Direct Evidence for the Source of Reported Magnetic Behavior in "CoTe"

In order to unambiguously identify the source of magnetism reported in recent studies of the Co-Te system, two sets of high-quality, epitaxial CoTe$_x$ films (thickness $\simeq$ 300 nm) were prepared by pulse laser deposition (PLD). X-ray diffraction (XRD) shows that all of the films are epitaxial along the [001] direction and have the hexagonal NiAs structure. There is no indication of any second phase metallic Co peaks (either $fcc$ or $hcp$) in the XRD patterns. The two sets of CoTe$_x$ films were grown on various substrates with PLD targets having Co:Te in the atomic ratio of 50:50 and 35:65. From the measured lattice parameters $c = 5.396 \AA$ for the former and $c = 5.402\AA$ for the latter, the compositions CoTe$_{1.71}$ (63.1% Te) and CoTe$_{1.76}$ (63.8% Te), respectively, are assigned to the principal phase. Although XRD shows no trace of metallic Co second phase, the magnetic measurements do show a ferromagnetic contribution for both sets of films with the saturation magnetization values for the CoTe$_{1.71}$ films being approximately four times the values for the CoTe$_{1.76}$ films. $^{59}$Co spin-echo nuclear magnetic resonance (NMR) clearly shows the existence of metallic Co inclusions in the films. The source of weak ferromagnetism reported in several recent studies is due to the presence of metallic Co, since the stoichiometric composition "CoTe" does not exist.Comment: 19 pages, 7 figure

Local Structure and It's Effect on The Ferromagnetic Properties of La0.5_{0.5}Sr0.5_{0.5}CoO3_3 thin films}

We have used high-resolution Extended X-ray Absorption Fine-Structure and diffraction techniques to measure the local structure of strained La$_{0.5}$Sr$_{0.5}$CoO$_3$ films under compression and tension. The lattice mismatch strain in these compounds affects both the bond lengths and the bond angles, though the larger effect on the bandwidth is due to the bond length changes. The popular double exchange model for ferromagnetism in these compounds provides a correct qualitative description of the changes in Curie temperature $T_C$, but quantitatively underestimates the changes. A microscopic model for ferromagnetism that provides a much stronger dependence on the structural distortions is needed.Comment: 4 pages, 4 figure

Flux pinning and phase separation in oxygen rich La2-xSrxCuO4+y system

We have studied the magnetic characteristics of a series of super-oxygenated La2-xSrxCuO4+y samples. As shown in previous work, these samples spontaneously phase separate into an oxygen rich superconducting phase with a TC near 40 K and an oxygen poor magnetic phase that also orders near 40 K. All samples studied are highly magnetically reversible even to low temperatures. Although the internal magnetic regions of these samples might be expected to act as pinning sites, our present study shows that they do not favor flux pinning. Flux pinning requires a matching condition between the defect and the superconducting coherence length. Thus, our results imply that the magnetic regions are too large to act as pinning centers. This also implies that the much greater flux pinning in typical La2-xSrxCuO4 materials is the result of nanoscale inhomogeneities that grow to become the large magnetic regions in the super-oxygenated materials. The superconducting regions of the phase separated materials are in that sense cleaner and more homogenous than in the typical cuprate superconductor.Comment: 4 figures 8 pages Submitted to PR

Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery

Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profile using GPR imagery. The classifier clusters and classifies soil profiles strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture and structure of the horizons; and relative arrangement of the horizons, etc). This paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world. Results show that the procedure is able to classify the profile into zones that corresponded with those obtained by visual inspection and interpretation of radargrams. Results indicate that an F-NN model can supply real-time soil profile clustering and classification during field surveys

Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery

Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profile using GPR imagery. The classifier clusters and classifies soil profiles strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture and structure of the horizons; and relative arrangement of the horizons, etc). This paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world. Results show that the procedure is able to classify the profile into zones that corresponded with those obtained by visual inspection and interpretation of radargrams. Results indicate that an F-NN model can supply real-time soil profile clustering and classification during field surveys

Optimization Of Fuzzy Evapotranspiration Model Through Neural Training With Input–Output Examples

In a previous study, we demonstrated that fuzzy evapotranspiration (ET) models can achieve accurate estimation of daily ET comparable to the FAO Penman–Monteith equation, and showed the advantages of the fuzzy approach over other methods. The estimation accuracy of the fuzzy models, however, depended on the shape of the membership functions and the control rules built by trial–and–error methods. This paper shows how the trial and error drawback is eliminated with the application of a fuzzy–neural system, which combines the advantages of fuzzy logic (FL) and artificial neural networks (ANN). The strategy consisted of fusing the FL and ANN on a conceptual and structural basis. The neural component provided supervised learning capabilities for optimizing the membership functions and extracting fuzzy rules from a set of input–output examples selected to cover the data hyperspace of the sites evaluated. The model input parameters were solar irradiance, relative humidity, wind speed, and air temperature difference. The optimized model was applied to estimate reference ET using independent climatic data from the sites, and the estimates were compared with direct ET measurements from grass–covered lysimeters and estimations with the FAO Penman–Monteith equation. The model–estimated ET vs. lysimeter–measured ET gave a coefficient of determination (r2) value of 0.88 and a standard error of the estimate (Syx) of 0.48 mm d–1. For the same set of independent data, the FAO Penman–Monteith–estimated ET vs. lysimeter–measured ET gave an r2 value of 0.85 and an Syx value of 0.56 mm d–1. These results show that the optimized fuzzy–neural–model is reasonably accurate, and is comparable to the FAO Penman–Monteith equation. This approach can provide an easy and efficient means of tuning fuzzy ET models

Investigation Of A Fuzzy-Neural Network Application In Classification Of Soils Using Ground-Penetrating Radar Imagery

Errors associated with visual inspection and interpretations of radargrams often inhibit the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this article presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profiles using GPR imagery. The classifier clusters and classifies soil profile strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture, and structure of the horizons; and relative arrangement of the horizons, etc.). This article illustrates this classification procedure by its application on GPR data, both simulated and actual. Results show that the procedure is able to classify the profile into zones that corresponded with the classifications obtained by visual inspection and interpretation of radar grams. Application of F-NN to a study site in southwest Tennessee gave soil groupings that are in close correspondence with the groupings obtained in a previous study, which used the traditional methods of complete soil morphological, chemical, and physical characterization. At a crossover value of 3.0, the F-NN soil grouping boundary locations fall within a range of ±2.7 m from the soil groupings determined by the traditional methods. These results indicate that F-NN can supply accurate real-time soil profile clustering and classification during field surveys