RESUMEN
Antiferroelectrics are fundamental mother compounds critical in developing innovative lead-free piezoelectrics and ferroelectrics and hold great promise for wide-ranging applications in energy conversion and electronic devices. However, harnessing their superior properties presents a significant challenge due to the delicate balance required between their various states. In this study, through the unique design of nanopillar structures to alleviate the local polar heterogeneity, we have achieved significantly improved piezo-/ferro-electricity in classic lead-free antiferroelectric AgxNbO3-δ (x = 1, 0.9, and 0.8) epitaxial thin films. The effective piezoelectric coefficient reaches 440 pm V-1, 1 order of magnitude larger than the stoichiometric AgNbO3, rivaling classic lead zirconate titanate piezoelectrics. Atomic-scale electron microscopy investigations unravel the underlying mechanisms. The nanopillars, characterized by antisite occupancy of both Ag and Nb atoms and forming out-of-phase boundaries with the matrix, reduce the local crystal symmetry via interphase strain. This leads to the creation of flexible multinanodomain structures that significantly facilitate polarization rotation, thus substantially enhancing the piezoelectric performance. This study demonstrates the feasibility of engineering local heterogeneity through nanopillar design, offering a generally applicable method for property improvement of a wide range of antiferroelectrics.
RESUMEN
The 3D printing technique offers huge opportunities for customized thick-electrode designs with high loading densities to enhance the area capacity in a limited space. However, key challenges remain in formulating 3D printable inks with exceptional rheological performance and facilitating electronic/ion transport in thick bulk electrodes. Herein, a hybrid ink consisting of woody-derived cellulose nanofibers (CNFs), multiwalled carbon nanotubes (MWCNTs), and urea is formulated for the 3D printing nitrogen-doped thick electrodes, in which CNFs serve as both dispersing and thickening agents for MWCNTs, whereas urea acts as a doping agent. By systematically tailoring the concentration-dependent rheological performance and 3D printing process of the ink, a variety of gel architectures with high geometric accuracy and superior shape fidelity are successfully printed. The as-printed gel architecture is then transformed into a nitrogen-doped carbon block with a hierarchical porous structure and superior electrochemical performance after freeze-drying and annealing treatments. Furthermore, a quasi-solid-state symmetric supercapacitor assembled with two interdigitated carbon blocks obtained by a 3D printing technique combined with a nitrogen-doping strategy delivers an energy density of 0.10 mWh cm-2 at 0.56 mW cm-2 . This work provides guidance for the formulation of the printable ink used for 3D printing of high-performance thick carbon electrodes.
RESUMEN
Advancements in ultrafast electron microscopy have allowed elucidation of spatially selective structural dynamics. However, as the spatial resolution and imaging capabilities have made progress, quantitative characterization of the electron pulse trains has not been reported at the same rate. In fact, inexperienced users have difficulty replicating the technique because only a few dedicated microscopes have been characterized thoroughly. Systems replacing laser driven photoexcitation with electrically driven deflectors especially suffer from a lack of quantified characterization because of the limited quantity. The primary advantages to electrically driven systems are broader frequency ranges, ease of use and simple synchronization to electrical pumping. Here, we characterize the technical parameters for electrically driven UEM including the shape, size and duration of the electron pulses using low and high frequency chopping methods. At high frequencies, pulses are generated by sweeping the electron beam across a chopping aperture. For low frequencies, the beam is continuously forced off the optic axis by a DC potential, then momentarily aligned by a countering pulse. Using both methods, we present examples that measure probe durations of 2 ns and 10 ps for the low and high frequency techniques, respectively. We also discuss how the implementation of a pulsed probe affects STEM imaging conditions by adjusting the first condenser lens.
RESUMEN
The development of ultrafast electron microscopy (UEM), specifically stroboscopic imaging, has brought the study of structural dynamics to a new level by overcoming the spatial limitations of ultrafast spectroscopy and the temporal restrictions of traditional TEM simultaneously. Combining the concepts governing both techniques has enabled direct visualization of dynamics with spatiotemporal resolutions in the picosecond-nanometer regime. Here, we push the limits of imaging using a pulsed electron beam via RF induced transverse deflection based on the newly developed 200 keV frequency-tunable strip-line pulser. We demonstrate a 0.2 nm spatial resolution and elucidation of magnetic spin induction maps using the phase-microscopy method. We also present beam coherence measurements and expand our study using the breathing modes of a silicon interdigitated comb under RF excitation which achieves improved temporal synchronization between the electron pulse-train and electric field. A new RF holder has also been developed with impedance matching to the RF signal to minimize transmission power loss to samples and its performance is compared with a conventional sample holder.
RESUMEN
Two dimensional (2D) peak finding is a common practice in data analysis for physics experiments, which is typically achieved by computing the local derivatives. However, this method is inherently unstable when the local landscape is complicated or the signal-to-noise ratio of the data is low. In this work, we propose a new method in which the peak tracking task is formalized as an inverse problem, which thus can be solved with a convolutional neural network (CNN). In addition, we show that the underlying physics principle of the experiments can be used to generate the training data. By generalizing the trained neural network on real experimental data, we show that the CNN method can achieve comparable or better results than traditional derivative based methods. This approach can be further generalized in different physics experiments when the physical process is known.