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Ecofriendly Polymer-Graphene-Based Conductive Ink for Multifunctional Printed Electronics
No full textThe ongoing research on printed and flexible electronics is primarily focused on conductive three-dimensional (3D) print patterning. However, due to the nonhomogeneous distribution of conductive elements in a polymer matrix and their tendency to shrink, 3D-printed patterns often suffer from low-printing accuracies and poor mechanical and electrical properties. Here, poly(vinyl butyral-co-vinyl alcohol-co-vinyl acetate) (PVBVA) is reinforced with microwave-exfoliated graphene to develop a conductive ink for 3D printing. Compared with the pure PVBVA patterns, the PVBVA/graphene patterns exhibited a high-electrical conductivity, a twofold enhancement in tensile strength, an improved printing accuracy, and a high stability because of the graphene addition. The PVBVA/graphene inks flowed well during the printing; loading of up to 0.1 wt% graphene in the PVBVA gel resulted in notable changes in the rheological properties of the ink. The printed conductive patterns showed a high flexibility suitable for wearable electronics. Additionally, multifunctional electronic operations such as photoinduced heating, temperature sensing, and motion sensing are possible, and this study may pave the way for the development of a new class of smart wearable electronics for healthcare and soft robotics
Reducing the CIE colorimetric matching failure on wide color gamut displays
No full textColor matching experiments were conducted for 11 pairs of displays, using 7 displays with different spectral characteristics. The color matching results between the LCD display and displays that have a narrowband spectrum, such as a laser projector, QLED, or OLED, demonstrated a significant color difference between two matched colors. The maximum difference was 18.52 increment E00, which indicates the white color difference between the LCD and laser projector. There was also a clear observer variability of 2.27 increment E00. The new cone fundamental function derived from 757 metameric pairs showed good performance compared to CIE standard observers reducing the display color mismatching significantly. This function also demonstrated a better performance when evaluating color matching in color chart image
Formation and degradation of strongly reducing cyanoarene-based radical anions towards efficient radical anion-mediated photoredox catalysis
No full textCyanoarene-based photocatalysts are widely used due to their catalytic performance, but the formation of the active species and its potential degradation pathways are poorly understood. Here, the authors investigate these pathways under commonly-used photoredox-mediated reaction conditions. Cyanoarene-based photocatalysts (PCs) have attracted significant interest owing to their superior catalytic performance for radical anion mediated photoredox catalysis. However, the factors affecting the formation and degradation of cyanoarene-based PC radical anion (PC center dot-) are still insufficiently understood. Herein, we therefore investigate the formation and degradation of cyanoarene-based PC center dot- under widely-used photoredox-mediated reaction conditions. By screening various cyanoarene-based PCs, we elucidate strategies to efficiently generate PC center dot- with adequate excited-state reduction potentials (E-red*) via supra-efficient generation of long-lived triplet excited states (T-1). To thoroughly investigate the behavior of PC center dot- in actual photoredox-mediated reactions, a reductive dehalogenation is carried out as a model reaction and identified the dominant photodegradation pathways of the PC center dot-. Dehalogenation and photodegradation of PC center dot- are coexistent depending on the rate of electron transfer (ET) to the substrate and the photodegradation strongly depends on the electronic and steric properties of the PCs. Based on the understanding of both the formation and photodegradation of PC center dot-, we demonstrate that the efficient generation of highly reducing PC center dot- allows for the highly efficient photoredox catalyzed dehalogenation of aryl/alkyl halides at a PC loading as low as 0.001 mol% with a high oxygen tolerance. The present work provides new insights into the reactions of cyanoarene-based PC center dot- in photoredox-mediated reactions
Spatial and temporal variations of the PM2.5 concentrations in Hanoi metropolitan area, Vietnam, during the COVID-19 lockdown
No full textThe Vietnamese government has issued several directives of lockdown in Hanoi, the capital city, for a month (1-30 April 2020) to prevent the human-to-human transmission of COVID-19. This action has affected air pollution due to a decline in transportation. Therefore, this study investigates spatial and temporal change in the PM2.5 concentrations during the first 4 months of 2020 in Hanoi metropolitan area. Spatial distribution maps of the PM2.5 concentration in Hanoi were provided for the first time. The average PM2.5 concentrations at the 22 air monitoring stations were strongly correlated with the population. April had a significantly lower level of PM2.5 than the other 3 months. In particular, the concentrations of PM2.5 and NO2 decreased by 12 and 54%, respectively, between March and April, especially at areas for commercial activities. In April, a higher level of PM2.5 was recorded between Tuesday-Thursday, which is a reverse trend with that in March. Furthermore, Monday and Friday did not show rush hour peaks for the PM2.5 levels in April. A decrease in the PM2.5 concentrations was partly influenced by the long-range transport from the outside to Hanoi. This study implies that a reduction in traffic volumes and public activities may possibly improve the air quality
Tailoring the density of carbon nanotube networks through chemical self-assembly by click reaction for reliable transistors
No full textSemiconducting single-walled carbon nanotubes (sc-SWNTs) are attracting considerable interest for use as active layers in various electronic applications such as field-effect transistors (FETs) because of their extremely high intrinsic charge carrier mobility and solution processability at low costs. However, it is challenging to achieve a constant sc-SWNT density for ensuring commercial-level, uniform performance in FETs based on random -network SWNT films formed by solution processing. This paper reports a facile method for sorting sc-SWNT and precisely controlling the density of random-network sc-SWNT films by azide-functionalized polymer. The chemical self-assembly of SWNTs is performed between azide-functionalized polymer-wrapped sc-SWNTs and alkyne-based substrate via click reaction. A high-purity sc-SWNT ink is obtained by the conjugated polymer wrapping method using an azide-functionalized polyfluorene in methylcyclohexane. The sc-SWNTs are then chemically bound to a substrate with an alkyne adhesive layer through a Cu-catalyzed azide-alkyne cycloaddi-tion reaction. FETs with dense and uniform SWNT films with a linear density of 30 (+/- 2) tubes mu m- 1 exhibit markedly high hole mobility of up to 25.4 cm2 V-1 s-1 and excellent performance uniformity. Furthermore, the SWNT films anchored on the substrates are highly resistant to exogenous disruptions, such as sonication in organic solvents, leading the great potential for applications such as biosensors that require strong adhesive strength
