The emerging dye-sensitized solar cells, perovskite solar cells, and organic solar cells have been regarded as promising photovoltaic technologies. The device structures and components of these solar cells are imperative to the device’s efficiency and stability. Polymers can be used to adjust the device components and structures of these solar cells purposefully, due to their diversified properties. In dye-sensitized solar cells, polymers can be used as flexible substrates, pore- and film-forming agents of photoanode films, platinum-free counter electrodes, and the frameworks of quasi-solid-state electrolytes. In perovskite solar cells, polymers can be used as the additives to adjust the nucleation and crystallization processes in perovskite films. The polymers can also be used as hole transfer materials, electron transfer materials, and interface layer to enhance the carrier separation efficiency and reduce the recombination. In organic solar cells, polymers are often used as donor layers, buffer layers, and other polymer-based micro/nanostructures in binary or ternary devices to influence device performances. The current achievements about the applications of polymers in solar cells are reviewed and analyzed. In addition, the benefits of polymers for solar cells, the challenges for practical application, and possible solutions are also assessed.

In this paper, an edge computing system for IoT-based (Internet of Things) smart grids is proposed to overcome the drawbacks in the current cloud computing paradigm in power systems, where many problems have yet to be addressed such as fully realizing the requirements of high bandwidth with low latency. The new system mainly introduces edge computing in the traditional cloud-based power system and establishes a new hardware and software architecture. Therefore, a considerable amount of data generated in the electrical grid will be analyzed, processed, and stored at the edge of the network. Aided with edge computing paradigm, the IoT-based smart grids will realize the connection and management of substantial terminals, provide the real-time analysis and processing of massive data, and foster the digitalization of smart grids. In addition, we propose a privacy protection strategy via edge computing, data prediction strategy, and preprocessing strategy of hierarchical decision-making based on task grading (HDTG) for the IoT-based smart girds. The effectiveness of our proposed approaches has been demonstrated via the numerical simulations.INDEX TERMS Edge computing, IoT-based smart grids, data prediction, artificial intelligence, data privacy protection, cloud computing.

Background:We developed a computational model integrating clinical data and imaging features extracted from contrast-enhanced computed tomography (CECT) images, to predict lymph node (LN) metastasis in patients with pancreatic ductal adenocarcinoma (PDAC).Methods: This retrospective study included 159 patients with PDAC (118 in the primary cohort and 41 in the validation cohort) who underwent preoperative contrast-enhanced computed tomography examination between 2012 and 2015. All patients underwent surgery and lymph node status was determined. A total of 2041 radiomics features were extracted from venous phase images in the primary cohort, and optimal features were extracted to construct a radiomics signature. A combined prediction model was built by incorporating the radiomics signature and clinical characteristics selected by using multivariable logistic regression. Clinical prediction models were generated and used to evaluate both cohorts. Results: Fifteen features were selected for constructing the radiomics signature based on the primary cohort. The combined prediction model for identifying preoperative lymph node metastasis reached a better discrimination power than the clinical prediction model, with an area under the curve of 0.944 vs. 0.666 in the primary cohort, and 0.912 vs. 0.713 in the validation cohort.Conclusions: This pilot study demonstrated that a noninvasive radiomics signature extracted from contrastenhanced computed tomography imaging can be conveniently used for preoperative prediction of lymph node metastasis in patients with PDAC.

The ternary iron-group thiospinels of metal diindium sulfides (MIn S , M=Fe, Co, Ni) with a vertically aligned nanosheet array structure are fabricated through an in situ solvothermal method on F-doped tin oxide (FTO) substrates, which are employed as one type of platinum (Pt)-free counter electrodes (CEs) in structure-dependent dye-sensitized solar cells (DSSCs). A DSSC assembled with ternary CoIn S CE achieves an photoelectric conversion efficiency (PCE) of 8.83 %, outperforming than that of FeIn S (7.18 %) and NiIn S (8.27 %) CEs under full sunlight illumination (100 mW cm , AM 1.5 G), which is also comparable with that of the Pt CE (8.19 %). Putting aside that the interconnected nanosheet array provides fast electron transfer and electrolyte diffusion channels, the highest PCE of CoIn S based DSSC results from its largest specific surface area (144.07 m g ), providing abundant active sites and the largest electron injection efficiency from CE to electrolyte.

With the rapid development of flexible and wearable electronic devices, research on high-sensitivity strain sensors has been attracting much attention. Here, glutaraldehyde is used as a cross-linking reagent to precross-link poly(vinyl alcohol); then FeCl 3 •6H 2 O is added into the precross-linked poly(vinyl alcohol) to obtain composite films of FeCl 3 @PVA after gelatinization and freeze drying. Elastic conductive films of polypyrrole@poly(vinyl alcohol) (PPy@PVA) are prepared by immersing FeCl 3 @PVA into a solution of pyrrole in acetonitrile and water to complete the polymerization in situ. The effects of the concentrations of glutaraldehyde and FeCl 3 •6H 2 O on the film's structure and properties have been studied in detail; the results show that the strain sensor prepared from the optimized film has excellent stretchability (strain up to 309.5%), mechanical property (tensile strength of 32.8 MPa), and relatively high sensitivity (gauge factor can reach 5.07 under 1.0% strain). It can be used to detect various tiny physiological signals, for example, detecting the number of pulse beats, bending of the knuckles at different frequencies, and recognizing the pronunciation of different words by vocal cord vibration. These good properties mean that this kind of PPy@PVA strain sensor has great application prospects in physiological monitoring.

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Hemorrhage transformation (HT) is a frequent but maybe fatal complication following the acute ischemic stroke due to the severe damage of blood brain barrier (BBB). Quantitative BBB permeability imaging is...

The rapid development of flexible electronic devices has caused a boom in researching flexible sensors based on hydrogels, but most of the flexible sensors can only work at room temperature, and they are difficult to adapt to extremely cold or dry environments. Here, the flexible hydrogel fibers (PEDOT:PSS@ PVA) with excellent resistance to extreme environments have been prepared by adding glycerin (GL) to the mixture of poly(vinyl alcohol) (PVA) and poly 3,4-dioxyethylene thiophene:polystyrene sulfonic acid (PEDOT:PSS) because GL molecules can form dynamic hydrogen bonds with an elastic matrix of PVA molecules. It is found that the prepared sensor exhibits very good flexibility and mechanical strength, and the ultimate tensile strength can reach up to 13.76 MPa when the elongation at break is 519.9%. Furthermore, the hydrogel fibers possess excellent water retention performance and low-temperature resistance. After being placed in the atmospheric environment for 1 year, the sensor still shows good flexibility. At a low temperature of −60 °C, the sensor can stably endure 1000 repeated stretches and shrinks (10% elongation). In addition to the response to a large strain, this fiber sensor can also detect extremely small strains as low as 0.01%. It is proved that complex human movements such as knuckle bending, vocalization, pulse, and others can be monitored perfectly by this fiber sensor. The above results mean that the PEDOT:PSS@PVA fiber sensor has great application prospects in physiological monitoring.