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Conducting polymers (CPs) have received much attention in both fundamental and practical studies because they have electrical and electrochemical properties similar to those of both traditional semiconductors and metals. CPs possess excellent characteristics such as mild synthesis and processing conditions, chemical and structural diversity, tunable conductivity, and structural flexibility. Advances in nanotechnology have allowed the fabrication of versatile CP nanomaterials with improved performance for various applications including electronics, optoelectronics, sensors, and energy devices. The aim of this review is to explore the conductivity mechanisms and electrical and electrochemical properties of CPs and to discuss the factors that significantly affect these properties. The size and morphology of the materials are also discussed as key parameters that affect their major properties. Finally, the latest trends in research on electrochemical capacitors and sensors are introduced through an in-depth discussion of the most remarkable studies reported since 2003.

Flexible sensing devices have attracted significant attention for various applications, such as medical devices, environmental monitoring, and healthcare. Numerous materials have been used to fabricate flexible sensing devices and improve their sensing performance in terms of their electrical and mechanical properties. Among the studied materials, conductive polymers are promising candidates for next-generation flexible, stretchable, and wearable electronic devices because of their outstanding characteristics, such as flexibility, light weight, and non-toxicity. Understanding the interesting properties of conductive polymers and the solution-based deposition processes and patterning technologies used for conductive polymer device fabrication is necessary to develop appropriate and highly effective flexible sensors. The present review provides scientific evidence for promising strategies for fabricating conductive polymer-based flexible sensors. Specifically, the outstanding nature of the structures, conductivity, and synthesis methods of some of the main conductive polymers are discussed. Furthermore, conventional and innovative technologies for preparing conductive polymer thin films in flexible sensors are identified and evaluated, as are the potential applications of these sensors in environmental and human health monitoring.

Cellulose nanofiber (CNF) with high crystallinity has great mechanical stiffness and strength. However, its length is too short to be used for fibers of environmentally friendly structural composites. This paper presents a fabrication process of cellulose long fiber from CNF suspension by spinning, stretching and drying. Isolation of CNF from the hardwood pulp is done by using (2, 2, 6, 6-tetramethylpiperidine-1-yl) oxidanyl (TEMPO) oxidation. The effect of spinning speed and stretching ratio on mechanical properties of the fabricated fibers are investigated. The modulus of the fabricated fibers increases with the spinning speed as well as the stretching ratio because of the orientation of CNFs. The fabricated long fiber exhibits the maximum tensile modulus of 23.9 GPa with the maximum tensile strength of 383.3 MPa. Moreover, the fabricated long fiber exhibits high strain at break, which indicates high toughness. The results indicate that strong and tough cellulose long fiber can be produced by using ionic crosslinking, controlling spinning speed, stretching and drying.

This study aims to investigate a general optimization procedure of the Hedge-algebras controller (HAC) for controlling dynamic systems. Based on the analysis of factors affecting the control efficiency of HAC, the optimization problem is established following a multi-objective approach. When optimizing HAC, the design variables contain tuning coefficients of control rules, selections of linguistic terms of each rule in the rule base, fuzziness measure parameters of linguistic variables, and variations of the reference range of state and control variables. In which the proposed tuning coefficients have been improved compared with the previous study. In particular, a new inference method is proposed based on the shape/interpolation function of the finite element method. A three-story building structure subjected to earthquake loads is used in the simulation as a case study to demonstrate the effectiveness of the proposed approach. Research results in the present work show that the proposed procedure is general and can be utilized to control different dynamic systems. Moreover, as mentioned above, a large number of the design variables will cause a significant variation in objective functions. It means that the optimum performance is improved compared to optimal cases of individual design variables.

Empirical evidence demonstrates that credit standards, including lending margins and collateral requirements, move in a countercyclical direction. In this study, we construct a small open economy model with financial frictions to generate the countercyclical movement in credit standards. Our analysis demonstrates that countercyclical fluctuations in credit standards work as an amplifier of shocks to the economy. In particular, the existence of endogenous credit standards increases output volatility by 21%. We also suggest three alternative tools for policymakers to dampen the effects of endogenous credit standards on macroeconomic volatility. First, the introduction of credit growth to the monetary policy succeeds in counteracting the fluctuation of lending, and thus decreasing the additional volatility considerably. Second, the exchange rate augmented monetary policy, if well-constructed, is considered an efficient tool to eliminate most of the additional fluctuations caused by deep habits in the banking sector. Finally, the introduction of the foreign interest augmented policy also proves successful in dampening the effect of endogenous movements in lending standards.

Ginkgo biloba seeds were dried in microwave drier under different microwave powers (200, 280, 460, and 640 W) to determinate the drying kinetics and color changes during drying process. Drying curves of all samples showed a long constant rate period and falling rate period along with a short heating period. The effective moisture diffusivities were found to be 3.318 × 10−9 to 1.073 × 10−8 m2/s within the range of microwave output levels and activation energy was 4.111 W/g. The L⁎ and b⁎ values of seeds decreased with drying time. However, a⁎ value decreased firstly and then increased with the increase of drying time. Artificial neural network (ANN) modeling was employed to predict the moisture ratio and color parameters (L⁎, a⁎, and b⁎). The ANN model was trained for finite iteration calculation with Levenberg-Marquardt algorithm as the training function and tansig-purelin as the network transfer function. Results showed that the ANN methodology could precisely predict experimental data with high correlation coefficient (0.9056–0.9834) and low mean square error (0.0014–2.2044). In addition, the established ANN models can be used for online prediction of moisture content and color changes of ginkgo biloba seeds during microwave drying process.

