A relativistic quantum approach to neutrino and antineutrino emission via the direct Urca process in strongly magnetized neutron-star matter
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The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. Further, new modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.
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As countries accelerate their energy transitions, understanding how renewable energy (RE) systems structurally integrate into national economies is essential. This study presents a longitudinal economic input-output (EIO) analysis of the renewable energy sector in South Korea from 2016 to 2022. We develop a novel EIO-based framework that disaggregates the RE sector both by energy source (thermal, hydro, nuclear and renewable) and by industrial function (manufacturing, generation, and services), allowing for a detailed assessment of production dynamics, value-added creation, and import dependency. By quantifying backward and forward linkages and induced economic effects, the analysis reveals persistent structural vulnerabilities in renewable manufacturing and increasing sectoral interdependencies. Results reveal that while the renewable energy sector's production and value-added shares have increased, critical segments remain highly import-dependent, particularly in equipment manufacturing. The analysis highlights systemic gaps in domestic supply chain resilience and offers sector-specific insights for reducing vulnerability and enhancing energy security. Although applied to South Korea as a case study, the proposed framework is designed to be transferable to other national contexts where renewable energy planning requires economic structural insights. The findings offer policy-relevant guidance for enhancing domestic energy resilience and aligning industrial strategy with long-term decarbonization goals.
Energy assessments can provide an effective way of identifying and implementing energy efficiency measures to save energy costs and avoid emissions throughout manufacturing facilities. Consequently, it generates significant economic and environmental benefits to localities, states, and the nation. Quantifying these benefits requires a systematic techno-economic-environmental framework for capturing the interactions. This article employs methodologies to improve the energy efficiency of small and medium-size industries through their sustainable industrial motor systems. Motor systems offer large opportunities to enhance energy efficiency through adopting advanced technologies and better-informed operations. Case studies presented illuminate the potential savings and impacts from implementing sustainable motor systems and the importance of energy assessments. Here, the integrated macro-economic analysis quantifies the regional sustainability impacts of implementing the industrial energy efficiency offered by an energy auditing program in Ohio over a ten-year period. Results show that implementing all the center’s motor recommendations have directly saved $\$702$ M in energy costs, avoided 2.7 million metric tons of carbon dioxide emissions, and created 3,445 jobs, resulting in a total annual economic impact of $\$788$ M stemming from direct, indirect, and induced regional economic impacts. It provides policy implications for encouraging sustainable industrial motor systems.
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The frequency and intensity of summer heat waves in East Asia have increased sharply in recent decades, significantly impacting public health and the economy. The Arctic-Siberian Plain (ASP) teleconnection pattern has been identified as a key driver, with ASP warming amplifying atmospheric circulation patterns conducive to extreme temperatures. This study evaluates the ability of Coupled Model Inter-comparison Project phase 6 models to simulate the ASP pattern across interannual variability (IAV) and intra-seasonal variability (ISV) timescales using the Common Basis Function method. The multi-model mean shows statistically significant pattern correlations with ERA5 reanalysis, with correlation coefficients of 0.90 and 0.99 for IAV and ISV, respectively. While the ASP pattern is generally well captured, models exhibit substantial inter-model diversity in the intensity and position of anticyclonic anomalies over the ASP and East Asia. Models with ASP pattern variability similar to reanalysis better reproduce extreme East Asian temperatures, whereas those over- or underestimating ASP variability exhibit lower skill. These performance differences are related to differences in simulating key variables associated with the development of the ASP pattern. Our findings highlight the role of the ASP pattern in modulating extreme heat events, as models with improved ASP simulations align more closely with observed temperature extremes. Refining ASP representations in models could enhance seasonal heat wave predictions, improving climate adaptation strategies.
