No abstract

No abstract

One-dimensional Haldane gap materials, such as the rare earth barium chain nickelates, have received great interest due to their vibrant one-dimensional spin antiferromagnetic character and unique structure. Herein we report how these 1D structural features can also be highly beneficial for thermoelectric applications by analysis of the system CaBaGdNiO 0 ≤ x ≤ 0.25. Attractive Seebeck coefficients of 140-280 μV K at 350-1300 K are retained even at high acceptor-substitution levels, provided by the interplay of low dimensionality and electronic correlations. Furthermore, the highly anisotropic crystal structure of Haldane gap materials allows very low thermal conductivities, reaching only 1.5 W m K at temperatures above 1000 K, one of the lowest values currently documented for prospective oxide thermoelectrics. Although calcium substitution in BaGdNiO increases the electrical conductivity up to 5-6 S cm at 1150 K < T < 1300 K, this level remains insufficient for thermoelectric applications. Hence, the combination of highly promising Seebeck coefficients and low thermal conductivities offered by this 1D material type underscores a potential new structure type for thermoelectric materials, where the main challenge will be to engineer the electronic band structure and, probably, microstructural features to further enhance the mobility of the charge carriers.

This work aims to compare the accuracy of several drying modelling techniques namely semi-empirical, diffusive and artificial neural network (ANN) models as applied to salted codfish (Gadus Morhua). To this end, sets of experimental data were collected to adjust parameters for the models. Modelling of codfish drying was performed by resorting to Page and Thompson semi-empirical models and to a Fick diffusion law. The ANN employed a neural network multilayer 'feed-forward', consisting of one input layer, with four neurons, one hidden layer, formed by five neurons and one output layer with a convergence criterion for training purposes. The simulations showed good results for the ANN (correlation coefficient between 0.987 and 0.999) and semi-empirical models (correlation coefficient ranging from 0.992 to 0.997 for Page's model, and from 0.993 to 0.996 for Thompson's model), while improvements were required to obtain better predictions by the diffusion model (correlation coefficients ranged from 0.864 to 0.959).

No abstract