基于单光子计数激光雷达的Bayesian检测研究

在单光子计数激光雷达检测领域,目前的检测方法在低信噪比情况下虚警概率会增加,同时也无法适应噪声变化的问题。针对这些问题,提出了一种基于Bayesian的检测方法,该方法首先通过雷达方程估计回波信号光子数的范围,将其作为先验信息,而后结合二项分布建立了累计概率模型,基于Bayesian判决准则计算得到检测阈值,此阈值能够在检测概率与虚警概率中间择其平衡。这种方法不仅克服了低信噪比检测困难的情况,还减少了先验信息的获取难度。实验结果表明,对比固定阈值其虚警概率降低了10倍。对比“恒虚警”其检测概率提高了约20。验证了方法具有良好的检测效果,具备一定的可操作性。     

磁性硅铁载铜吸附Hg0动力学机制及密度泛函研究

采用粉末冶金法将纳米单质铜(Cu⁰)、硅铁(FeSi)、四氧化三铁硅涂层(Fe₃O₄@SiO₂)混合煅烧并制备出新型磁性硅铁载铜吸附剂MagFeSi-Cu⁰。实验研究不同烟气温度下MagFeSi-Cu⁰的汞吸附能力基础上,结合颗粒内扩散模型、准二阶动力学模型、Elovich模型及Bangham模型分析了MagFeSi-Cu⁰吸附Hg⁰过程的主要控制步骤。在此基础上,依据密度泛函理论(DFT)研究了不同反应温度下FeSi表面Cu⁰原子与Hg⁰原子的汞齐作用机制。研究结果表明,Bangham模型的拟合值与MagFeSi-Cu⁰汞吸附实验值拟合度最高,MagFeSi-Cu⁰表面痕量Hg⁰吸附由汞的外扩散和表面铜汞齐吸附共同控制;通过密度泛函计算,发现Cu⁰颗粒表面Cu-Hg齐吸附能为-0.534 eV,当烟气温度从80℃上升至200℃时,Hg⁰原子与单质Cu原子的吸附自由能从-22.47 kJ/mol下降至-13.96 kJ/mol,这些结果为深入了解Hg⁰在Cu(111)表面的反应机理提供了理论基础。     

Images of a First‐Order Spin‐Reorientation Phase Transition in a Metallic Kagome Ferromagnet

First‐order phase transitions, where one phase replaces another by virtue of a simple crossing of free energies, are best known between solids, liquids, and vapors, but they also occur in a wide range of other contexts, including even elemental magnets. The key challenges are to establish whether a phase transition is indeed first order, and then to determine how the new phase emerges because this will determine thermodynamic and electronic properties. Here it is shown that both challenges are met for the spin reorientation transition in the topological metallic ferromagnet Fe₃Sn₂. The magnetometry and variable temperature magnetic force microscopy experiments reveal that, analogous to the liquid–gas transition in the temperature–pressure plane, this transition is centered on a first‐order line terminating in a critical end point in the field‐temperature plane. The nucleation and growth associated with the transition is directly imaged, indicating that the new phase emerges at the most convoluted magnetic domain walls for the high temperature phase and then moves to self‐organize at the domain centers of the high temperature phase. The dense domain patterns and phase coexistence imply a complex inhomogenous electronic structure, which can yield anomalous contributions to the electrical conductivity.     

Mathematical formulation and heuristic algorithms for optimisation of auto-part milk-run logistics network considering forward and reverse flow of pallets

The operational planning of distribution network for automotive industry is complex with many conditions to consider, including heterogeneous fleet, enforcing the feasibility of 3D-packing of pallets into vehicles to address the vehicle's capacity in terms of weight and volume, compatibility of orders in a vehicle, returning empty pallets from assembly-plants backwards to suppliers, and delivery time windows. A mathematical model (MILP) is proposed that takes account of these conditions to minimise total transportation costs. The network structure can be a combination of direct shipment and milk-run for both forward and reverse flow of pallets. The model is solved optimally for small-size problems. For solving larger problems, a heuristic algorithm (in two versions) is proposed that uses a similarity measure to generate a reasonable list of orders. Best/first-fit strategies are employed to generate a feasible solution with the aid of a relaxed version of the proposed MILP. Improvement heuristics are also designed. Unlike most of existing constructive heuristics, our aim for developing the heuristic approach is to force routing decision, with all of its considerations, being made optimal. We also use the proposed best-fit strategy in the body of grouping evolution strategy (GES) algorithm to attain an effective meta-heuristic approach. The effectiveness of heuristics is tested on generated instances which demonstrates they are optimal for small-size problems. They are also tested on the data of daily auto-parts shipments gathered from the largest Iranian automobile company. Results demonstrate there exists a significant potential for cost saving through milk-run strategy compared with the direct shipping strategy.     

Study of physicochemical properties of flutamide-loaded Ocimum basilicum microspheres with ex vivo mucoadhesion and in vitro drug release

Drug which shows extensive first pass effect is difficult task that, needs to be solved by formulators in the pharmaceutical science. The low oral bioavailability (49%) of flutamide may be due to poor wettability, low aqueous solubility and extensive first pass effect. The aim of present investigation was to prepare flutamide loaded microspheres and incorporate it into suppositories for rectal delivery to avoid first pass effect and enhance residence time. Flutamide loaded mucoadhesive microspheres of Ocimum Basilicum mucilage (OBM) were prepared using spray drying and characterized by percent production yield, encapsulation efficiency, particle size, zeta potential, polydispersity index, DSC, SEM, XRPD, in vitro drug release and stability studies. Moreover, ex vivo mucoadhesion was investigated using falling liquid film technique to determine the adhesion of microspheres to sheep rectal mucosa. The microspheres had nearly spherical shape and size about 2.53?μm. The encapsulation efficiency and mucoadhesion of optimized formulation MBF10 were found to be 69.6?±?2.3% and 89.01?±?2.18%, respectively. Percent CDR of optimized flutamide loaded mucoadhesive microspheres was found to be 88.7?±?1.3 at 7?h. In conclusion, OBM microparticles based suppository could be used to deliver drug through rectal delivery.     