A holistic review on how artificial intelligence has redefined water treatment and seawater desalination processes
No full textIn the modern era, deep learning (DL), and machine learning (ML), have emerged as potential technologies that are widely applied in the fields of science, engineering, and technology. These tools have been extensively used to optimize seawater desalination and water treatment processes to achieve efficient performance. Indeed, automation has played a key role in redefining the issues of water treatment and seawater desalination. Artificial intelligence (AI) has been developed as a versatile tool for processing data and optimizing smart water services while addressing the issues of monitoring, management, and labor costs. Recently, specific AI tools, such as artificial neural networks (ANNs) and genetic algorithms, have been implemented for self-monitoring and modeling applications in the field of water treatment and seawater desalination. In the present article, the application of AI in the water treatment and seawater desalination sectors is thoroughly reviewed. Additionally, conventional modeling approaches are compared with ANN modeling. Furthermore, the challenges and shortcomings are discussed, along with future prospects. Moreover, the applications of AI mechanisms in data processing, optimization, modeling, prediction, and decision-making during water treatment and seawater desalination processes are underscored. Finally, innovative trends in seawater desalination and water treatment with AI tools are summarized
A key role of orientation in the coding of visual motion direction
No full textDespite the fundamental importance of visual motion processing, our understanding of how the brain represents basic aspects of motion is incomplete. While it is generally believed that direction is the main representational feature of motion, motion processing is also influenced by nondirectional orientation signals that are present in most motion stimuli. Here, we aimed to test whether this nondirectional motion axis contributes motion perception even when orientation is completely absent from the stimulus. Using stimuli with and without orientation signals, we found that serial dependence in a simple motion direction estimation task was predominantly determined by the orientation of the previous motion stimulus. Moreover, the observed attraction profiles closely matched the characteristic pattern of serial attraction found in orientation perception. Evidently, the sequential integration of motion signals strongly depends on the orientation of motion, indicating a fundamental role of nondirectional orientation in the coding of visual motion direction
Training-Free Stuck-at Fault Mitigation for ReRAM-based Deep Learning Accelerators
No full textAlthough Resistive RAMs can support highly efficient matrix-vector multiplication, which is very useful for machine learning and other applications, the non-ideal behavior of hardware such as stuck-at fault and IR drop is an important concern in making ReRAM crossbar array-based deep learning accelerators. Previous work has addressed the nonideality problem through either redundancy in hardware, which requires a permanent increase of hardware cost, or software retraining, which may be even more costly or unacceptable due to its need for a training dataset as well as high computation overhead. In this paper we propose a very light-weight method that can be applied on top of existing hardware or software solutions. Our method, called FPT (Forward-Parameter Tuning), takes advantage of a certain statistical property existing in the activation data of neural network layers, and can mitigate the impact of mild nonidealities in ReRAM crossbar arrays for deep learning applications without using any hardware, a dataset, or gradientbased training. Our experimental results using MNIST, CIFAR-10, CIFAR-100, and ImageNet datasets in binary and multibit networks demonstrate that our technique is very effective, both alone and together with previous methods, up to 20rate, which is higher than even some of the previous remapping methods. We also evaluate our method in the presence of other nonidealities such as variability and IR drop. Further, we provide an analysis based on the concept of effective fault rate, which not only demonstrates that effective fault rate can be a useful tool to predict the accuracy of faulty RCA-based neural networks, but also explains why mitigating the SAF problem is more difficult with multi-bit neural networks
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No full textHuman iPS-derived blood-brain barrier model exhibiting enhanced barrier properties empowered by engineered basement membrane
No full textThe basement membrane (BM) of the blood-brain barrier (BBB), a thin extracellular matrix (ECM) sheet underneath the brain microvascular endothelial cells (BMECs), plays crucial roles in regulating the unique physiological barrier function of the BBB, which represents a major obstacle for brain drug delivery. Owing to the difficulty in mimicking the unique biophysical and chemical features of BM in in vitro systems, current in vitro BBB models have suffered from poor physiological relevance. Here, we describe a highly ameliorated human BBB model accomplished by an ultra-thin ECM hydrogel-based engineered basement membrane (nEBM), which is supported by a sparse electrospun nanofiber scaffold that offers in vivo BM-like microenvironment to BMECs. BBB model reconstituted on a nEBM recapitulates the physical barrier function of the in vivo human BBB through ECM mechano-response to physiological relevant stiffness (???500???kPa) and exhibits high efflux pump activity. These features of the proposed BBB model enable modelling of ischemic stroke, reproducing the dynamic changes of BBB, immune cell infiltration, and drug response. Therefore, the proposed BBB model represents a powerful tool for predicting the BBB permeation of drugs and developing therapeutic strategies for brain diseases