By virtue of its non-toxicity, high T c, and non-linear optical and ferroelectric properties, one-dimensional (1D) potassium niobate (KNbO3) may enable the development of numerous nanoscale devices. Despite the progress in 1D perovskite materials, preparing high aspect ratio KNbO3 nanostructures is still a concern. This report presents the successful synthesis of ultra-long KNbO3 nanofibers using a simple sol-gel assisted far-field electrospinning process. At optimized conditions, centimeters long, orthorhombic KNbO3 nanofibers with an average diameter of 100 nm have been obtained. The nanofibers are composed of uniform grains densely stacked along the direction of the nanofiber axis. Due to large surface-volume ratio, a high sensitive humidity nanosensor based on KNbO3 nanofibers displaying a logarithmic-linear dependence behavior of the conductance with the relative humidity (RH) was demonstrated. The conductance increases dramatically from 10(-10) ℧ to 10(-6) ℧ while RH varies from 15% to 95% at room temperature. In addition, the nanosensor exhibits excellent sensing performance, including ultrafast response (≤2 s) and recovery times (≤10 s), good linearity and reproducibility. Furthermore, the change in ferroelectric coercivity with respect to the RH and its effect in the sensing behavior were unveiled. This work could enable broad applications in the fields of environmental sensing and nano-electrical-mechanical systems.

Self-powered nanodevices for applications such as sensor networks and IoTs are among the emerging technologies in electronics. Piezoelectric nanogenerators (P-NGs) that harvest energy from mechanical stimuli are highly valuable in the development of self-sufficient nanosystems. Despite progress in the development of P-NGs, the use of porous perovskite ferroelectric nanofibers was barely considered or discussed. In this letter, a flexible high output nanogenerator is fabricated using a nanocomposite comprising porous potassium niobate (KNbO3) nanofibers and polydimethylsiloxane. When a compressive force was applied to as-fabricated P-NG, a peak-to-peak output voltage of ∼16 V and a maximum closed circuit current of 230 nA were obtained, which are high enough to realize self-powered nanodevices. In addition, due to their porosity and non-toxic nature, KNbO3 nanofibers may be used as an alternative to the dominant lead-based piezoelectric devices. Besides the high output performance of the device, multifunctional capability, flexible design, and cost-effective construction of the as-fabricated P-NG can be crucial to large-scale deployment of autonomous devices.

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Ferroelectric perovskites are an important group of materials underpinning a wide variety of devices ranging from sensors and transducers to nonvolatile memories and photovoltaic cells. Despite the progress in material synthesis, ferroelectric characterization of nanoscale perovskites is still a challenge. Piezoresponse force microscopy (PFM) is one of the most popular tools for probing and manipulating nanostructures to study the ferroelectric properties. However, the interpretation of hysteresis data and alternate signal origins are critical. Here, we use a family of scanning probe microscopy (SPM) techniques to systematically investigate the ferroelectric behavior of electrospun potassium niobate (KNbO) nanofibers. Band Excitation (BE) SPM scans reveal that PFM signals are dominated by changes in resonant frequency due to rough nanofiber surfaces, rather than the actual local piezoelectric strength. We investigate the bias-induced charge injection properties and electrostatic interactions on the PFM response of the nanofiber using contact mode Kelvin probe force microscopy (cKPFM). Furthermore, the impact of relative humidity on the KNbO nanofiber's piezoresponse, switching behavior, and tip-induced charges are explored. The resultant data from BE scans were utilized to estimate the piezoelectric constants of the KNO nanofiber. These observations will provide clarity in studying newly developed ferroelectric nanostructures and unambiguously interpreting the PFM data.