Electronic structure modification of metal phthalocyanines by a carbon nanotube support for efficient oxygen reduction to hydrogen peroxide.

An active and selective two-electron oxygen reduction reaction (2e- ORR) is required for efficient electrosynthesis of H2O2. This reaction can be promoted by metal phthalocyanines (MPcs), which serve as model catalysts with well-defined structures. MPc molecules have mostly been evaluated on conductive carbon-based substrates, including glassy carbon (GC) and carbon nanotubes (CNTs), yet their influence on the electrocatalytic properties is not well understood. This study demonstrated that the ORR activity per surface area was improved by up to 4-fold with MPc molecules supported on CNTs (MPc/CNTs, M = Co, Mn, and Fe) compared to MPc loaded directly on GC. Ultraviolet photoelectron spectroscopy and density functional theory calculations revealed that the CNTs modified the electronic structure of the MPc molecules to optimize the *OOH binding energy and boost the heterogeneous electron transfer rates. Detailed kinetic analysis enabled multiple reaction pathways to be decoupled to extract the metal-dependent intrinsic 2e-/4e- ORR activities. Finally, MPc/CNT catalysts were employed in an H2O2 electrosynthesis flow cell, which delivered an industrial-scale current density of -200 mA cm-2 and an H2O2 faradaic efficiency of 88.7 ± 0.6% with the CoPc/CNT catalyst in a neutral electrolyte.

Physicochemical and sensory characteristics of gluten-free noodles added with chicken breast meat: a comparative study with wheat flour noodle.

The rising health consciousness of consumers has resulted in multiple studies on the use of animal and vegetable proteins in gluten-free noodle production, but chicken breast meat (CBM) has not been the subject of such studies. Thus, we aimed to create protein-fortified gluten-free noodles using economical and nutritious CBM and compare their quality attributes with commonly used wheat flour noodles (WN). Among the CBM noodles (CN), CN with tapioca starch (CN-T) showed the highest sensory and textural similarity to WN. The color values of cooked noodles were not considerably different. The water absorption capacity and volume expansion ratio of CN-T were not significantly different from those of WN. In CNs, an ungelatinized microstructure was observed, and CN-T displayed well-formed structural bonds related to adhesiveness, similar to WN. The CN-T had a protein content about 2% higher than WN. This finding is informative for the development of gluten-free noodles using CBM.

Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning.

The electrochemical carbon dioxide reduction reaction (CO2RR) is an attractive approach for mitigating CO2 emissions and generating value-added products. Consequently, discovery of promising CO2RR catalysts has become a crucial task, and machine learning (ML) has been utilized to accelerate catalyst discovery. However, current ML approaches are limited to exploring narrow chemical spaces and provide only fragmentary catalytic activity, even though CO2RR produces various chemicals. Here, by merging pre-developed ML model and a CO2RR selectivity map, we establish high-throughput virtual screening strategy to suggest active and selective catalysts for CO2RR without being limited to a database. Further, this strategy can provide guidance on stoichiometry and morphology of the catalyst to researchers. We predict the activity and selectivity of 465 metallic catalysts toward four expected reaction products. During this process, we discover previously unreported and promising behavior of Cu-Ga and Cu-Pd alloys. These findings are then validated through experimental methods.

Influence of Gas for Thermal Treatment on Hydrogen Permeation in V-Ni Alloy Membranes.

Hydrogen separation is an important step for the utilization of hydrogen energy. Metallic alloys, such as vanadium-nickel, are potential hydrogen separation materials. Due to the strong propensity of vanadium to form oxides and hydrides, vanadium alloy has a lower hydrogen permeability, and it is difficult to maintain the permeability over time. Therefore, special preparation processes such as Pd coating have been suggested for hydrogen separation vanadium-based membranes. However, aside from the prohibitive price of palladium, the interdiffusion of palladium and vanadium makes the coated membrane inviable to be used at a high temperature. Thermal treatment with inert gas was investigated in this study to assess the applicability of the vanadium alloy without palladium coating for hydrogen separation and clarify the mechanism behind the thermal treatment. Argon is inert with vanadium and displayed permeability recovery after 43 h thermal treatment, but the permeability declined under certain conditions. In contrast, nitrogen is known to interact with vanadium and the hydrogen permeability was maintained at a level lower than the test with argon. Given that nitrogen can compete with hydrogen for the active sites on vanadium, nitrogen might hinder hydrogen adsorption and hydride formation, whereas argon reduced the partial pressure of hydrogen during the thermal treatment, enhancing the driving force of hydrogen desorption. In the X-ray diffraction spectrum, vanadium hydrides and oxides were confirmed after hydrogen permeation and thermal treatment. In the X-ray photoelectron spectroscopy data, oxygen was a dominant element due to vanadium oxides and adsorbed nitrogen was also observed. According to binding energy shifts of nitrogen, nitrogen used for thermal treatment might substitute or compete for active sites with adsorbed nitrogen and hydrogen, existing in vanadium lattice. Although thermal treatment can be used to recover hydrogen permeability, the alloy cannot be recovered as hydrogen-free. However, results demonstrate the potential of thermal treatment to complement an uncoated vanadium alloy for a hydrogen separation membrane.

