Measurement and prediction of solubilities and diffusion coefficients of carbon dioxide in starch–water mixtures at elevated pressures

The solubility and diffusion coefficient of carbon dioxide in intermediate‐moisture starch–water mixtures were determined both experimentally and theoretically at elevated pressures up to 16 MPa at 50 °C. A high‐pressure decay sorption system was assembled to measure the equilibrium CO₂ mass uptake by the starch–water system. The experimentally measured solubilities accounted for the estimated swollen volume by Sanchez–Lacombe equation of state (S‐L EOS) were found to increase almost linearly with pressure, yielding 4.0 g CO₂/g starch–water system at 16 MPa. Moreover, CO₂ solubilities above 5 MPa displayed a solubility increase, which was not contributed by the water fraction in the starch–water mixture. The solubilities, however, showed no dependence on the degree of gelatinization (DG) of starch. The diffusion coefficient of CO₂ was found to increase with concentration of dissolved CO₂, which is pressure‐dependent, and decrease with increasing DG in the range of 50–100%. A free‐volume‐based diffusion model proposed by Areerat was employed to predict the CO₂ diffusivity in terms of pressure, temperature, and the concentration of dissolved CO₂. S‐L EOS was once more used to determine the specific free volume of the mixture system. The predicted diffusion coefficients showed to correlate well with the measured values for all starch–water mixtures. © 2005 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 44: 607–621, 2006     

Local chemical distribution and electronic structure of Ge1−xTx DMS single crystals (T=Cr,Mn, Fe)

The spatial concentration distribution and local electronic structure of ferromagnetic Ge_1−xT_x (T=Cr, Mn, Fe) DMS single crystals have been investigated by using scanning photoelectron microscopy (SPEM), X-ray absorption spectroscopy (XAS), and photoemission spectroscopy (PES). It is found that doped T ions in Ge_1−xT_x crystals are chemically phase-separated, suggesting that the observed ferromagnetism arises from the phase-separated T-rich phases in Ge_1−xT_x.     

聚甲基丙烯酸甲酯掺杂Ce3+的飞秒激光三维信息存储

用飞秒激光(200 fs,1 kHz,800 nm)脉冲在掺杂稀土离子Ce3 的聚甲基丙烯酸甲酯(PMMA)膜中进行了光存储实验研究,包括对样品的吸收光谱、激光照射前后的电子旋转共振(Electron spin resonance,ESR)光谱的测量和讨论。结果表明掺杂稀土离子Ce3 的聚甲基丙烯酸甲酯膜具有较低的写入阈值,有利于高速、并行的三维光存储。实验结果采用传统光学显微镜并行读出。给出了四层存储结果(点间距和层间距分别是4μm和16μm),并讨论了脉冲能量的大小对空腔尺寸的影响,进行高密度存储时,在保证读出信号灰度值足够大的情况下,应选择尽量小的激光脉冲写入能量。实验结果表明这种材料可以应用于三维光信息存储。     

具有HfN/HfO2栅结构的p型MOSFET中的负偏置-温度不稳定性研究

研究了HfN/HfO₂高K栅结构p型金属-氧化物-半导体(MOS)晶体管(MOSFET)中，负 偏置-温度应力引起的阈值电压不稳定性(NBTI)特征.HfN/HfO₂高K栅结构的等效 氧化层厚度(EOT)为1.3nm，内含原生缺陷密度较低.研究表明，由于所制备的HfN/HfO₂ 高K栅结构具有低的原生缺陷密度，因此在p-MOSFET器件中观察到的NBTI属HfN/HfO2高K栅结构的本征特征，而非工艺缺陷引起的；进一步研究表明，该HfN/HfO₂高K栅结构中观察到的NBTI与传统的SiO₂基栅介质p-MOSFET器件中观察 到的NBTI具有类似的特征，可以被所谓的反应-扩散(R-D)模型表征： HfN/HfO₂ 栅结构p-MOSFET器件的NBTI效应的起源可以归为衬底注入空穴诱导的界面反应机理，即在负 偏置和温度应力作用下，从Si衬底注入的空穴诱导了Si衬底界面Si-H键断裂这一化学反应的 发生，并由此产生了Si^＋陷阱在Si衬底界面的积累和H原子在介质层内部的扩散 ，这种Si^＋陷阱的界面积累和H原子的扩散导致了器件NBTI效应的发生. 关键词： 高K栅介质 负偏置-温度不稳定性(NBTI) 反应-扩散(R-D)模型     

LF λ-subgroup Generated by LF Subset

In this paper .an important property layer-preserving of LF group and an concept LF λ-subgroup generated by LF subset will be introduced. On the height of LF group theory ,we recognize the concept generated subgroup more clearly.     

激发态卟啉衍生物研究(Ⅶ)——取代基对卟啉—硝基苯化合物发光特性的影响

本文报导共价链相连的X-P-Y“三合板”型化合物在二甲基甲酰胺(DMF)和苯溶液中的发光特性.结果发现:引入取代基X=Cl—,CH3O—和Y=苯、硝基苯对中心卟啉的吸收和荧光光谱均无明显影响,仅其峰值频率将因溶剂不同而有所偏移.此外,由Cl—取代的卟啉—苯的荧光强度总比CH3O—取代时高;若将Y取代基由苯改换为硝基苯时,将使CH3O—P—Y化合物的荧光增强,但在Cl—P—Y化合物中则出现相反的趋势.不论在何种化合物中,在Y取代基中的苯环上引入NO2—基后,均可使其消光系数减小,而且当硝基在邻位取代时,这一效应更为显著.所有这些变化规律均不因溶剂不同而异.如果我们认为X—P—Y化合物的发光强度变化和其中心的卟啉大环上的π电子云密度偏移有规律性的关联,那么上述的一些取代基效应是可以理解的.进而,我们认为:本文所报导的事实将有助于充实巧妙设计高效利用太阳能的分子体系的科学依据.     

A minimum average risk approach to shrinkage estimators of the normal mean

For the problem of estimating the normal mean based on a random sample X ₁,...,X _n when a prior value ₀ is available, a class of shrinkage estimators % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-qqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xHapdbiqaaeGaciGaaiaabeqaamaabaabaaGcbaWaaubeaeqaje% aWbaGaamOBaaWcbeqdbaGafqiVd0MbaKaaaaGccaqGGaGaaiikaiaa% dUgacaGGPaGaeyypa0Jaam4AaiaacIcadaqfqaqabKqaahaacaqGUb% aaleqaneaacaqGubaaaOGaaiykaiaabccadaqfqaqabKqaahaacaWG% UbaaleqaneaaceqGybGbaebaaaGccaqGGaGaey4kaSIaaeiiaiaacI% cacaaIXaGaaeiiaiabgkHiTiaabccacaWGRbGaaiikamaavababeqc% baCaaiaab6gaaSqab0qaaiaabsfaaaGccaGGPaGaaiykamaavababe% qcbaCaaiaad6gaaSqab0qaaiabeY7aTbaaaaa!5615!\[\mathop {\hat \mu }\nolimits_n {\rm{ }}(k) = k(\mathop {\rm{T}}\nolimits_{\rm{n}} ){\rm{ }}\mathop {{\rm{\bar X}}}\nolimits_n {\rm{ }} + {\rm{ }}(1{\rm{ }} - {\rm{ }}k(\mathop {\rm{T}}\nolimits_{\rm{n}} ))\mathop \mu \nolimits_n \] is considered, where % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-qqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xHapdbiqaaeGaciGaaiaabeqaamaabaabaaGcbaWaaubeaeqaje% aWbaGaamOBaaWcbeqcdawaaiaadsfaaaGccaqGGaGaaeypaiaabcca% caWGUbWaaWbaaSqabeaacaaIXaGaai4laiaaikdaaaGccaGGOaWaa0% aaaeaacaWGybaaamaaBaaajeaWbaGaamOBaaWcbeaakiaabccacqGH% sislcaqGGaWaaubeaeqajeaWbaGaaGimaaWcbeqdbaGaaeiVdaaaki% aacMcacaqGGaGaae4laiabeccaGiabeo8aZbaa!4C33!\[\mathop T\nolimits_n {\rm{ = }}n^{1/2} (\overline X _n {\rm{ }} - {\rm{ }}\mathop {\rm{\mu }}\nolimits_0 ){\rm{ /}} \sigma \] and k is a weight function. For certain choices of k, % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-qqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xHapdbiqaaeGaciGaaiaabeqaamaabaabaaGcbaWaaubeaeqaje% aWbaGaamOBaaWcbeqdbaGafqiVd0MbaKaaaaGccaqGGaGaaiikaiaa% dUgacaGGPaaaaa!3CEE!\[\mathop {\hat \mu }\nolimits_n {\rm{ }}(k)\] coincides with previously studied preliminary test and shrinkage estimators. We consider choosing k from a natural non-parametric family of weight functions so as to minimize average risk relative to a specified prior p. We study how, by varying p, the MSE efficiency (relative to \-X) properties of % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-qqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xHapdbiqaaeGaciGaaiaabeqaamaabaabaaGcbaWaaubeaeqaje% aWbaGaamOBaaWcbeqdbaGafqiVd0MbaKaaaaGccaqGGaGaaiikaiaa% dUgacaGGPaaaaa!3CEE!\[\mathop {\hat \mu }\nolimits_n {\rm{ }}(k)\] can be controlled. In the process, a certain robustness property of the usual family of posterior mean estimators, corresponding to the conjugate normal priors, is observed.