Materials Design of Solar Cell Absorbers Beyond Perovskites and Conventional Semiconductors via Combining Tetrahedral and Octahedral Coordination

Tetrahedral coordination structures, e.g. crystalline Si, GaAs, CdTe, and octahedral coordination structures, e.g. perovskites, represent two classes of successful crystal structures hitherto for solar cell absorbers. Here, via first‐principles calculations and crystal symmetry analysis, the two classes of semiconductors are shown exhibiting complementary properties in terms of bond covalency/ionicity, optical property, defect tolerance, and stability, which are correlated with their respective coordination number. Therefore, a spinel structure is proposed, which combines tetrahedral and octahedral coordination into a single crystal structure, as an alternative to perovskite and conventional semiconductors for potential photovoltaic applications. The case studies of a class of 105 spinel AB₂X₄ systems identify five spinel compounds HgAl₂Se₄, HgIn₂S₄, CdIn₂Se₄, HgSc₂S₄, and HgY₂S₄ as promising solar cell absorbers. In particular, HgAl₂Se₄ has suitable bandgap (1.36 eV by GW0 calculation), small direct–indirect bandgap difference (24 meV), appropriate carrier effective mass (m_e = 0.08 m₀, and m_h = 0.69 m₀), strong optical absorption, and high dynamic stability. This study suggests that crystal systems with mixed tetrahedral and octahedral coordination may open a viable route for emerging solar cell absorbers.     

Single-Crystalline TiO2(B) Nanobelts with Unusual Large Exposed {100} Facets and Enhanced Li-Storage Capacity

The {100} facet of single-crystalline TiO₂(B) is an ideal platform for inserting Li ions, but it is hard to be obtained due to its high surface energy. Here, the single-crystalline TiO₂(B) nanobelts from H₂Ti₃O₇ with nearly 70% {100} facets exposed are synthesized, which significantly enhances Li-storage capacity. The first-principle calculations demonstrate an ab in-plane 2D diffusion through the exposed {100} facets. As a consequence, the nanobelts can significantly accommodate Li ions in LiTiO₂ formula with specific capacity up to 335 mAh g⁻¹, which is in good agreement with the electrochemical characterizations. Coating with conductive and protective poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate), the cut-off discharge voltage is as low as 0.5 V, leading to a capacity of 160.7 mAh g⁻¹ after 1500 cycles with a retention rate of 66% at 1C. This work provides a practical strategy to increase the Li-ion capacity and cycle stability by tailoring the crystal orientation and nanostructures.     

Highly disordered ionic distribution in α-dicalcium silicate for structure relaxation

Dicalcium silicate, which is found in steelmaking slag for dephosphorization, exists as the hexagonal α phase at high temperatures. The α-dicalcium silicate forms a solid solution with tricalcium phosphate in the entire composition range, although the reason for high solubility of phosphorus remains unclear in view of the crystal structure. It has previously been reported that the crystal structure of α-dicalcium silicate consists of a symmetric arrangement of Ca²⁺ ions and SiO₄⁴⁻ tetrahedra, although other polymorphs exhibit asymmetric arrangements. However, because the occupation probability of each atomic site in the α polymorph is not limited to unity, it has not been qualified how these ions are exactly arranged. In this study, the ionic distribution in the α polymorph of dicalcium silicate was evaluated by first-principles calculation based on density functional theory (DFT), as well as by molecular dynamics (MD) simulation with a polarizable ion model optimized by DFT calculation. The results indicated that the completely symmetric ionic arrangement, as reported for the α phase, is the most unstable. Electronic-state calculation and MD simulation indicated that a highly disordered ionic arrangement spontaneously forms in the α-phase crystal for structure relaxation when held at high temperatures, or when phosphorus is incorporated.     

Integration of machine learning and first principles models

Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white-box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black-box models) and artificial intelligence (AI) techniques. Hybrid models (gray-box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine-learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models.     

First principles computational studies of spontaneous reduction reaction of Eu(III) in eutectic LiCl‐KCl molten salt

Using first principles calculations, we study fundamental mechanism of spontaneous reduction reaction of Eu³⁺ to Eu²⁺ in eutectic LiCl‐KCl molten salt. We decouple the reaction Gibbs free energy into enthalpy and entropy contributions by using rigorous thermodynamic formalism. Key structural features of the solvation shell are characterized by the radial distribution function and the coordination number. Compared with Eu²⁺, the Eu³⁺ ion has a more rigid framework of the solvation shells, corroborating its stronger electrostatic interaction with neighboring ligands of Cl^? ions and a more favorable state on the aspect of enthalpy. Computations on vibrational frequency, however, pose significant contribution of vibrational entropy to the reaction Gibbs free energy for the reduction. Vibration frequency of Eu²⁺ is smaller than that of Eu³⁺, driving a more positive change of the entropy in the reduction reaction. Furthermore, an Eu²⁺ diffuses more quickly than an Eu³⁺ in the LiCl‐KCl molten salt with switching mechanism of ligand Cl^? ions in the solvation shell. Our results propose that the spontaneity of the reduction reaction is driven by the entropic contribution by overcoming the penalty of the reaction enthalpy.