Fast parallel algorithm for three-dimensional distance-driven model in iterative computed tomography reconstruction

The projection matrix model is used to describe the physical relationship between reconstructed object and projection.Such a model has a strong influence on projection and backprojection,two vital operations in iterative computed tomographic reconstruction.The distance-driven model(DDM) is a state-of-the-art technology that simulates forward and back projections.This model has a low computational complexity and a relatively high spatial resolution;however,it includes only a few methods in a parallel operation with a matched model scheme.This study introduces a fast and parallelizable algorithm to improve the traditional DDM for computing the parallel projection and backprojection operations.Our proposed model has been implemented on a GPU(graphic processing unit) platform and has achieved satisfactory computational efficiency with no approximation.The runtime for the projection and backprojection operations with our model is approximately 4.5 s and 10.5 s per loop,respectively,with an image size of 256×256×256 and 360 projections with a size of 512×512.We compare several general algorithms that have been proposed for maximizing GPU efficiency by using the unmatched projection/backprojection models in a parallel computation.The imaging resolution is not sacrificed and remains accurate during computed tomographic reconstruction.     

Evidence for ion–ion interactions between peptides and anions (HSO4− or ClO4−) derived from high‐acidity acids

The existence of gas‐phase electrostatic ion–ion interactions between protonated sites on peptides ([Glu] Fibrinopeptide B, Angiotensin I and [Asn¹, Val⁵]‐Angiotensin II) and attaching anions (ClO₄^? and HSO₄^?) derived from strong inorganic acids has been confirmed by CID MS/MS. Evidence for ion–ion interactions comes especially from the product ions formed during the first dissociation step, where, in addition to the expected loss of the anion or neutral acid, other product ions are also observed that require covalent bond cleavage (i.e. H₂O loss when several carboxylate groups are present, or NH₃ loss when only one carboxylate group is present). For [[Glu] Fibrinopeptide B + HSO₄]^?, under CID, H₂O water loss was found to require less energy than H₂SO₄ departure. This indicates that the interaction between HSO₄^? and the peptide is stronger than the covalent bond holding the hydroxyl group, and must be an ion–ion interaction. The strength and stability of this type of ion‐pairing interaction are highly dependent on the accessibility of additional mobile charges to the site. Positive mobile charges such as protons from the peptide can be transferred to the attaching anion to possibly form a neutral that may depart from the complex. Alternatively, an ion–ion interaction can be disrupted by a competing proximal additional negatively charged site of the peptide that can potentially form a salt bridge with the positively charged site and thereby facilitate the attaching anion's departure. Copyright © 2014 John Wiley & Sons, Ltd.     

New Concepts for Designing d10‐M(L)n Catalysts: d Regime,s Regime and Intrinsic Bite‐Angle Flexibility

Our aim is to understand the electronic and steric factors that determine the activity and selectivity of transition‐metal catalysts for cross‐coupling reactions. To this end, we have used the activation strain model to quantum‐chemically analyze the activity of catalyst complexes d¹⁰‐M(L)_n toward methane C?H oxidative addition. We studied the effect of varying the metal center M along the nine d¹⁰ metal centers of Groups 9, 10, and 11 (M=Co^?, Rh^?, Ir^?, Ni, Pd, Pt, Cu⁺, Ag⁺, Au⁺), and, for completeness, included variation from uncoordinated to mono‐ to bisligated systems (n=0, 1, 2), for the ligands L=NH₃, PH₃, and CO. Three concepts emerge from our activation strain analyses: 1) bite‐angle flexibility, 2) d‐regime catalysts, and 3) s‐regime catalysts. These concepts reveal new ways of tuning a catalyst’s activity. Interestingly, the flexibility of a catalyst complex, that is, its ability to adopt a bent L‐M‐L geometry, is shown to be decisive for its activity, not the bite angle as such. Furthermore, the effect of ligands on the catalyst’s activity is totally different, sometimes even opposite, depending on the electronic regime (d or s) of the d¹⁰‐M(L)_n complex. Our findings therefore constitute new tools for a more rational design of catalysts.     

