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Polymer adhesion promoters were formulated to improve the adhesion strength of lead frame/epoxy composites. The poly(itaconic acid-co-acrylamide) CIAcAAM) was synthesized via free radical polymerization to improve the interfacial adhesion between Ni and Cu lead frames and epoxy composites. IAcAAM was synthesized by setting itaconic acid (IA) and acrylamide (AAM) at molar ratios of 7:3, 5:5. and 3:7. The interfacial adhesion mechanism was investigated by introducing IAcAAM with three different comonomer molar ratios. The adhesion strength between EMC and Cu and Ni substrates to which each IAcAAM was applied was measured using a universal testing machine, and the effect of IAcAAM with various LA:AAM molar ratios on the surface energy of EMC was closely studied. As a result, the adhesion properties of Ni lead frame/epoxy composite and Cu lead frame/epoxy composite were improved as small amounts of all IAcAAM samples were added to the epoxy mixture. We confirmed that IAcAAM with an appropriate molar ratio can be used in the EMC process and has the potential to increase the adhesion of epoxy composites and metal lead frames. Highlights * A polymeric adhesive promoter was synthesized to enhance the adhesion strength of lead frame/epoxy composites. * Poly(itaconic acid-co-acrvlamide) (IAcAAM) was copolymeriz,ed from itaconic acid and acrylamide for this purpose. * The adhesion strength of lead frame/epoxy composites was compared for different IAcAAM monomer contents and with a commercial adhesive promoter. * IAcAAM showed superior adhesion strength in the Ni lead frame/epoxy composite material. * The highest adhesion strength was achieved at a monomer composition of 5:5. KEYWORDS adhesion promoter, adhesive strength, epoxy composite, epoxy molding compound, lead frame, 1 | INTRODUCTION An epoxy molding compound (EMC) is a composite material composed ol' ingredients such as silica, epoxy resin, phenolic resin, carbon black, and flame retardants. This compound is [...]

Semi-interpenetrating polymer network (semi-IPN) technology was introduced in order to improve the gel strength and salt resistance of itaconic acid (IA)-based superabsorbent polymers (SAPs). A semi-IPN SAP based on IA and 2-acrylamido-2-methyl-l-propanesulfonic acid (AMPS) was synthesized. Polymerization was performed using a one-pot method in an aqueous solution in the presence of polyvinyl alcohol (PVA). The structural and thermal properties of PVA/poly(IA-co-AMPS) were studied using Fourier transform infrared, differential scanning calorimetry, and thermogravimetry analysis. PVA formed strong hydrogen bonds with hydrophilic functional groups in the SAP network to improve the gel strength and thermal stability of the SAP. A PVA content of 6 wt% increased the centrifuge retention capacity and absorbency under load of the SAP to 60.1 and 16.4 g/g, respectively. Owing to the less salt-sensitive hydroxyl groups of PVA, semi-IPN SAP showed higher absorption (up to 26.3%) in an aqueous salt solution. The resulting SAP has a very low concentration of residual monomers (167.4 ppm). Experimental results suggest that PVA/poly(IA-co-AMPS) is an eco-friendly SAP with excellent absorption properties, making it suitable for application in personal care products. KEYWORDS 2-acrylamido-2-methyl-l-propanesulfonic add, itaconic acid, polyvinyl alcohol, semi-interpenetrating polymer network, superabsorbent polymer, 1 | INTRODUCTION Superabsorbent polymers (SAPs) are hydrophilic and polymeric networks capable of absorbing large amounts of water or body fluids. [1] Their main applications are diapers, sanitary napkins, and [...]

In this study, 2.0 mol% CuO-added 0.96(K0.5Na0.5)(Nb1−zSbz)O3–0.04(Bi0.5Ag0.5)ZrO3 [CKN(N1−zSz)–BAZ] piezoceramics (0.0 ≤ z ≤ 0.08) were densified at 980 °C without the secondary phase. The crystal structure of the CKN(N1−zSz)–BAZ piezoceramics changed from the tetragonal–orthorhombic structure to the tetragonal–orthorhombic–pseudocubic structure with increasing z. The CKN(N1−zSz)–BAZ piezoceramics (0.0 ≤ z ≤ 0.07) were well textured at 980 °C along the [001] direction, without holes, when 3.0 mol% Na(Nb0.9Sb0.1)O3 (NNS) templates were used. However, the specimens textured using NaNbO3 (NN) templates exhibited holes owing to the dissolution of the NN templates. Moreover, the elastic modulus and hardness of the specimen (z = 0.05) textured using the NNS template were larger than those of the specimen (z = 0.05) textured using the NN template, possibly because of the absence of holes. The [001]-textured CKN(N1−zSz)–BAZ piezoceramics exhibited a reduced dielectric constant and enhanced piezoelectric properties. Particularly, the [001]-textured CKN(N0.95S0.05)–BAZ piezoceramic provides large d33 (610 pC N−1), kp (0.68) and d33 × g33 (30 × 10−12 m2 N−1) values. A cantilever-shaped piezoelectric energy harvester was fabricated using the [001]-textured CKN(N0.95S0.05)–BAZ ceramic, which showed a large output power of 1.6 mW at the resonance frequency, indicating that this piezoceramic is a good candidate for a piezoelectric energy harvester. [ABSTRACT FROM AUTHOR]

