Ordered mesoporous MFe(2)O(4) (M = Co, Cu, Mg, Ni, Zn) thin films with nanocrystalline walls, uniform 16 nm diameter pores and high thermal stability: template-directed synthesis and characterization of redox active trevorite

In this paper, we report on ordered mesoporous NiFe(2)O(4) thin films synthesized via co-assembly of hydrated ferric nitrate and nickel chloride with an amphiphilic diblock copolymer, referred to as KLE. We establish that the NiFe(2)O(4) samples are highly crystalline after calcination at 600 °C, and that the conversion of the amorphous inorganic framework comes at little cost to the ordering of the high quality cubic network of pores averaging 16 nm in diameter. We further show that the synthesis method employed in this work can be readily extended to other ferrites, such as CoFe(2)O(4), CuFe(2)O(4), MgFe(2)O(4), and ZnFe(2)O(4), which could pave the way for innovative device design. While this article focuses on the self-assembly and characterization of these materials using various state-of-the-art techniques, including electron microscopy, grazing incidence small-angle X-ray scattering (GISAXS), time-of-flight secondary ion mass spectrometry (TOF-SIMS), X-ray photoelectron spectroscopy (XPS), as well as UV-vis and Raman spectroscopy, we also examine the electrochemical properties and show the benefits of combining a continuous mesoporosity with nanocrystalline films. KLE-templated NiFe(2)O(4) electrodes exhibit reasonable levels of lithium ion storage at short charging times which stem from facile pseudocapacitance.     

Hot-electron-driven enhancement of spin-lattice coupling in Gd and Tb 4f ferromagnets observed by femtosecond x-ray magnetic circular dichroism

Femtosecond x-ray magnetic circular dichroism was used to study the time-dependent magnetic moment of 4f electrons in the ferromagnets Gd and Tb, which are known for their different spin-lattice coupling. We observe a two-step demagnetization with an ultrafast demagnetization time of 750 fs identical for both systems and slower times which differ sizeably with 40 ps for Gd and 8 ps for Tb. We conclude that spin-lattice coupling in the electronically excited state is enhanced up to 50 times compared to equilibrium.     

Scheduling with few changes

In this work we consider scheduling problems where a sequence of assignments from products to machines – or from tasks to operators, or from workers to resources – has to be determined, with the goal of minimizing the costs (=money, manpower, and/or time) that are incurred by the interplay between those assignments. To account for the different practical requirements (e.g. few changes between different products/tasks on the same machine/operator, few production disruptions, or few changes of the same worker between different resources), we employ different objective functions that are all based on elementary combinatorial properties of the schedule matrix. We propose simple and efficient algorithms to solve the corresponding optimization problems, and provide hardness results where such algorithms most likely do not exist.     

A posteriori error estimation for arbitrary order FEM applied to singularly perturbed one-dimensional reaction-diffusion problems

FEM discretizations of arbitrary order r are considered for a singularly perturbed one-dimensional reaction-diffusion problem whose solution exhibits strong layers. A posteriori error bounds of interpolation type are derived in the maximum norm. An adaptive algorithm is devised to resolve the boundary layers. Numerical experiments complement our theoretical results.     

The sdfem on Shishkin meshes for linear convection-diffusion problems

Summary. We consider singularly perturbed linear elliptic problems in two dimensions. The solutions of such problems typically exhibit layers and are difficult to solve numerically. The streamline diffusion finite element method (SDFEM) has been proved to produce accurate solutions away from any layers on uniform meshes, but fails to compute the boundary layers precisely. Our modified SDFEM is implemented with piecewise linear functions on a Shishkin mesh that resolves boundary layers, and we prove that it yields an accurate approximation of the solution both inside and outside these layers. The analysis is complicated by the severe nonuniformity of the mesh. We give local error estimates that hold true uniformly in the perturbation parameter , provided only that , where mesh points are used. Numerical experiments support these theoretical results. Received February 19, 1999 / Revised version received January 27, 2000 / Published online August 2, 2000     

Evolutionary neighborhood discovery algorithm for agricultural routing planning in multiple fields

Annals of Operations Research - In recent years, operations research in agriculture has improved the harvested yield, reduced the cost and time required for field operations, and maintained...     

Nonlinear Optical Properties of Benzylic Amide [2] Catenanes: A Novel Versatile Photonic Material

Abstract

Benzylic amide catenanes are a class of synthetically-accessible interlocked molecular rings which can rotate one through the other depending on the nature of the local environment. The rings contain four phenyl units each and interlocking also affords their packing in novel, highly interacting ways that may lead to unexpected properties thus opening up the possibility of developing new materials. Third harmonic generation in benzylic amide catenane solutions was measured at a wavelength of γ = 1064 nm, with the fundamental and the harmonic wavelengths in the region of transparency of the material. The thoroughly non resonant value of the hyperpolarisability γ(-3ω; ω, ω, ω) was found to be (6.5 ± 0.7) × 10^?35 esu with a negligible imaginary part, in agreement with the value of (6.8 ± 0.9) × 10^?35 esu calculated from a bond-additivity model of hyperpolarisability. The static second order hyperpolarisability predicted by a Molecular Orbital model was about a factor four less than the experimental value. Second hyperpolarizability values of several solvents were also measured at the fundamental wavelength of γ = 1064 nm.     

Information Field Theory and Artificial Intelligence

Information field theory (IFT), the information theory for fields, is a mathematical framework for signal reconstruction and non-parametric inverse problems. Artificial intelligence (AI) and machine learning (ML) aim at generating intelligent systems, including such for perception, cognition, and learning. This overlaps with IFT, which is designed to address perception, reasoning, and inference tasks. Here, the relation between concepts and tools in IFT and those in AI and ML research are discussed. In the context of IFT, fields denote physical quantities that change continuously as a function of space (and time) and information theory refers to Bayesian probabilistic logic equipped with the associated entropic information measures. Reconstructing a signal with IFT is a computational problem similar to training a generative neural network (GNN) in ML. In this paper, the process of inference in IFT is reformulated in terms of GNN training. In contrast to classical neural networks, IFT based GNNs can operate without pre-training thanks to incorporating expert knowledge into their architecture. Furthermore, the cross-fertilization of variational inference methods used in IFT and ML are discussed. These discussions suggest that IFT is well suited to address many problems in AI and ML research and application.