Highlights: • DYRK1A is essential to maintain cancer stemness in OSCC. • DYRK1A regulates cancer stemness via FGF2 expression. • Levels of DYRK1A and FGF2 expression are positively correlated in OSCC cells. • DYRK1A-FGF2 is a novel molecular axis regulating cancer stemness in OSCC. DYRK1A, one of the dual-specificity tyrosine phosphorylation-regulated kinases (DYRKs), plays an important role in various biological processes by regulating downstream targets via kinase-dependent and independent mechanisms. Here, we report a novel role of DYRK1A in maintaining tumor growth and stemness of oral/oropharyngeal squamous cell carcinoma (OSCC) cells. Deletion of DYRK1A from OSCC cells abrogated their in vivo tumorigenicity and self-renewal capacity, the key features of cancer stem-like cells (CSCs; also referred to as tumor-initiating cells). The DYRK1A deletion also induced the suppression of CSC populations and properties, such as migration ability and chemoresistance. Conversely, ectopic expression of DYRK1A in OSCC cells augmented their CSC phenotype. Among five DYRK members (DYRK1A, 1B, 2, 3, and 4), DYRK1A is the most dominantly expressed kinase, and its expression is upregulated in OSCC compared to normal oral epithelial cells. More importantly, DYRK1A was highly enriched in various CSC-enriched OSCC populations compared to their corresponding non-CSC populations, indicating its pivotal role in cancer progression and stemness. Further, our study revealed that fibroblast growth factor 2 (FGF2) is a key regulator in the DYRK1A-mediated CSC regulation. Functional studies demonstrated that the loss of DYRK1A inhibits CSC phenotype via reduction of FGF2. Overexpression of DYRK1A promotes CSC phenotype via upregulation of FGF2. Our study delineates a novel mechanism of cancer stemness regulation by DYRK1A-FGF2 axis in OSCC. Thus, inhibition of DYRK1A would lead to a potential novel therapeutic option for targeting CSCs in OSCC.
Chirality is ubiquitous in the universe and in living creatures over detectable length scales from the subatomic to the galactic, as exemplified in the two extremes by subatomic particles (neutrinos) and spiral galaxies. Between them are living creatures that display multiple levels of chirality emerging from hierarchically assembled asymmetric building blocks. Not too far from the bottom of this pyramid are the foundational building blocks with chiral atomic centers on sp 3 carbon atoms exemplified by L-amino acids and D-sugars that are self-assembled into higher-order structures with increasing dimensions forming highly complex, amazingly functional, and energy-efficient living systems. The organization and materials employed in their construction inspired scientists to replicate complex living systems via the self-assembly of chiral components. Multiple studies pointed to unexpected and unique electromagnetic properties of chiral structures with nanoscale and microscale dimensions, including giant circular dichroism and collective circularly polarized scattering that their constituent units did not possess. To address the wide variety of chiral geometries observed in continuous materials, singular particles, and their complex systems, multiple analytic techniques are needed. Simultaneously, their spectroscopic properties create a pathway to multiple applications. For example, mirror-asymmetric vibrations at chiral centers formed by sp 3 carbon atoms lead to optical activity for the infrared (IR) wavelength regions. At the same time, understanding the optical activity in, for example, the IR region enables biomedical applications because multiple modalities of biomedical imaging and vibrational optical activity (VOA) of biomolecules are known for IR range. In turn, VOA can be realized in both absorption and emission modalities due to large magnetic transition moments, as vibrational circular dichroism (VCD) or Raman optical activity (ROA) spectroscopy. In addition to the VOA, in the range of longer wavelengths, lattice vibrational mode or phononic behavior occurs in chiral crystals and nanoassemblies, which can be readily detected by terahertz circular dichroism (TCD) spectroscopy. Meanwhile, chiral self-assembly can induce circularly polarized light emission (CPLE) regardless of the existence of chirality in coassembled fluorophores. The CPLE from self-assembled chiral materials is particularly interesting because the CPLE can originate from both circularly polarized luminescence and circularly polarized scattering (CPS). Furthermore, because self-assembled nanostructures often exhibit stronger optical activity than their building blocks owing to dimension and resonance effects, the optical activity of single assembled nanostructures can be investigated by using microscopic technology combined with chiral optics. Here, we describe the state of the art for spectroscopic methods for the comprehensive analysis of chiral nanomaterials at various photon wavelengths, addressed with special attention given to new tools emerging both for materials with self-organized hierarchical chirality and single-particle spectroscopy.
