一种高效有机结合态磷肥的多谱学分子形态表征

当前,磷肥不合理施用引发的资源危机和环境问题日益凸显,创制新型磷肥、提高磷肥利用率是保障农业生产、环境安全及可持续发展的关键。鉴于有机结合态磷肥不易被土壤组分固定,在土壤中移动性强,作物有效性高,研发新型有机结合态磷肥有望从根本上突破并解决无机磷肥利用率低的问题。新近,新型有机结合态磷肥的研发方兴未艾,而有机结合态磷肥的赋存形态及其高效机理尚不清楚。基于多种有机结合态磷肥研制及肥效方面的研究成果,选取价格低廉、肥效稳定的淀粉基磷肥,采用液相磷-31核磁共振(P-NMR)技术、同步辐射X射线吸收近边结构谱(XANES)技术在分子层面上对其磷形态进行表征。将供试淀粉基磷肥酶解制成α-极限淀粉糊精,溶解于45%的二甲亚砜,通过液相P-NMR分析发现该磷肥中总磷含量约为5 218 mg·kg-1,与灼烧-比色法测得的结果一致;其中磷主要以有机形态赋存,正磷酸单酯占总磷比例达75.8%。正磷酸二酯含量为17.3%,而无机磷仅占总磷含量的6.9%。另外,通过指纹图谱比对,供试样品磷的K-边XANES谱与植酸磷的谱图极为相似。以上研究结果表明该淀粉基磷肥中磷以正磷酸单酯为主,为深入揭示有机结合态磷肥的高效机制提供了重要依据。     

Dy3+、Tm3+共掺杂Ca2MgSi2O7的发光特性

采用高温固相法合成了系列Ca₂MgSi₂O₇：Dy³⁺,Tm³⁺发光材料。对样品进行了XRD结构表征,测量了激发光谱、发射光谱、色温和荧光寿命。研究结果表明,Ca₂MgSi₂O₇：Tm³⁺在355 nm激发下显示出蓝色发光,在CIE1931中的色坐标为x=0.165 9,y=0.082 2,色纯度为89%。通过Dy³⁺和Tm³⁺的叠加激发谱带激发,即在349,353,365 nm激发下,Ca₂MgSi₂O₇：Dy³⁺,Tm³⁺显示出青白、冷白和暖白光,相关色温值分别为5 193,9 672,4 685 K。300~500 nm区域间可以有效地激发Ca₂MgSi₂O₇：Dy³⁺,Tm³⁺,并在400~600 nm之间产生蓝光和黄光复合产生的白光,表明该体系可用作白光LED的发光材料。     

基于叶绿素荧光光谱和反射光谱的甜瓜种子品种鉴别

甜瓜的品种多样,富含多种营养成分,甜瓜种子品种不纯将对甜瓜生产造成一定危害,研究采用种子的叶绿素荧光光谱结合反射光谱的分析方法鉴别甜瓜种子品种,以甜瓜品种“一特白”、“一特金”、“京蜜7号”、“京蜜11号”、“伊丽莎白”为研究对象。构建了甜瓜种子品种鉴别光谱系统,包括激发光源单元、光谱数据采集单元和数据处理单元,使用该系统获取不同品种甜瓜种子的光谱数据。对光谱数据分别进行一阶导数(first derivative, FD),Savitzky-Golay(SG) 平滑,FD结合SG平滑预处理。采用主成分分析(principal component analysis, PCA)方法降低光谱数据的维数,提取主成分。使用两种不同分组方法将样品按照3∶1的比例分为训练集和验证集,并分别采用Fisher判别和Bayes判别分析方法建立甜瓜种子品种的判别模型。本文比较了仅使用叶绿素荧光光谱与使用叶绿素荧光光谱结合反射光谱建立判别模型的判别结果,结果显示,使用叶绿素荧光光谱结合反射光谱建模的判别结果优于仅使用叶绿素荧光光谱建模的判别结果,Fisher判别分析和Bayes判别分析的验证集样品品种的判别正确率均达到98.0%。研究结果表明,采用叶绿素荧光光谱结合反射光谱鉴别甜瓜种子品种具有可行性。     

基于高光谱的番茄叶片斑潜蝇虫害检测

番茄植株在生长过程中受病虫害的侵染,将导致番茄减产和种植户的经济效益降低,该研究用高光谱技术结合化学计量学方法,实现了番茄叶片斑潜蝇虫害的快速识别。搭建了简易的高光谱成像系统,包括光源单元、高光谱图像采集单元和数据处理单元,用该系统获取番茄叶片的高光谱图像,对高光谱图像进行校准,并从每一幅图像中提取光谱信息。分别采用了光谱角匹配(SAM)分析方法和光谱红边参数判别分析(DA)方法识别番茄叶片斑潜蝇虫害。在SAM分析中,对高光谱数据进行了归一化预处理,以消除多余信息,增加样品之间的差异。比较了以不同番茄叶片样品的反射光谱作为测试光谱时,虫害识别效果的差异,当以受到斑潜蝇侵染的番茄叶片的平均反射光谱作为测试光谱时,虫害识别的正确率较高,达到96.5%。在光谱红边参数判别分析中,从光谱数据中提取了红边位置、红边振幅、最小振幅、红边面积、红谷位置和红边振幅/最小振幅6组红边信息,利用判别分析方法建立番茄叶片斑潜蝇虫害的判别模型,比较了距离判别、Fisher判别、Bayes判别分析方法的判别效果,使用距离判别分析建模的判别正确率最低,判别正确率为88.0%,使用Fisher判别分析建模的效果最佳,判别正确率为96.0%。研究结果表明,采用高光谱技术识别番茄叶片斑潜蝇虫害具有可行性。     

