020 | Energy and AI2020，2，2： | 0 |
| 10 | 射频等离子系统中微弧放电的解析模型显示文摘最近我们在实验中发现:射频等离子体系统中的高电位可以引发微弧放电,这种微弧放电现象不是发生在射频系统的输入极上而是发生在接地极上。这种相对于电极呈非对称分布的微弧放电现象不是我们期待的结果,也不能用现有的理论来解释它。本文在Ch ild-L angm u ir鞘层模型理论和电流连续性理论的基础上,推导建立了一个用于表述射频等离子体系统中的这种非对称型微弧放电现象的解析模型。从我们的推导过程可以发现鞘层内外的电位差取决于接地极鞘层面积与输入极鞘层面积之比。当接地极的鞘层面积大于输入极鞘层面积时,接地极鞘层电位差的最小值成为高电位,从而引发电弧放电。而输入极鞘层电位差的最小值不发生变化。这个模型和推导结果与实验现象和P IC仿真数据有较好的一致性。 | Y.Yin 杭凌侠 | 2006 | 真空2006，43，3： | 0 |
| 11 | Resistive field generation in intense proton beam interaction with solid targets显示文摘The Brown-Preston-Singleton(BPS)stopping power model is added to our previously developed hybrid code to model ion beam-plasma interaction.Hybrid simulations show that both resistive field and ion scattering effects are important for proton beam transport in a solid target,in which they compete with each other.When the target is not completely ionized,the self-generated resistive field effect dominates over the ion scattering effect.However,when the target is completely ionized,this situation is reversed.Moreover,it is found that Ohmic heating is important for higher current densities and materials with high resistivity.The energy fraction deposited as Ohmic heating can be as high as 20%-30%.Typical ion divergences with half-angles of about 5°-10°will modify the proton energy deposition substantially and should be taken into account. | W.Q.Wang J.J.Honrubia Y.Yin X.H.Yang F.Q.Shao | 2024 | Matter and Radiation at Extremes2024，9，1： | 0 |
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