Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAntiferroelectric negative capacitance from a structural phase transition in zirconia
116
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Crystalline materials with broken inversion symmetry can exhibit a spontaneous electric polarization, which originates from a microscopic electric dipole moment. Long-range polar or anti-polar order of such permanent dipoles gives rise to ferroelectricity or antiferroelectricity, respectively. However, the recently discovered antiferroelectrics of fluorite structure (HfO$_2$ and ZrO$_2$) are different: A non-polar phase transforms into a polar phase by spontaneous inversion symmetry breaking upon the application of an electric field. Here, we show that this structural transition in antiferroelectric ZrO$_2$ gives rise to a negative capacitance, which is promising for overcoming the fundamental limits of energy efficiency in electronics. Our findings provide insight into the thermodynamically forbidden region of the antiferroelectric transition in ZrO$_2$ and extend the concept of negative capacitance beyond ferroelectricity. This shows that negative capacitance is a more general phenomenon than previously thought and can be expected in a much broader range of materials exhibiting structural phase transitions.
rate research
Read More
A negative capacitance has been observed in a nano-colloid between 0.1 and 10^-5 Hz. The response is linear over a broad range of conditions. The low-omega dispersions of both the resistance and capacitance are consistent with the free-carrier plasma model, while the transient behavior demonstrates an unusual energy storage mechanism. A collective excitation, therefore, is suggested.
As machine learning becomes increasingly important in engineering and science, it is inevitable that machine learning techniques will be applied to the investigation of materials, and in particular the structural phase transitions common in ferroelectric materials. Here, we build and train an artificial neural network to accurately predict the energy change associated with atom displacements and use the trained artificial neural network in Monte-Carlo simulations on ferroelectric materials to investigate their phase transitions. We apply this approach to two-dimensional monolayer SnTe and show that it can indeed be used to simulate the phase transitions and predict the transition temperature. The artificial neural network, when viewed as a universal mathematical structure, can be readily transferred to the investigation of other ferroelectric materials when training data generated with ab initio methods are available.
Zirconia (zirconium dioxide) and hafnia (hafnium dioxide) are binary oxides used in a range of applications. Because zirconium and hafnium are chemically equivalent, they have three similar polymorphs, and it is important to understand the properties and energetics of these polymorphs. However, while density functional theory calculations can get the correct energetic ordering, the energy differences between polymorphs depend very much on the specific density functional theory approach, as do other quantities such as lattice constants and bulk modulus. We have used highly accurate quantum Monte Carlo simulations to model the three zirconia and hafnia polymorphs. We compare our results for structural parameters, bulk modulus, and cohesive energy with results obtained from density functional theory calculations. We also discuss comparisons of our results with existing experimental data, in particular for structural parameters where extrapolation to zero temperature can be attempted. We hope our results of structural parameters as well as for cohesive energy and bulk modulus can serve as benchmarks for density-functional theory based calculations and as a guidance for future experiments.
The prototypical antiferroelectric PbZrO$_3$ has several unsettled questions, such as the nature of the antiferroelectric transition, possible intermediate phase and the microscopic origin of the Pbam ground state. Using first principles, we show that no phonon becomes truly soft at the cubic-to-Pbam transition temperature, and the order-disorder character of this transition is clearly demonstrated based on molecular dynamics simulations and potential energy surfaces. The out-of-phase octahedral tilting is an important degree of freedom, which can collaborate with other phonon distortions and form a complex energy landscape with multiple minima. Candidates of the possible intermediate phase are suggested based on the calculated kinetic barriers between energy minima, and the development of a first-principles-based effective Hamiltonian. The use of this latter scheme further reveals that specific bi-linear interactions between local dipoles and octahedral tiltings play a major role in the formation of the Pbam ground state, which contrasts with most of the previous explanations.
Garnet-type Li7La3Zr2O12 (LLZO) is a solid electrolyte material with a low-conductivity tetragonal and a high-conductivity cubic phase. Using density-functional theory and variable cell shape molecular dynamics simulations, we show that the tetragonal phase stability is dependent on a simultaneous ordering of the Li ions on the Li sublattice and a volume-preserving tetragonal distortion that relieves internal structural strain. Supervalent doping introduces vacancies into the Li sublattice, increasing the overall entropy and reducing the free energy gain from ordering, eventually stabilizing the cubic phase. We show that the critical temperature for cubic phase stability is lowered as Li vacancy concentration (dopant level) is raised and that an activated hop of Li ions from one crystallographic site to another always accompanies the transition. By identifying the relevant mechanism and critical concentrations for achieving the high conductivity phase, this work shows how targeted synthesis could be used to improve electrolytic performance.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