Confinement of guest molecules in porous materials such as metal organic frameworks (MOFs) promises to deliver emergent properties separate from those of the individual components. Understanding the confinement mechanism is therefore important for the development of new synthesis routes that adjust MOF properties for specific applications. In this work, we developed a new synthetic method to confine guest molecules into MOF pores through a stepwise thermal annealing process, wherein the confinement of 7,7,8,8-tetracyanoquinodimethane (TCNQ) guest molecules into Cu 3 (BTC) 2 (BTC = benzene-1,3,5-tricarboxylic acid) MOF host is used as an example of how novel materials can be created with new physical properties. The stepwise thermal annealing process includes 1) an activation process of pristine Cu 3 (BTC) 2 MOF to maximizes the TCNQ guest loading in the MOF host by effectively removing the residual solvents and 2) post-annealing of the TCNQ infiltrated MOF to enhances the interaction of the confined guest molecules with the MOF host. Obtained experimental results based on thermogravimetric analysis, N 2 gas adsorption, electron microscopy, X-ray diffraction and infrared absorption, combined with density functional theory calculations provide evidence that the use of a stepwise thermal annealing process yields enhancements in the guest loading, packing and interaction between the TCNQ guest and the MOF host. The new hybrid TCNQ@Cu 3 (BTC) 2 system is stable and shows no significant signs of structural degradation even after submersion in water. This is due to the presence of significantly stronger interactions of TCNQ with the frame-work metal ions compared to those of the water molecules competing for the same framework binding sites. It was also found that TCNQ@Cu 3 (BTC) 2 system maintains a significant CO 2 and CH 4 adsorption potential compared to the pristine MOF. The synthetic route developed in this work yields novel guest@MOF hybrid systems that will be useful for many MOF-based applications such as gas separations and chemical sensors performed under humid conditions.
Layered double hydroxides (LDHs) are a class of cationic-layered solids that can be synthetically designed for a variety of advanced functions. Facile thin film growth of LDHs is an important requisite for a variety of applications including functional coatings, displays, and sensing. In this work we demonstrate, for the first time, an in-situ and patternable thin film synthesis of interconnected Zn-Cr LDH particles from a transparent conducting oxide (TCO) precursor, aluminum-doped zinc oxide (AZO), at room temperature within minutes. Synthetic parameters such as chromium (III) nitrate concentration, solvent composition, and reaction time were found to significantly affect the thickness and morphology of the resulting LDH films. These LDH thin films can undergo interlayer anion exchange, which modulates the interlayer distance of the LDH sheets and surface energy of the thin film. Replacement of the interlayer anion with perfluorooctanoate increases the interlayer sheet distance from 0.9 nm to 2.8 nm and induces a super-hydrophobic thin film that is capable of adsolubilizing and retaining organic guest molecules. The synthetic method and structural analysis of the LDH thin films introduced in this work opens new avenues of application for LDH films.
ACS Spring 2022 National Meeting, Virtual, March 20-24, 2022
A group of copper iodide-based hybrid semiconductors with the general formula of 2D-CuI(L) 0.5 (L = organic ligands) are synthesized and structurally characterized. All compounds are two-dimensional (2D) networks made of one-dimensional (1D) copper iodide staircase chains that are interconnected by bidentate nitrogen-containing ligands. Results from optical absorption and emission experiments and density functional theory (DFT) calculations reveal that their photoluminescence (PL) can be systematically tuned by adjusting the lowest unoccupied molecular orbital (LUMO) energies of the organic ligands. Charge carrier transport measurements were carried out for the first time on single crystals of selected 2D-CuI(L) 0.5 structures, and the results show that they possess p-type conductivity with a Hall mobility of ~1 cm 2 V -1 s -1 for 2D-CuI(pm) 0.5 and 0.13 cm 2 V -1 s -1 for 2D-CuI(pz) 0.5 , respectively. These values are comparable to or higher than the mobilities of typical highly luminescent organic semiconductors. Finally, this work suggests that robust, high-dimensional copper iodide hybrid semiconductors are promising candidates to be considered as a new type of emissive layer for light-emitting diode (LED) devices.