One of the various sorts of damage to asphalt concrete is cracking. Repeated loads, the deterioration or aging of material combinations, or structural factors can contribute to the development of cracks. Asphalt concrete’s crack resistance is represented by the CT index. 107 CT Index data samples from the University of Transport Technology’s lab are measured. These data samples are used to establish a database from which a Machine Learning (ML) model for predicting the CT Index of asphalt concrete can be built. For creating the highest performing machine learning model, three well-known machine learning methods are introduced: Random Forest (RF), K-Nearest Neighbors (KNN), and Multivariable Adaptive Regression Spines (MARS). Monte Carlo simulation is used to verify the accuracy of the ML model, which includes the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The RF model is the most effective ML model, according to analysis and evaluation of performance indicators. By SHAPley Additive exPlanations based on RF model, the input Aggregate content passing 4.75 mm sieve (AP4.75) has a significant effect on the variation of CT Index value. In following, the descending order is Reclaimed Asphalt Pavement content (RAP) > Bitumen content (BC) > Flash point (FP) > Softening point > Rejuvenator content (RC) > Aggregate content passing 0.075mm sieve (AP0.075) > Penetration at 25°C (P). The results study contributes to selecting a suitable AI approach to quickly and accurately determine the CT Index of asphalt concrete mixtures.

Nanocrystalline celluloses (NCCs) were isolated from different cellulose sources such as wood (softwood and hardwood), non-wood plant (cotton linters and cattail), and marine pulp (red algae) by acid hydrolysis. The NCCs were compared with respect to their dimensions, shapes, degrees of polymerization, crystallinities, thermal stabilities, and effects on the properties of bio-composites. Self-assembly phenomena of the NCCs were observed by electron microscopy. The NCCs from red algae fibers had the longest length (~432 nm) and the highest aspect ratio among the five cellulose sources. The NCCs from cotton linters, cattail fibers, and red algae fibers showed greater thermal degradation resistance than those from wood fibers. The NCCs with much lower molecular weights than their starting materials showed much higher crystalline indices than their starting ones. All-cellulose bio-composites, where the prepared NCCs were used as filaments and the dissolved cellulose as matrix, displayed increased Young’s moduli in proportion to the added amount of the NCCs.

This study explores the potential application of lignin nanoparticles and chitosan–lignin nanoparticles (CLNs) as hydrophobic barrier coatings for paperboard. The lignin nanoparticles were initially prepared using a mixed solvent of ethanol and acetone. Their characteristics were examined via scanning electron microscopy (SEM) and dynamic light scattering, which revealed particle sizes in the range of 180–400 nm. The results indicated that the coatings with pure lignin nanoparticles failed to impart hydrophobicity to the paperboard, whereas the CLN coatings significantly enhanced hydrophobicity and reduced water absorption. The water contact angle increased from 109° to over 128° after the first CLN coating, remained at 127° with the second and third coating layers, and was maintained at 119° with four layers. Multilayer coatings were applied to improve barrier performance; however, no further enhancement in hydrophobicity was observed. The CLN-coated paper exhibited a significantly improved surface smoothness, as confirmed by SEM. The results indicate that a single-layer CLN coating is effective for imparting water-barrier properties to paperboard. In contrast, the coating with pure lignin nanoparticles resulted in cracked surfaces and inconsistent coating thicknesses.

When illuminated by a laser, plasmonic nanoparticles immersed in water can very quickly and strongly heat up, leading to the nucleation of so-called plasmonic vapor bubbles. While the long-time behavior of such bubbles has been well-studied, here, using ultrahigh-speed imaging, we reveal the nucleation and early life phase of these bubbles. After some delay time from the beginning of the illumination, a giant bubble explosively grows, and collapses again within 200 μs (bubble life phase 1). The maximal bubble volume Vmax remarkably increases with decreasing laser power, leading to less total dumped energy E. This dumped energy shows a universal linear scaling relation with Vmax , irrespective of the gas concentration of the surrounding water. This finding supports that the initial giant bubble is a pure vapor bubble. In contrast, the delay time does depend on the gas concentration of the water, as gas pockets in the water facilitate an earlier vapor bubble nucleation, which leads to smaller delay times and lower bubble nucleation temperatures. After the collapse of the initial giant bubbles, first, much smaller oscillating bubbles form out of the remaining gas nuclei (bubble life phase 2). Subsequently, the known vaporization dominated growth phase takes over, and the bubble stabilizes (life phase 3). In the final life phase 4, the bubble slowly grows by gas expelling due to heating of the surrounding. Our findings on the explosive growth and collapse during the early life phase of a plasmonic vapor bubble have strong bearings on possible applications of such bubbles.