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A novel broadband reflectarray antenna employing equivalent magnetic dipole elements
Nature-inspired hierarchical building materials with low CO2 emission and superior performance
Abstract Conventional cement-based materials are faced with significant challenges, including large carbon emissions, high density, and quasi-brittleness. Here, inspired by hierarchical porous structures existing in nature, we develop a low carbon, lightweight, strong and tough cement-based material (LLST), which is obtained by a rapid gelation of hydrogel as skeleton and subsequent deposition of cement hydrates as a skin. As a result, the LLST exhibits hierarchical structure consisting of sponge-like micropores (1 ~ 50 μm) and nanopores (5 ~ 100 nm), without detrimental macropores that compromise light weight, strength, and toughness. Compared with the normal cement paste, LLST displays a 54% reduction in density, 145% and 1365% improvement in specific compressive strength and fracture energy, with only 51% carbon emission. These properties are further investigated with machine learning force field molecular dynamics along with well-tempered metadynamics simulations, indicating that strong chemical bonding is generated at the atomic level between functional groups in the hydrogel and Ca ion released from cement hydration. These findings not only demonstrate a strategy for developing lightweight building materials with low-carbon emission and remarkable mechanical properties, but also provide valuable insights for realizing the coexistence of light weight, strength and toughness by tailoring the hierarchical pore structure.
Evolution of strain field and crack prediction in cemented paste backfill specimens based on digital image correlation and computer vision recognition model
Covalent organic framework without cocatalyst loading for efficient photocatalytic sacrificial hydrogen production from water
Predicting the academic achievement of students using black hole optimization and Gaussian process regression
Abstract Academic achievement is vital for campus life and education since it indicates the caliber of the teachers, administration, and students’ learning abilities. Issues such as poor study conditions and family disruptions can impede a student’s capacity to achieve. Teachers are looking for practical solutions to these concerns because solving problems one at a time might be tough. This study uses a combination of black hole optimization (BHO) and Gaussian process regression (GPR) algorithms to predict students’ academic success in higher education. The method is divided into three stages: data pre-processing, identification of effective indicators using BHO algorithms, and forecasting of academic performance. The presented approach makes use of the GPR algorithm to choose the relevant features and the weighted combination of GPR models to forecast that the GPR model would be used for the weighting operation that is, to determine the ideal weights. The experimental findings demonstrate that our method has a lower error rate of 0.95 and 0.81 in terms of RMSE and MAE than the competing methods. The proposed method can assist teachers in analyzing student behavioral patterns, understanding academic performance impact mechanisms, and developing effective learning supervision plans.
Modulating competitive adsorption of hybrid self-assembled molecules for efficient wide-bandgap perovskite solar cells and tandems
Development and effectiveness verification of AI education data sets based on constructivist learning principles for enhancing AI literacy
Energy-ordered resource stratification as an agnostic signature of life
Abstract The search for extraterrestrial life hinges on identifying biosignatures, often focusing on gaseous metabolic byproducts as indicators. However, most such biosignatures require assuming specific metabolic processes. It is widely recognized that life on other planets may not resemble that of Earth, but identifying biosignatures “agnostic” to such assumptions has remained a challenge. Here, we propose a novel approach by considering the generic outcome of life: the formation of competing ecosystems. We use a minimal model to argue that the presence of ecosystem-level dynamics, characterized by ecological interactions and resource competition, may yield biosignatures independent of specific metabolic activities. Specifically, we propose the emergent stratification of chemical resources in order of decreasing energy content as a candidate new biosignature. While likely inaccessible to remote sensing, this signature could be relevant for sample return missions, or for detection of ancient signatures of life on Earth itself.
Upconversion luminescence and temperature sensing characteristics of MgAl2O4 spinel doped with Er3+ and Yb3+ ions
Type III interferon primes pDCs for TLR7 activation and antagonizes immune suppression mediated by TGF-β and PGE2
Gadolinium based contrast agent induced electrical conductivity heterogeneity analysis in the brain of Alzheimer’s disease
Visualizing the internal structure of the charge-density-wave state in CeSbTe
Abstract The collective reorganization of electrons into a charge density wave has long served as a textbook example of an ordered phase in condensed matter physics. Two-dimensional square lattices with p electrons are well-suited to the realization of charge density waves, due to the anisotropy of the p orbitals and the resulting one dimensionality of the electronic structure. In spite of a long history of study of charge density waves in square-lattice systems, few reports have recognized the significance of a hidden orbital degree of freedom. The degeneracy of p x and p y electrons may give rise to orbital patterns in real space that endow the charge density wave with additional broken symmetries or unusual order parameters. Here, we use scanning tunneling microscopy to visualize the internal structure of the charge-density-wave state of CeSbTe, which contains Sb square lattices with 5p electrons. We image atomic-sized, anisotropic lobes of charge density with periodically modulating anisotropy, which we interpret in terms of a superposition of p x and p y bond density waves. Our results support the fact that delocalized p orbitals can reorganize into emergent electronic states of matter.