No abstract

Poly(3-hexylthiophene) (P3HT) films with various molecular weights (MWs) were successfully prepared, and both their molecular structures and thermoelectric (TE) properties were investigated. It was found that the molecular weight of P3HT had an important effect on the carrier-transport properties by affecting the molecular structure and, as a result, also had an effect on the TE performance. The electrical conductivity of the P3HT films first increased upon increasing the molecular weight and then decreased at high molecular weights, whereas the Seebeck coefficient remained at the same level. As a result, the P3HT-M (MW≈50 000 g mol ) film reached an electrical conductivity of (103.8±1.2) S cm, which is more than 20 times higher than the electrical conductivity of P3HT-M (MW≈10 000 g mol ) and almost 30 % higher than the electrical conductivity of P3HT-M (MW≈100 000 g mol ). Consequently, the maximum TE power factor of P3HT-M at room temperature was as high as (22.6±0.6) μW mK , which is much higher than that of either P3HT-M or P3HT-M . Microstructure analysis combined with C-AFM suggested that carrier transport between most of the ordered and amorphous regions was unconnected in films with low molecular weights, and this resulted in a high migration barrier and poor carrier mobility. Upon increasing the molecular weight, the long molecular chain provided enough connectivity for the charge to move through the ordered regions, which decreased the carrier barrier and increased carrier mobility. Therefore, both the conductivity and Seebeck coefficient were significantly improved. However, a too-high molecular weight could cause more folding of the polymer chain, which would deteriorate the electrical-transport properties. The experimental results not only reveal the intrinsic effect of molecular weight on the electric transporting properties of conducting polymers but also suggest that molecular-weight engineering is an effective way to design and screen high-performance polymer TE materials.

No abstract

Domain dynamics has been one of the hottest research topics for ferroelectric materials in order to understand the ferroelectric mechanisms and to develop the related applications. By using high-speed piezoresponse force microscopy (HSPFM), it is possible to observe the dynamic domain evolution in an ultrashort time increment. This paper combines the HSPFM experiments and machine learning to study the domain growth under a weak AC field in ferroelectric materials. Here, the Bayesian optimized support vector machine is employed to classify the switching domain and stable domain. The results indicate that the machine learning classifier is capable of discerning the switching area. In addition, the domain associated characteristics, such as domain pinning and domain wall pinning, can also be observed and analyzed by combining experiments and machine learning. The machine learning approach can fast and deeply extract the complicated features related to free energy from the multidimensional signals obtained by HSPFM.

Piezoresponse Force Spectroscopy (PFS) is a powerful method widely used for measuring the nanoscale ferroelectric responses of the materials. However, it is found that certain non-ferroelectric materials can also generate similar responses from the PFS measurements due to many other factors, hence, it is believed that PFS alone is not sufficient to differentiate the ferroelectric and non-ferroelectric materials. On the other hands, this work shows that there are distinct differences in contact resonance frequency variation during the PFS measurements for ferroelectric and non-ferroelectric materials.Therefore, a new, simple and effective method is proposed to differentiate the responses from the ferroelectric and non-ferroelectric materials, this new analysis uses contact resonance frequency responses during the PFS measurements as a new parameter to differentiate the PFS measured responses from 2 different materials. Development and applications of the ferroelectric materials have been one of the most active topics for decades. Due to the unique characteristics of spontaneous polarization, ferroelectric materials have been used in a wide range of applications, such as sensors, actuators and memory devices. 1 Developing new ferroelectric materials has great significances for research and applications in the area of functional materials. 2 Comparing with the common dielectric materials with a linear polarization response, ferroelectric materials demonstrate a nonlinear and nonzero polarization response. 3 To study the ferroelectric phenomena at nanoscale, such as at domain level, Piezoresponse Force Microscopy (PFM) and its spectroscopy form, Piezoresponse Force Spectroscopy (PFS), are widely used in the last decades. As the premier characterization tools for domain structures, orientation and properties of the ferroelectric materials, PFM and PFS techniques can probe time-or voltage-dependent phenomena withhigh spatial resolution. 4 In the PFS measurements, the surface of the sample contacts with a sharp conductive tip at the end of PFM cantilever. After applying excitation of DC voltage and scanning of the sample surface with the same tip, local polarization switching may occur and can be detected by the same tip. However, due to the principle of probing method in PFS, 5,6 the measurements of the local ferroelectric responses can be affected by a number of factors. Besides the polarization-electric field (P-E) relationship, the electrostatic force between the tip and sample surface, 7 surface charging, 8-10 Vegard effect 11 and ionic mechanisms 12-14 can also induce the hysteresis-like loops in which are similar to the P-E loops obtained in ferroelectric materials during the PFS measurements. In addition, it is also noted that such hysteresis-like loop can also be observed in a broad variety of non-ferroelectric materials during the PFS measurements, for example, glass, 15 LiCoO2, 12 TiO2 16 and even banana peel. 5 It was therefore believed that the hysteresis loops obtained by PFS is insufficient as the only pr...