Toward Quantitative Electrochemical Measurements on the Nanoscale by Scanning Probe Microscopy: Environmental and Current Spreading Effects

The application of electric bias across tip–surface junctions in scanning probe microscopy can readily induce surface and bulk electrochemical processes that can be further detected though changes in surface topography, Faradaic or conductive currents, or electromechanical strain responses. However, the basic factors controlling tip-induced electrochemical processes, including the relationship between applied tip bias and the thermodynamics of local processes, remains largely unexplored. Using the model Li-ion reduction reaction on the surface in Li-ion conducting glass ceramic, we explore the factors controlling Li-metal formation and find surprisingly strong effects of atmosphere and back electrode composition on the process. We find that reaction processes are highly dependent on the nature of the counter electrode and environmental conditions. Using a nondepleting Li counter electrode, Li particles could grow significantly larger and faster than a depleting counter electrode. Significant Li ion depletion leads to the inability for further Li reduction. Time studies suggest that Li diffusion replenishes the vacant sites after 12 h. These studies suggest the feasibility of SPM-based quantitative electrochemical studies under proper environmental controls, extending the concepts of ultramicroelectrodes to the single-digit nanometer scale.

Ferroelectricity in Si-doped HfO2 Revealed: A Binary Lead-free Ferroelectric

Static domain structures and polarization dynamics of silicon doped HfO2 are explored. The evolution of ferroelectricity as a function of Si-doping level driving the transition from paraelectricity via ferroelectricity to antiferroelectricity is investigated. Ferroelectric and antiferroelectric properties can be observed locally on the pristine, poled and electroded surfaces, providing conclusive evidence to intrinsic ferroic behavior.

Small Signal Stability Performance of Power System during High Penetration of Wind Generation

The small signal stability of interconnected power systems is one of the important aspects that need to be investigated since the oscillations caused by this kind of instability have caused many incidents. With the increasing penetration of wind power in the power system, particularly doubly fed induction generator (DFIG), the impact on the power system small signal stability performance should be fully investigated. Because the DFIG wind turbine integration is through a fast action converter and associated control, it does not inherently participate in the electromechanical small signal oscillation. However, it influences the small signal stability by impacting active power flow paths in the network and replacing synchronous generators that have power system stabilizer (PSS). In this paper, the IEEE 39 bus test system has been used in the analysis. Furthermore, four study cases and several operation scenarios have been conducted and analysed. The selective eigenvalue Arnoldi/lanczos's method is used to obtain the system eigenvalue in the range of frequency from 0.2 Hz to 2 Hz which is related to electromechanical oscillations. Results show that the integration of DFIG wind turbines in a system during several study cases and operation scenarios give different influence on small signal stability performance.

Local probing of ferroelectric and ferroelastic switching through stress-mediated piezoelectric spectroscopy

Strain effects have a significant role in mediating classic ferroelectric behavior such as polarization switching and domain wall dynamics. These effects are of critical relevance if the ferroelectric order parameter is coupled to strain and is therefore, also ferroelastic. Here, switching spectroscopy piezoresponse force microscopy (SS-PFM) is combined with control of applied tip pressure to exert direct control over the ferroelastic and ferroelectric switching events, a modality otherwise unattainable in traditional PFM. As a proof of concept, stress-mediated SS-PFM is applied toward the study of polarization switching events in a lead zirconate titanate thin film, with a composition near the morphotropic phase boundary with co-existing rhombohedral and tetragonal phases. Under increasing applied pressure, shape modification of local hysteresis loops is observed, consistent with a reduction in the ferroelastic domain variants under increased pressure. These experimental results are further validated by phase field simulations. The technique can be expanded to explore more complex electromechanical responses under applied local pressure, such as probing ferroelectric and ferroelastic piezoelectric nonlinearity as a function of applied pressure, and electro-chemo-mechanical response through electrochemical strain microscopy.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.