Robust, Flexible Thermoelectric Film for Energy Harvesting by a Simple and Eco-Friendly Method.

As for the self-supporting composite films, it is significant to develop a structural design that allows for excellent flexibility while reducing the negative effect on thermoelectric (TE) properties. Herein, a robust, flexible TE film was fabricated by in situ chemical transformation and vacuum-assisted filtration without any organic solvents involved. The performance of the films was further optimized by adjusting the Ag/Te ratio and post-treatment methods. Owing to the semi-interpenetrating nanonetwork structure formed by AgxTe nanowires and bacterial cellulose, the obtained TE film displayed a high tensile strength of ∼78.4 MPa and a high power factor of 48.9 µW m-1 K-2 at room temperature. A slight electrical conductivity decrement of the TE film in flexible test (∼2% after 1000 bending cycles) indicates an excellent flexibility. Finally, a TE bracelet was assembled to harvest body heat energy, and a steady current of ∼2.7 µA was generated when worn on the wrist indoors. This work provides a reference for the structural design and practical application of flexible TE films.

Ultrahigh Power Factor of Ternary Composites with Abundant Se Nanowires for Thermoelectric Application.

In this work, ultrahigh-performance single-walled carbon nanotube (SWCNT)/Se nanowire (NW)/poly(3,4-ethylenedioxythiophene):poly(4-styrenesulfonate) (PEDOT:PSS) ternary thermoelectric (TE) nanocomposite films are successfully designed by rational design of CNT/Se/PEDOT:PSS ternary nanocomposites. The addition of CNTs apparently improves the electrical conductivity of composite films, resulting in a relatively huge growth of the power factor. The PEDOT:PSS interface layers uniformly attach on both sides of the Se NWs and CNTs effectively, forming a tightly interleaving and interconnected three-dimensional network. As a consequence, a maximum power factor of 863.83 µW/(m·K2) has been achieved for the sample containing 26 wt % CNTs at 434 K. Ultimately, a flexible TE generator prototype consisting of 5-unit freestanding composite film strips is fabricated using the optimized composite films, which can generate a maximum output power of 206.8 nW at a temperature gradient of 44.7 K. Therefore, the present work has a further potential to be used for the flexible polymer/carbon TE nanocomposite films and devices.

Flexible Thermoelectric Films Based on Bi2Te3 Nanosheets and Carbon Nanotube Network with High n-Type Performance.

Flexible thermoelectric materials and devices have gained wide attention due to their capability to stably and directly convert body heat or industrial waste heat into electric energy. Many research and synthetic methods of flexible high-performance p-type thermoelectric materials have made great progress. However, their counterpart flexible n-type organic thermoelectric materials are seldom studied due to the complex synthesis of conductive polymer and poor stability of n-type materials. In this work, bismuth tellurium (Bi2Te3) nanosheets are in situ grown on single-walled carbon nanotubes (SWCNTs) assisted by poly(vinylpyrrolidone) (PVP). A series of flexible SWCNTs@Bi2Te3 composite films on poly(vinylidene fluoride) (PVDF) membranes are obtained by vacuum-assisted filtration. The high electrical conductivity of 253.9 S/cm, and a corresponding power factor (PF) of 57.8 µW/m·K2 is obtained at 386 K for SWCNTs@Bi2Te3-0.8 film. Moreover, high electrical conductivity retention of 90% can be maintained after a 300-cycle bending test and no obvious attenuation can be detected after being stored in an Ar atmosphere for 9 months, which exhibits good flexibility and excellent stability of the SWCNTs@Bi2Te3 composite films. This work shows a convenient method to fabricate n-type and flexible thermoelectric composite film and further promotes the practical application of n-type flexible thermoelectric materials.

MicroRNA-150 targets Rho-associated protein kinase 1 to inhibit cell proliferation, migration and invasion in papillary thyroid carcinoma.

Thyroid cancer is the most prevalent malignant tumor of the endocrine organs and accounts for one third of all head and neck tumors. Dysregulation of microRNAs is well­known to contribute to the development of various cancers, including papillary thyroid carcinoma (PTC), which accounts for 80­90% of all thyroid cancer cases. The present study aimed to investigate the expression, functional roles of microRNA­150 (miR­150) and its direct target gene in PTC. miR­150 expression in PTC tissues and cell lines was analyzed by reverse transcription­quantitative polymerase chain reaction (RT­qPCR). After transfection with miR­150 mimics, cell proliferation, migration and invasion was analyzed by MTT and Transwell assays, respectively. Bioinformatics analysis was performed to investigate the potential target genes of miR­150, which were then confirmed by luciferase reporter assay, RT­qPCR and western blotting. Functional assays were also applied to investigate the effects of endogenous Rho­associated protein kinase 1 (ROCK1) in PTC. miR­150 was demonstrated to be significantly downregulated in PTC tissues and cell lines. In addition, reduced miR­150 expression was obviously correlated with TNM stage and lymph node metastasis in PTC patients. Restoration of miR­150 expression significantly inhibited PTC cell proliferation, migration and invasion in vitro. Furthermore, ROCK1 was identified as a direct target gene of miR­150. Therefore, ROCK1 knockdown may serve tumor suppressive functions in PTC, induced by miR­150 overexpression. In conclusion, miR­150 overexpression in PTC may inhibit growth and metastasis of PTC cells. miR­150/ROCK1­based targeted therapy may be a potential strategy for the treatment of PTC.

Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches.