Porous Carbon Microparticles from Phenylenediamine-Mellitic Acid Resin for Energy Storage Devices

Department of Energy EngineeringIn part 1, porous carbon has been prepared in diverse synthetic methods and utilized for energy storage devices. We present carbon sphere (CS), a type of porous carbon, by carbonization of phenylenediamine-mellitic acid (PDA-MA) resin at high temperature annealing process. Sphere-shape PDA-MA resin is readily synthesized with the mixture of m-PDA and mellitic acid through hydrothermal condensation without additional reagents. We found that the CS from the resin has high porosity (575 m2/g) and a substantial nitrogen content (~7%). The CS was tested for anode of Li-ion battery and showed cycle retention of 274 mAh/g after 400 cycles. In addition, rate capability test of the CS revealed fast kinetics and 82% capability retention from 2C to 10C. Showing a chance for further improvement on coulombic efficiency by higher temperature carbonization, this research gave possibility for the CS in energy storage application. In part 2, the performance of fiber-reinforced composites is governed not only by the nature of each individual component comprising the composite, but also by the interfacial properties between the fiber and the matrix. We present a novel layer-by-layer (LbL) assembly for the surface modification of glass fiber to enhance the interfacial properties between the glass fiber and epoxy matrix. Solution-processable graphene oxide (GO) and an aramid nanofiber (ANF) were employed as active components for the LbL assembly onto the glass fiber owing to their abundant functional groups and mechanical properties. We found that the interfacial properties of the glass fibers uniformly coated with GO and ANF multilayers, such as surface free energy and interfacial shear strength, were improved by 23.6% and 39.2%, respectively, compared with those of the bare glass fiber. In addition, the interfacial adhesion interactions between the glass fiber and epoxy matrix were highly tunable simply by changing the composition and the architecture of layers, taking the advantages of versatility of the LbL assembly.ope

SeiT: Storage-Efficient Vision Training with Tokens Using 1% of Pixel Storage

We need billion-scale images to achieve more generalizable and ground-breaking vision models, as well as massive dataset storage to ship the images (e.g., the LAION-4B dataset needs 240TB storage space). However, it has become challenging to deal with unlimited dataset storage with limited storage infrastructure. A number of storage-efficient training methods have been proposed to tackle the problem, but they are rarely scalable or suffer from severe damage to performance. In this paper, we propose a storage-efficient training strategy for vision classifiers for large-scale datasets (e.g., ImageNet) that only uses 1024 tokens per instance without using the raw level pixels; our token storage only needs <1% of the original JPEG-compressed raw pixels. We also propose token augmentations and a Stem-adaptor module to make our approach able to use the same architecture as pixel-based approaches with only minimal modifications on the stem layer and the carefully tuned optimization settings. Our experimental results on ImageNet-1k show that our method significantly outperforms other storage-efficient training methods with a large gap. We further show the effectiveness of our method in other practical scenarios, storage-efficient pre-training, and continual learning. Code is available at https://github.com/naver-ai/seitComment: ICCV 2023; First two authors contributed equally; code url: https://github.com/naver-ai/seit; 17 pages, 1.2M

Hybrid multilayer thin film supercapacitor of graphene nanosheets with polyaniline: importance of establishing intimate electronic contact through nanoscale blending

A hybrid electrode consisting of an electric double-layer capacitor of graphene nanosheets and a pseudocapacitor of the conducting polymer polyaniline exhibits a synergistic effect with excellent electrochemical performance for flexible thin film supercapacitors. This hybrid supercapacitor is constructed by a nanoscale blending method of layer-by-layer (LbL) assembly based on the electrostatic interactions between positively charged polyaniline (PANi) and negatively charged graphene oxide (GO) nanosheets. The hybrid electrode provides not only improved electronic conductivity through the intimate contact with the graphene nanosheet, but also enhanced chemical stability during the charge-discharge process. We also investigated the dependence of the electrochemical performance on the various parameters of LbL assembly such as the number of bilayers and the post-thermal and chemical treatments that could affect the degree of reduction of GO and PANi. We found that after thermal treatment, the LbL-assembled thin film of PANi with GO nanosheets exhibited an excellent gravimetric capacitance of 375.2 F g(-1) at a discharge current density of 0.5 A g(-1) that outperformed many other hybrid supercapacitors reported to date. The hybrid supercapacitor maintained its capacity up to 90.7% over 500 cycles at a high current density of 3.0 A g(-1). This study opens up the possibility for the production of diverse graphene-based hybrid nanocomposites that are promising for future flexible supercapacitors.close413

Electrically focus-tuneable ultrathin lens for high-resolution square subpixels.

Owing to the tremendous demands for high-resolution pixel-scale thin lenses in displays, we developed a graphene-based ultrathin square subpixel lens (USSL) capable of electrically tuneable focusing (ETF) with a performance competitive with that of a typical mechanical refractive lens. The fringe field due to a voltage bias in the graphene proves that our ETF-USSL can focus light onto a single point regardless of the wavelength of the visible light-by controlling the carriers at the Dirac point using radially patterned graphene layers, the focal length of the planar structure can be adjusted without changing the curvature or position of the lens. A high focusing efficiency of over 60% at a visible wavelength of 405 nm was achieved with a lens thickness of <13 nm, and a change of 19.42% in the focal length with a 9% increase in transmission was exhibited under a driving voltage. This design is first presented as an ETF-USSL that can be controlled in pixel units of flat panel displays for visible light. It can be easily applied as an add-on to high resolution, slim displays and provides a new direction for the application of multifunctional autostereoscopic displays

Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching

Knowledge distillation extracts general knowledge from a pretrained teacher network and provides guidance to a target student network. Most studies manually tie intermediate features of the teacher and student, and transfer knowledge through predefined links. However, manual selection often constructs ineffective links that limit the improvement from the distillation. There has been an attempt to address the problem, but it is still challenging to identify effective links under practical scenarios. In this paper, we introduce an effective and efficient feature distillation method utilizing all the feature levels of the teacher without manually selecting the links. Specifically, our method utilizes an attention based meta network that learns relative similarities between features, and applies identified similarities to control distillation intensities of all possible pairs. As a result, our method determines competent links more efficiently than the previous approach and provides better performance on model compression and transfer learning tasks. Further qualitative analyses and ablative studies describe how our method contributes to better distillation

Quantification of Microbial Growth Pattern on Growth Medium Using Image Processing Program and Evaluation of Antifungal Activities of Wood Vinegar

Digitized growth pattern images of wood-rotting fungi Fomitopsis palustris (FOM) and Trametes versicolor (TRA) were used with a video processing program with a new formula to calculate the numerical results of microbial inhibition by an active substance (wood vinegar). First, images of daily growth pattern were collected to generate calibration curves for each fungus. FOM stopped growing in 7 days, whereas TRA stopped growing in 11 days. The calibration curves FOM and TRA showed R2 = 0.9899 and 0.8880, respectively. The new methods allow numerical comparison by adding different concentrations of active components (wood vinegar) to assess the inhibition effect of microbial growth. Using the formula allowed the results of inhibition effect to be collected as numerical data. Through the image data, it was possible to present daily inhibitory efficacy data in detail, numerically. Based on these results, microbial growth pattern could be evaluated

Online knowledge validation with prudence analysis in a document management application

Prudence analysis (PA) is a relatively new, practical and highly innovative approach to solving the problem of brittleness in knowledge based system (KBS) development. PA is essentially an online validation approach where as each situation or case is presented to the KBS for inferencing the result is simultaneously validated. Therefore, instead of the system simply providing a conclusion, it also provides a warning when the validation fails. Previous studies have shown that a modification to multiple classification ripple-down rules (MCRDR) referred to as rated MCRDR (RM) has been able to achieve strong and flexible results in simulated domains with artificial data sets. This paper presents a study into the effectiveness of RM in an eHealth document monitoring and classification domain using human expertise. Additionally, this paper also investigates what affect PA has when the KBS developer relied entirely on the warnings for maintenance. Results indicate that the system is surprisingly robust even when warning accuracy is allowed to drop quite low. This study of a previously little touched area provides a strong indication of the potential for future knowledge based system development. © 2011 Elsevier Ltd. All rights reserved

Effects of Ca and Ce Addition on Tensile and Fracture Properties in Squeeze Cast AT42(Mg-4Al-2Sn) Magnesium Alloys

The effects of the addition of Ca and Ce to the AT42(Mg-4Al-2Sn) alloy on the microstructural modification and deformation, as well as the fracture mechanisms of squeeze cast magnesium alloys, were investigated in this study. Microstructural analyses indicated that the AT42 alloy contained Mg17Al12 and Mg2Sn particles precipitated along cell boundaries, whereas long, needle-shaped CaMgSn particles were precipitated additionally in the AT42-0.5Ca and AT42-1Ca alloys. In the AT42-1Ca-0.5Ce and AT42-1Ca-1Ce alloys containing Al11Ce3 particles as well as Mg17Al12, Mg2Sn, and CaMgSn particles, the overall distribution of precipitates was homogeneously modified considerably as the solidification cell size was refined. According to the observation of deformation and the fracture processes of the AT42-1Ca alloys, the fracture proceeded mainly along cracked, needle-shaped CaMgSn particles at a relatively low stress-intensity factor level. However, in the AT42-1Ca-1Ce alloys, the deformation and fracture proceeded into cells rather than into cell boundaries as twins were developed actively inside cells, although few microcracks were initiated at the precipitates. Thus, the AT42-1Ca-1Ce alloy had the highest strength, ductility, and fracture toughness simultaneously because of the increase in the volume fraction of hard precipitates and the development of many twins in the Mg matrix.open113sciescopu

Characterization of a Translucent Material Produced from <i>Paulownia tomentosa</i> Using Peracetic Acid Delignification and Resin Infiltration

Paulownia tomentosa, a tree species that allows for efficient production of translucent wood, was selected as an experimental wood species in this study, and a two-step process of delignification and polymer impregnation was performed. For delignification, 2–4 mm thick specimens were immersed in peracetic acid for 8 h. The delignified-wood specimens were impregnated using epoxy, a commercial transparent polymer. To identify the characteristics of the resulting translucent wood, the transmittance and haze of each type of wood section (cross- and tangential) were measured, while bending strength was measured using a universal testing machine. The translucent wood varied in properties according to the wood section, and the total transmittance and haze were 88.0% and 78.5% for the tangential section and 91.3% and 96.2% for the cross-section, respectively. For the bending strength, untreated wood showed values of approximately 4613.5 MPa modulus of elasticity (MOE), while the epoxy impregnation to improve the strength of the wood had increased the MOE up to approximately 6089.9 MPa, respectively. A comparative analysis was performed in this study with respect to the substitution of balsa, which is used widely in the production of translucent wood. The results are anticipated to serve as baseline data for the functionalization of translucent wood