Highly Stretchable and Sensitive Strain Sensors Using Fragmentized Graphene Foam

Stretchable electronics have recently been extensively investigated for the development of highly advanced human‐interactive devices. Here, a highly stretchable and sensitive strain sensor is fabricated based on the composite of fragmentized graphene foam (FGF) and polydimethylsiloxane (PDMS). A graphene foam (GF) is disintegrated into 200–300 μm sized fragments while maintaining its 3D structure by using a vortex mixer, forming a percolation network of the FGFs. The strain sensor shows high sensitivity with a gauge factor of 15 to 29, which is much higher compared to the GF/PDMS strain sensor with a gauge factor of 2.2. It is attributed to the great change in the contact resistance between FGFs over the large contact area, when stretched. In addition to the high sensitivity, the FGF/PDMS strain sensor exhibits high stretchability over 70% and high durability over 10 000 stretching‐releasing cycles. When the sensor is attached to the human body, it functions as a health‐monitoring device by detecting various human motions such as the bending of elbows and fingers in addition to the pulse of radial artery. Finally, by using the FGF, PDMS, and μ‐LEDs, a stretchable touch sensor array is fabricated, thus demonstrating its potential application as an artificial skin.     

Tuning the pH-responsiveness capability of poly(acrylic acid-co-itaconic acid)/NaOH hydrogel: Design,swelling, and rust removal evaluation

The swelling behavior of poly(acrylic acid-co-itaconic acid)/NaOH hydrogel as well as its ability for iron and copper rust removal was studied and established for the first time. Through an experimental design, the influence of the synthesis parameters on hydrogel response was determined. It was found that pH-responsiveness dependence of hydrogel determines its application. In alkaline media, the hydrogel acted as superabsorbent, while in acidic, the most outstanding property was its pickling capability that allowed to clean carbon steel and copper metallic surfaces. Infrared, thermogravimetry, and scanning electron microscopy were performed to determine copolymer formation, thermal properties, and morphology. Metallic crystallographic phases formed during the corrosion processes were determined by X-ray diffractometer. Hydrogel adhesiveness followed by diffusion and dissolution of metal oxides species was identified as the main steps in the rust removal mechanism. This method offers a new, environmentally friendly perspective to eliminate corrosion from metallic surfaces compared with traditional strategies. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48403.     

Neurogenic Interventions for Fear Memory via Modulation of the Hippocampal Function and Neural Circuits

Fear memory helps animals and humans avoid harm from certain stimuli and coordinate adaptive behavior. However, excessive consolidation of fear memory, caused by the dysfunction of cellular mechanisms and neural circuits in the brain, is responsible for post-traumatic stress disorder and anxiety-related disorders. Dysregulation of specific brain regions and neural circuits, particularly the hippocampus, amygdala, and medial prefrontal cortex, have been demonstrated in patients with these disorders. These regions are involved in learning, memory, consolidation, and extinction. These are also the brain regions where new neurons are generated and are crucial for memory formation and integration. Therefore, these three brain regions and neural circuits have contributed greatly to studies on neural plasticity and structural remodeling in patients with psychiatric disorders. In this review, we provide an understanding of fear memory and its underlying cellular mechanisms and describe how neural circuits are involved in fear memory. Additionally, we discuss therapeutic interventions for these disorders based on their proneurogenic efficacy and the neural circuits involved in fear memory.     

N‐Arylsulfonyl Indolines as Retinoic Acid Receptor‐Related Orphan Receptor γ (RORγ) Agonists

The nuclear retinoic acid receptor‐related orphan receptor γ (RORγ; NR1F3) is a key regulator of inflammatory gene programs involved in T helper 17 (T_H17) cell proliferation. As such, synthetic small‐molecule repressors (inverse agonists) targeting RORγ have been extensively studied for their potential as therapeutic agents for various autoimmune diseases. Alternatively, enhancing T_H17 cell proliferation through activation (agonism) of RORγ may boost an immune response, thereby offering a potentially new approach in cancer immunotherapy. Herein we describe the development of N‐arylsulfonyl indolines as RORγ agonists. Structure–activity studies reveal a critical linker region in these molecules as the major determinant for agonism. Hydrogen/deuterium exchange coupled to mass spectrometry (HDX‐MS) analysis of RORγ–ligand complexes help rationalize the observed results.     

Dispersant‐free dyeing of poly(lactic acid) fabric with temporarily solubilised disperse dyes from azopyridone derivatives

Poly(lactic acid) fibre is derived from annually renewable crops and known to be 100% compostable. In order to extend its environmental friendliness into the dyeing process, dispersant‐free dyeing of poly(lactic acid) fabric with three temporarily solubilised azo disperse dyes based on hydroxypyridone moiety containing a β‐sulphatoethylsulphonyl group was investigated. The dyes were successfully applied to poly(lactic acid) fabric without the use of dispersants. The colour yields of the dyes on poly(lactic acid) fabric were observed to be dependent on dyebath pH and dyeing temperature. The optimum results were obtained at pH 4–5 and 110 °C. One of the dyes showed a colour yield as good as that of a commercial disperse dye and good build‐up on poly(lactic acid) fabric. All of the dyes could be alkali cleared owing to ionisation of the dye under mild alkaline conditions. Wash fastness was good to very good, and light fastness was good. The chemical oxygen demand levels of the poly(lactic acid) dyeing effluent from the dyes were considerably lower than those from a commercial disperse dye.     

Dweb model: Representing Web 2.0 dynamism

Due to the increase in the number and popularity of applications such as e-commerce or on-line booking systems, typical of the Web 2.0, dynamic contents are becoming more and more frequent. This trend suggests the review of widely accepted paradigms and models for the World Wide Web. As a system that is continuously changing, both in the offered applications and in its infrastructure, performance evaluation studies are a main concern to provide sound proposals when designing new web-related systems. Although the dynamism in the workload characterization has also been tackled in previous research, it has not been modeled in a precise way yet because of its complex nature.In this paper we propose the Dweb model which represents the dynamism of current web applications in the workload characterization. Dweb is based on three main concepts that allow to model dynamic workload: navigation, workload test and workload distribution. In addition, a dynamic workload generator has been implemented to show the practical application of the proposed model, which has been illustrated through a case study.     

Importance Sampling for Objective Function Estimations in Neural Detector Training Driven by Genetic Algorithms

To train Neural Networks (NNs) in a supervised way, estimations of an objective function must be carried out. The value of this function decreases as the training progresses and so, the number of test observations necessary for an accurate estimation has to be increased. Consequently, the training computational cost is unaffordable for very low objective function value estimations, and the use of Importance Sampling (IS) techniques becomes convenient. The study of three different objective functions is considered, which implies the proposal of estimators of the objective function using IS techniques: the Mean-Square error, the Cross Entropy error and the Misclassification error criteria. The values of these functions are estimated by IS techniques, and the results are used to train NNs by the application of Genetic Algorithms. Results for a binary detection in Gaussian noise are provided. These results show the evolution of the parameters during the training and the performances of the proposed detectors in terms of error probability and Receiver Operating Characteristics curves. At the end of the study, the obtained results justify the convenience of using IS in the training.