Energy-efficient CO2 hydrogenation with fast response using photoexcitation of CO2 adsorbed on metal catalysts.

Many heterogeneous catalytic reactions occur at high temperatures, which may cause large energy costs, poor safety, and thermal degradation of catalysts. Here, we propose a light-assisted surface reaction, which catalyze the surface reaction using both light and heat as an energy source. Conventional metal catalysts such as ruthenium, rhodium, platinum, nickel, and copper were tested for CO2 hydrogenation, and ruthenium showed the most distinct change upon light irradiation. CO2 was strongly adsorbed onto ruthenium surface, forming hybrid orbitals. The band gap energy was reduced significantly upon hybridization, enhancing CO2 dissociation. The light-assisted CO2 hydrogenation used only 37% of the total energy with which the CO2 hydrogenation occurred using only thermal energy. The CO2 conversion could be turned on and off completely with a response time of only 3 min, whereas conventional thermal reaction required hours. These unique features can be potentially used for on-demand fuel production with minimal energy input

Singlet Fermionic Dark Matter with Dark ZZ

We present a fermionic dark matter model mediated by the hidden gauge boson. We assume the QED-like hidden sector which consists of a Dirac fermion and U(1)$_X$ gauge symmetry, and introduce an additional scalar electroweak doublet field with the U(1)$_X$ charge as a mediator. The hidden U(1)$_X$ symmetry is spontaneously broken by the electroweak symmetry breaking and there exists a massive extra neutral gauge boson in this model which is the mediator between the hidden and visible sectors. Due to the U(1)$_X$ charge, the additional scalar doublet does not couple to the Standard Model fermions, which leads to the Higgs sector of type I two Higgs doublet model. The new gauge boson couples to the Standard Model fermions with couplings proportional to those of the ordinary $Z$ boson but very suppressed, thus we call it the dark $Z$ boson. We study the phenomenology of the dark $Z$ boson and the Higgs sector, and show the hidden fermion can be the dark matter candidate.Comment: 10 pages, 3 figure

An Empirical Examination of Consumer Behavior for Search and Experience Goods in Sentiment Analysis

With the explosive increase of user-generated content such as product reviews and social media, sentiment analysis has emerged as an area of interest. Sentiment analysis is a useful method to analyze product reviews, and product feature extraction is an important task in sentiment analysis, during which one identifies features of products from reviews. Product features are categorized by product type, such as search goods or experience goods, and their characteristics are totally different. Thus, we examine whether the classification performance differs by product type. The findings show that the optimal threshold varies by product type, and simply decreasing the threshold to cover many features does not guarantee improvement of the classification performance

Improving Generalization of Drowsiness State Classification by Domain-Specific Normalization

Abnormal driver states, particularly have been major concerns for road safety, emphasizing the importance of accurate drowsiness detection to prevent accidents. Electroencephalogram (EEG) signals are recognized for their effectiveness in monitoring a driver's mental state by monitoring brain activities. However, the challenge lies in the requirement for prior calibration due to the variation of EEG signals among and within individuals. The necessity of calibration has made the brain-computer interface (BCI) less accessible. We propose a practical generalized framework for classifying driver drowsiness states to improve accessibility and convenience. We separate the normalization process for each driver, treating them as individual domains. The goal of developing a general model is similar to that of domain generalization. The framework considers the statistics of each domain separately since they vary among domains. We experimented with various normalization methods to enhance the ability to generalize across subjects, i.e. the model's generalization performance of unseen domains. The experiments showed that applying individual domain-specific normalization yielded an outstanding improvement in generalizability. Furthermore, our framework demonstrates the potential and accessibility by removing the need for calibration in BCI applications.Comment: Submitted to 2024 12th IEEE International Winter Conference on Brain-Computer Interfac

Effects of nanofluids containing graphene/graphene-oxide nanosheets on critical heat flux

The superb thermal conduction property of graphene establishes graphene as an excellent material for thermal management. In this paper, we selected graphene/graphene oxide nanosheets as the additives in nanofluids. The authors interestingly found that the highly enhanced critical heat flux (CHF) in the nanofluids containing graphene/graphene-oxide nanosheets (GON) cannot be explained by both the improved surface wettability and the capillarity of the nanoparticles deposition layer. Here we highlights that the GON nanofluid can be exploited to maximize the CHF the most efficiently by building up a characteristically ordered porous surface structure due to its own self-assembly characteristic resulting in a geometrically changed critical instability wavelength.open363