The channel is a crucial component of the polymer electrolyte membrane fuel cell (PEMFC). Since the channel can change the reactant transfer capability, water removal capability, and distribution of the reactant, it affects the performance and durability of PEMFCs. This study investigated the effects of obstacles in the serpentine-type flow channel on the performance of PEMFCs by computational fluid dynamics (CFD). The height of the obstacles was varied to analyze the electrochemical performances of the fuel cells. In addition, the depth of the flow channel was varied to compare the performances of the PEMFCs. To better represent the real-world tendency, the agglomerate model and the Forchheimer inertial effect were used. The results showed that changes in the channel depth caused greater performance improvements compared to the installation of obstacles, due to the enhanced mass transfer and improved water removal. However, the results for the installation of obstacles showed the lower non-uniformity of the current density and a reduced pressure drop compared to the changes in the channel depth, offering advantages in terms of flooding, the fuel cell life, and the operating cost. [ABSTRACT FROM AUTHOR]

With recent advancements in deep learning, spoofing techniques have developed and generative adversarial networks (GANs) have become an emerging threat to finger-vein recognition systems. Therefore, previous research has been performed to generate finger-vein images for training spoof detectors. However, these are limited and researchers still cannot generate elaborate fake finger-vein images. Therefore, we develop a new densely updated contrastive learning-based self-attention generative adversarial network (DCS-GAN) to create elaborate fake finger-vein images, enabling the training of corresponding spoof detectors. Additionally, we propose an enhanced convolutional network for a next-dimension (ConvNeXt)-Small model with a large kernel attention module as a new spoof detector capable of distinguishing the generated fake finger-vein images. To improve the spoof detection performance of the proposed method, we introduce fractal dimension estimation to analyze the complexity and irregularity of class activation maps from real and fake finger-vein images, enabling the generation of more realistic and sophisticated fake finger-vein images. Experimental results obtained using two open databases showed that the fake images by the DCS-GAN exhibited Frechet inception distances (FID) of 7.601 and 23.351, with Wasserstein distances (WD) of 18.158 and 10.123, respectively, confirming the possibility of spoof attacks when using existing state-of-the-art (SOTA) frameworks of spoof detection. Furthermore, experiments conducted with the proposed spoof detector yielded average classification error rates of 0.4% and 0.12% on the two aforementioned open databases, respectively, outperforming existing SOTA methods for spoof detection. [ABSTRACT FROM AUTHOR]

Existing research on image-based plant classification has demonstrated high performance using artificial intelligence algorithms. However, limited camera viewing angles can cause parts of the plant to be invisible in the acquired images, leading to an inaccurate classification. However, this issue has not been addressed by previous research. Hence, our study aims to introduce a method to improve classification performance by taking these limitations into account; specifically, we incorporated both segmentation and classification networks structured as shallow networks to expedite the processing times. The proposed shallow plant segmentation network (Shal-PSN) performs adversarial learning based on a discriminator network; and a shallow plant classification network (Shal-PCN) with applied residual connections was also implemented. Moreover, the fractal dimension estimation is used in this study for analyzing the segmentation results. Additionally, this study evaluated the performance of the proposed Shal-PSN that achieved the dice scores (DSs) of 87.43% and 85.71% with PlantVillage and open leaf image (OLID-I) open datasets, respectively, in instances where 40–60% of plant parts were missing. Moreover, the results demonstrate that the proposed method increased the classification accuracy from 41.16% to 90.51% in the same instances. Overall, our approach achieved superior performance compared to the existing state-of-the-art classification methods. [ABSTRACT FROM AUTHOR]