In the current study, we synthesized a Cu-doped ZnO (CZO) nanoparticles material using a sol-gel method with different doping concentrations of Cu (0, 2, 3 and 4 at.%). The control of the Cu concentration on structural, electrical and optical properties of CZO nanoparticles was investigated in detail. The XRD analysis of the CZO nanoparticles reveals the formation of ZnO hexagonal wurtzite structure for all samples which confirm the incorporation of Cu2+ ions into the ZnO lattice by substitution. Furthermore, CZO nanoparticles showed a small red shift of absorption band with the incorporation of Cu from 0 to 4 at.%; i.e. a decreased band gap value from 3.34 eV to 3.27 eV with increasing of Cu doping content. The frequency dispersion of the electric conductivity were studied using the Jonscher universal power law, according to relation σ(ω) = σDC + A ωs(T). Alternative current conductivity increases with increasing Cu content in spite of the decrease the activation energy with copper loading. It was found that the conductivity reached its maximum value for critical Cu concentration of 3 at.%. The frequency relaxation phenomenon was also investigated and all results were discussed in term of the copper doping concentration.

The optoelectronic properties and role of Cu concentration on the structural and electrical properties of Cu doped ZnO nanoparticles

Curie temperature modeling of magnetocaloric lanthanum manganites using Gaussian process regression

DC conductivity and study of AC electrical conduction mechanisms by non-overlapping small polaron tunneling model in LiFeP2O7 ceramic

Efficient solid-state refrigeration techniques at room temperature have drawn increasing attention due to their potential for improving energy efficiency of refrigeration, air-conditioning, and temperature-control systems without using harmful gas in conventional gas compression techniques. Recent developments of increased magnetocaloric effects and relative cooling power (RCP) in ferromagnetic lanthanum manganites show promising results of further developments in magnetic refrigeration devices. By incorporating chemical substitutions, oxygen content modifications, and various synthesis methods, these manganites experience lattice distortions from perovskite cubic structures to orthorhombic structures. Lattice distortions, revealed by changes in lattice parameters, have significant influences on adiabatic temperature changes and isothermal magnetic entropy changes, and thus RCP. Empirical results and previous models through thermodynamics and first-principles have shown that changes in lattice parameters correlate with those in RCP, but correlations are merely general tendencies and obviously not universal. In this work, the Gaussian process regression model is developed to find statistical correlations and predict RCP based on lattice parameters among lanthanum manganites. This modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of RCP and understandings of magnetic phase transformations and magnetocaloric effects in lanthanum manganites.

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Relative cooling power modeling of lanthanum manganites using Gaussian process regression

Efficient solid-state refrigeration techniques have drawn increasing attention due to their potential for improving energy efficiency of refrigeration temperature control systems without using harmful gas as in conventional gas compression techniques. Research on magnetocaloric lanthanum manganites with a large maximum magnetic entropy change near room temperature shows promising results for further developments of magnetic refrigeration devices. By incorporating chemical substitutions, oxygen content modifications, and various synthesis methods, these manganites experience lattice distortions from perovskite cubic structures to pseudocubic, orthorhombic, and rhombohedral structures. Further changes in lattice parameters can also be achieved by the introduction of strain due to lattice mismatches. Empirical results and previous models through thermodynamics and first principles show that changes in lattice parameters correlate with those in MMCE, but correlations are merely general tendencies and obviously not universal. In this work, the Gaussian process regression model is developed as a machine learning tool to find statistical correlations between the MMCE and lattice parameters among lanthanum manganites. More than 100 lattices, cubic, pseudocubic, orthorhombic, and rhombohedral, with the MMCE ranging from 0.65 to 
$$8.00\,\hbox {J}\,\hbox {kg}^{-1}\,\hbox {K}^{-1}$$

 under a field change of 5 T are explored for this purpose. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of the magnetocaloric effect. Furthermore, the machine learning algorithm predicts close MMCE results on epitaxial films with strained lattices against experimental results, which can provide guidance on thin film structure design and help understandings of magnetic phase transformations and magnetocaloric effects.

Machine learning the magnetocaloric effect in manganites from lattice parameters

Electrical conduction mechanism and transport properties of LiCrP2O7 compound

Effect of chromium concentration on the structural, magnetic and electrical properties of praseodymium-calcium manganite

Metal-organo-zinc oxide materials: Investigation on the structural, optical and electrical properties

We have studied the influence of erbium doping on the optical and electrical properties of a Bi4Ti3O12 compound. Bi4−xErxTi3O12 (x = 0.0, 0.1, 0.2 and 0.3) samples are prepared by the solid state reaction method. X-ray diffraction (XRD) revealed that our samples are homogenous and crystallize in the orthorhombic system with the Fmmm space group. Absorbance measurement reveals that the peak intensity increases with increasing erbium concentrations and this behavior confirms the good homogeneity of the samples. DC-conductivity measurements show that all samples are characterized by a semiconductor behavior. It is found that electrical conductivity is reduced considerably with increasing erbium content up to x = 0.3. This is due to the decrease in oxygen vacancy density. For the free compound, dc-conductivity is characterized by the appearance of a saturation region at a specific temperature (Tsat = 480 K). For Er doped compounds, Tsat goes beyond 600 K. An AC-conductivity study shows that the conductivity is governed by the jump relaxation model (JRM) and conduction through grain boundaries. We found that the conductivity spectrum of the investigated materials obeys the Jonscher universal double power law. The deduced activation energy increases when increasing the erbium content from Ea = 467 meV for x = 0 to Ea = 670 meV for x = 0.3. Increasing the Er substitution reduces the frequency dispersion of the real part of the dielectric constant e′. Also, the tan δ plot indicates that the investigated samples exhibit the characteristics of a relaxor dielectric.

/pdf/effect-of-erbium-concentration-on-the-structural-optical-and-58lrrexj1y.pdf

A. Bettaibi

Papers

Electrical conduction mechanism and transport properties of LiCrP2O7 compound

Effect of chromium concentration on the structural, magnetic and electrical properties of praseodymium-calcium manganite

Metal-organo-zinc oxide materials: Investigation on the structural, optical and electrical properties

Effect of erbium concentration on the structural, optical and electrical properties of a Bi4Ti3O12 system

Physical properties of 20% Cr-doped Pr0.7Ca0.3MnO3 perovskite