扩散模型和凝聚模型耦合作用下胶体凝聚动力学的Monte Carlo模拟研究

以扩散模型(Ds(γ)=D0×sγ)和凝聚模型(Pij(σ)=P0×(i×j)σ)为基础,对胶体体系随时间的演变、团簇大小分布及其标度关系、团簇的重均大小S(t)的变化规律以及模型对最终分形维数的影响四个角度进行了比较研究,发现扩散指数γ0和凝聚概率指数σ0对胶体的凝聚动力学过程有相似的影响.本文在较宽的γ和σ取值范围内,对胶体的凝聚动力学进行了模拟研究,对慢速凝聚向快速凝聚的转化机理作了定量分析,并进一步分析了在团簇-团簇凝聚(CCA)模型下,得到类似扩散置限凝聚(DLA)模型的凝聚体的物理意义,结果表明:(1)γ0代表了体系中团簇或单粒做"定向运动"而非无规则的布朗运动的情况.这种"定向运动"的推动力可能来自于大团簇产生的强"长程范德华力"、"电场力"等,或来自于体系边界处的外力场的作用.(2)当σ0时,体系成为先快后慢的慢速凝聚,这可能对应大团簇为一排斥中心,即胶体颗粒存在"排斥力场"的现象.(3)证实了团簇的重均大小在凝聚过程的早期按指数规律增长,而后期按幂函数规律增长的实验现象.模拟研究还表明,胶体体系的凝聚动力学过程,在σ0时是一个存在正反馈机制的非线性动力学过程,而在σ0时则体现出负反馈的特征.     

Langmuir Analysis of Nanoparticle Polyvalency in DNA‐Mediated Adsorption

Many nanoparticle adsorption processes are dictated by the collective interactions of surface‐bound ligands. These adsorption processes define how nanoparticles interact with biological systems and enable the assembly of nanoparticle‐based materials and devices. Herein, we present an approach for quantifying nanoparticle adsorption thermodynamics in a manner that satisfies the assumptions of the Langmuir model. Using this approach, we study the DNA‐mediated adsorption of polyvalent anisotropic nanoparticles on surfaces and explore how deviations from model assumptions influence adsorption thermodynamics. Importantly, when combined with a solution‐based van’t Hoff analysis, we find that polyvalency plays a more important role as the individual interactions become weaker. Furthermore, we find that the free energy of anisotropic nanoparticle adsorption is consistent across multiple shapes and sizes of nanoparticles based on the surface area of the interacting facet.     

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Radical C−H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C−H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out-of-sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism-based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.     

A Pressure-Induced Inverse Order–Disorder Transition in Double Perovskites

Given the consensus that pressure improves cation ordering in most of known materials, a discovery of pressure-induced disordering could require recognition of an order–disorder transition in solid-state physics/chemistry and geophysics. Double perovskites Y₂CoIrO₆ and Y₂CoRuO₆ polymorphs synthesized at 0, 6, and 15 GPa show B-site ordering, partial ordering, and disordering, respectively, accompanied by lattice compression and crystal structure alteration from monoclinic to orthorhombic symmetry. Correspondingly, the long-range ferrimagnetic ordering in the B-site ordered samples are gradually overwhelmed by B-site disorder. Theoretical calculations suggest that unusual unit-cell compressions under external pressures unexpectedly stabilize the disordered phases of Y₂CoIrO₆ and Y₂CoRuO₆.     

A refined numerical model for determining inflation-burst behavior of composite membrane structures

The airship structures made of multi-layer composite fabrics or membranes can offer the platform for earth observations, wireless communications and space research due to light weight and good mechanical performance. The structural safety and serviceability strongly depend on material properties and working conditions. Available studies are limited within service stress limits or are lack of suitable biaxial tensile constitutive models for understanding structural behavior. This paper thus focuses on a refined numerical model for determining inflation-burst behavior of composite airship structures considering new biaxial constitutive equations, novel failure criteria and manufacture factors.The differences between ideal and real forms of airship structures, e.g. volume difference, demonstrate the necessity for incorporating cutting-pattern effects in the initial numerical model. For structural analysis, stress distributions on real structural forms are different from those on ideal forms because of welding parts that can enhance local stiffness. The ultimate pressures are 56.7 kPa and 59.5 kPa for ideal and real structural forms. Structural breaking initiated at the maximum diameter of ideal structural forms propagates fast while welding parts can prevent breaking propagation for real structural forms. Therefore, the refined numerical model can reveal basic structural behavior and safety performance of airship structures in the inflation-burst processes.