Efficient stochastic Galerkin methods for random diffusion equations

We discuss in this paper efficient solvers for stochastic diffusion equations in random media. We employ generalized polynomial chaos (gPC) expansion to express the solution in a convergent series and obtain a set of deterministic equations for the expansion coefficients by Galerkin projection. Although the resulting system of diffusion equations are coupled, we show that one can construct fast numerical methods to solve them in a decoupled fashion. The methods are based on separation of the diagonal terms and off-diagonal terms in the matrix of the Galerkin system. We examine properties of this matrix and show that the proposed method is unconditionally stable for unsteady problems and convergent for steady problems with a convergent rate independent of discretization parameters. Numerical examples are provided, for both steady and unsteady random diffusions, to support the analysis.     

Discontinuity detection in multivariate space for stochastic simulations

Edge detection has traditionally been associated with detecting physical space jump discontinuities in one dimension, e.g. seismic signals, and two dimensions, e.g. digital images. Hence most of the research on edge detection algorithms is restricted to these contexts. High dimension edge detection can be of significant importance, however. For instance, stochastic variants of classical differential equations not only have variables in space/time dimensions, but additional dimensions are often introduced to the problem by the nature of the random inputs. The stochastic solutions to such problems sometimes contain discontinuities in the corresponding random space and a prior knowledge of jump locations can be very helpful in increasing the accuracy of the final solution. Traditional edge detection methods typically require uniform grid point distribution. They also often involve the computation of gradients and/or Laplacians, which can become very complicated to compute as the number of dimensions increases. The polynomial annihilation edge detection method, on the other hand, is more flexible in terms of its geometric specifications and is furthermore relatively easy to apply. This paper discusses the numerical implementation of the polynomial annihilation edge detection method to high dimensional functions that arise when solving stochastic partial differential equations.     

A generalized polynomial chaos based ensemble Kalman filter with high accuracy

As one of the most adopted sequential data assimilation methods in many areas, especially those involving complex nonlinear dynamics, the ensemble Kalman filter (EnKF) has been under extensive investigation regarding its properties and efficiency. Compared to other variants of the Kalman filter (KF), EnKF is straightforward to implement, as it employs random ensembles to represent solution states. This, however, introduces sampling errors that affect the accuracy of EnKF in a negative manner. Though sampling errors can be easily reduced by using a large number of samples, in practice this is undesirable as each ensemble member is a solution of the system of state equations and can be time consuming to compute for large-scale problems. In this paper we present an efficient EnKF implementation via generalized polynomial chaos (gPC) expansion. The key ingredients of the proposed approach involve (1) solving the system of stochastic state equations via the gPC methodology to gain efficiency; and (2) sampling the gPC approximation of the stochastic solution with an arbitrarily large number of samples, at virtually no additional computational cost, to drastically reduce the sampling errors. The resulting algorithm thus achieves a high accuracy at reduced computational cost, compared to the classical implementations of EnKF. Numerical examples are provided to verify the convergence property and accuracy improvement of the new algorithm. We also prove that for linear systems with Gaussian noise, the first-order gPC Kalman filter method is equivalent to the exact Kalman filter.     

Effect of surface tension on a liquid-jet produced by the collapse of a laser-induced bubble against a rigid boundary

The effect of surface tension on the behavior of a liquid-jet is investigated experimentally by means of a fiber-coupled optical beam deflection (OBD) technique. It is found that a target under water is impacted in turn by a laser-plasma ablation force and by a high-speed liquid-jet impulse induced by bubble collapse in the vicinity of a rigid boundary. The liquid-jet impact is found to be the main damage mechanism in cavitation erosion. Furthermore, the liquid-jet increases monotonously with surface tension, so cavitation erosion rises sharply with increasing surface tension. Surface tension also reduces bubble collapse duration. From the experimental results and the modified Rayleigh theory, the maximum bubble radius is obtained and it is found to reduce with increasing surface tension.     

Observation of the surface circular photogalvanic effect in InN films

A sizable spin-dependent photocurrent related to the interband transition in InN films is observed. The surface charge accumulation layer is suggested to be the origin of the circular photogalvanic current, which is consistent with the result of uniaxial strain experiments and the comparison of front and back incidence. The homogeneous photocurrent demonstrates the existence of spin splitting in the InN surface layer, and the structure inversion asymmetry (SIA)-dominant mechanism indicates a great possibility for the manipulation of spin splitting, which would undoubtedly benefit further research and applications of spintronics.     

金纳米粒子上CdS纳米半导体材料的制备

利用电极表面组装金纳米粒子为模板,通过循环扫描电化学沉积CdS,在电极表面构筑CdS@Au的核-壳纳米结构。结果表明,在实验条件下所得的CdS均属六方相,但CdS的型貌强烈依赖于电化学沉积扫描电位。金纳米粒子表面的CdS包裹层导致其表面等离子体共振红移,这与金属粒子周围介质折射系数的改变以及粒子内部电荷密度分布的变化密切相关。