Transition Metal Ion Doping on ZIF-8 Enhances the Electrochemical CO2 Reduction Reaction.

The electrochemical reduction of CO2  to diverse value-added chemicals is a unique, environmentally friendly approach for curbing greenhouse gas emissions while addressing sluggish catalytic activity and low Faradaic efficiency (FE) of electrocatalysts. Here, zeolite-imidazolate-frameworks-8 (ZIF-8) containing various transition metal ions-Ni, Fe, and Cu-at varying concentrations upon doping are fabricated for the electrocatalytic CO2 reduction reaction (CO2 RR) to carbon monoxide (CO) without further processing. Atom coordination environments and theoretical electrocatalytic performance are scrutinized via X-ray absorption spectroscopy (XAS) and density functional theory (DFT) calculations. Upon optimized Cu doping on ZIF-8, Cu0.5 Zn0.5 /ZIF-8 achieves a high partial current density of 11.57 mA cm-2 and maximum FE for CO of 88.5% at -1.0 V (versus RHE) with a stable catalytic activity over 6 h. Furthermore, the electron-rich sp2 C atom facilitates COOH* promotion after Cu doping of ZIF-8, leading to a local effect between the zinc-nitrogen (Zn-N4 ) and copper-nitrogen (Cu-N4 ) moieties. Additionally, the advanced CO2 RR pathway is illustrated from various perspectives, including the pre-H-covered state under the CO2 RR. The findings expand the pool of efficient metal-organic framework (MOF)-based CO2 RR catalysts, deeming them viable alternatives to conventional catalysts.

N-Step Pre-Training and Décalcomanie Data Augmentation for Micro-Expression Recognition.

Facial expressions are divided into micro- and macro-expressions. Micro-expressions are low-intensity emotions presented for a short moment of about 0.25 s, whereas macro-expressions last up to 4 s. To derive micro-expressions, participants are asked to suppress their emotions as much as possible while watching emotion-inducing videos. However, it is a challenging process, and the number of samples collected tends to be less than those of macro-expressions. Because training models with insufficient data may lead to decreased performance, this study proposes two ways to solve the problem of insufficient data for micro-expression training. The first method involves N-step pre-training, which performs multiple transfer learning from action recognition datasets to those in the facial domain. Second, we propose Décalcomanie data augmentation, which is based on facial symmetry, to create a composite image by cutting and pasting both faces around their center lines. The results show that the proposed methods can successfully overcome the data shortage problem and achieve high performance.

SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map.

Modern data augmentation strategies such as Cutout, Mixup, and CutMix, have achieved good performance in image recognition tasks. Particularly, the data augmentation approaches, such as Mixup and CutMix, that mix two images to generate a mixed training image, could generalize convolutional neural networks better than single image-based data augmentation approaches such as Cutout. We focus on the fact that the mixed image can improve generalization ability, and we wondered if it would be effective to apply it to a single image. Consequently, we propose a new data augmentation method to produce a self-mixed image based on a saliency map, called SalfMix. Furthermore, we combined SalfMix with state-of-the-art two images-based approaches, such as Mixup, SaliencyMix, and CutMix, to increase the performance, called HybridMix. The proposed SalfMix achieved better accuracies than Cutout, and HybridMix achieved state-of-the-art performance on three classification datasets: CIFAR-10, CIFAR-100, and TinyImageNet-200. Furthermore, HybridMix achieved the best accuracy in object detection tasks on the VOC dataset, in terms of mean average precision.

Increased hepatic Fatty Acid uptake and esterification contribute to tetracycline-induced steatosis in mice.

Tetracycline induces microvesicular steatosis, which has a poor long-term prognosis and a higher risk of steatohepatitis development compared with macrovesicular steatosis. Recent gene expression studies indicated that tetracycline treatment affects the expression of many genes associated with fatty acid transport and esterification. In this study, we investigated the role of fatty acid transport and esterification in tetracycline-induced steatosis. Intracellular lipid accumulation and the protein expression of fatty acid translocase (FAT or CD36) and diacylglycerol acyltransferase (DGAT) 2 were increased in both mouse liver and HepG2 cells treated with tetracycline at 50 mg/kg (intraperitoneal injection, i.p.) and 100 µM, respectively. Tetracycline increased the cellular uptake of boron-dipyrromethene-labeled C16 fatty acid, which was abolished by CD36 RNA interference. Oleate-induced cellular lipid accumulation was further enhanced by co-incubation with tetracycline. Tetracycline downregulated extracellular signal-regulated kinase (ERK) phosphorylation, which negatively regulated DGAT2 expression. U0126, a specific ERK inhibitor, also increased DGAT2 expression and cellular lipid accumulation. DGAT1 and 2 knock-down with specific small interfering (si)-RNA completely abrogated the steatogenic effect of tetracycline in HepG2 cells. Taken together, our data showed that tetracycline induces lipid accumulation by facilitating fatty acid transport and triglyceride esterification by upregulating CD36 and DGAT2, respectively.