Energy Theory Research Articles

Quantum potential energy: Adaptive facades in architecture

This paper addressed the topic of creating quantum architecture through human awareness to transform the formal and spatial elements of architecture according to its environmental sustainability compatible with nature. The research emphasizes the creation of a built environment that uses natural resources to enhance the psychological and physical comfort of humans, relying on the quantum energy inherent in the interaction of particles, which is called “kinetic energy” in quantum mechanics, and it can be considered energy due to motion. The research aims to determine the recommended standards for use in designing the building facade according to the magnetic field lines. It focuses on the classification of smart interfaces in particular. The complexity of evaluating adaptive or dynamic facades is related to evaluating the performance of the facade elements and systems, the overall performance of the building, as well as the behavior and satisfaction of the occupants. The research seeks to present a conceptual and cognitive framework by linking the concept of potential kinetic energy and quantum theory with architecture and then presents a group of literary studies that will enrich and build the theoretical framework and then apply it to the groups of projects selected for practical study and then reach the final conclusions.

Enhancing Interface Performance Through Self-Assembly Mechanisms of APTES on Surface-Modified Tuff Aggregates

To enhance the adhesion between tuff and asphalt, this study investigates the efficacy of alkalinization treatment technology using a molecular self-assembly layer derived from the silane-coupling agent γ-aminopropyltriethoxysilane (APTES). APTES hydrolysis solutions at varying concentrations were prepared to assess their impact on the adhesive strength of the aggregate–asphalt interface and water damage resistance. Using surface energy theory, the interface adhesion work of tuff was analyzed, while SEM and EDS were employed to examine changes in surface morphology and composition after treatment. The results demonstrate that an APTES:water:ethanol mass ratio of 5:45:50, along with a curing temperature of 200 °C, significantly improves the bonding strength between tuff and asphalt. The silanol groups on APTES react with hydroxyl groups on the tuff surface to form siloxane bonds (Si-O-Si), anchoring APTES to the tuff. This study elucidates the self-assembly mechanisms of APTES on tuff aggregates and demonstrates the consequent enhancement of interfacial adhesion, providing valuable insights for the application of tuff as tunnel spoil in road engineering.

Classifying Modified Gravity and Dark Energy Theories with Bayesian Neural Networks: Massive Neutrinos, Baryonic Feedback, and the Theoretical Error

Classifying Modified Gravity and Dark Energy Theories with Bayesian Neural Networks: Massive Neutrinos, Baryonic Feedback, and the Theoretical Error

Study on the Adhesion Performance of Biochar-Modified Asphalt Based on Surface Free Energy and Atomic Force Microscopy

To investigate the effect of biochar on the adhesion performance of asphalt, the macroscopic and microscopic adhesion performance of 70# base asphalt, SBS-modified asphalt (SBSMA), sludge-based biochar-modified asphalt, and waste wood-based biochar-modified asphalt (WWBMA) were tested using atomic force microscopy (AFM) and contact angle tests, respectively. The impact of these two testing methods on the evaluation of adhesion performance was also analyzed. Research results indicated that biochar increased the number of bee-like structures on the asphalt surface while significantly reducing their average area. This improves the distribution of asphalt adhesion by reducing the adhesion difference between bee-like structured areas and non-bee-like structured areas while simultaneously enhancing the overall adhesion of the asphalt surface. Surface free energy (SFE) theory analysis indicates a linear correlation between the SFE obtained from the contact angle test and the atomic force microscopy test. Biochar significantly increases the SFE of asphalt and its components, thereby increasing the work of adhesion between asphalt and aggregate and reducing the work of debonding. Consequently, it improves the bonding performance between asphalt and aggregate, as well as its resistance to moisture damage.

Research on bridge safety early warning method based on strain energy theory and health monitoring data

Bridges are technology-intensive and heavily invested permanent infrastructure. After completion and opening to traffic, bridge structures are easy to be affected by factors such as traffic load and atmospheric environment. Therefore, it is necessary to do safety warning and evaluation of bridges, especially the abnormal behavior in the early stages of bridge operation. In this research paper, a large-span continuous rigid frame bridge installed with the health monitoring system (HMS), of which a large amount of health monitoring data are collected by the HMS, is used as an example, and then a bridge safety early warning method is proposed when the bridge is during early operating period. First of all, the research finding that the internal stress of the prototype bridge obeys normal distribution through statistical analysis is used; next, we deduce that the strain energy inside the prototype bridge is subject to the Non-central Chi-square distribution combined with the strain energy and statistical theories; in the end, the key probability density distribution function of strain energy and its parameters are derived by using the key stress distribution function of the high performance concrete C50 strength grade used in the prototype bridge. The method recommended in this paper is conducive to the formulation of bridge preventive maintenance strategies.

Drivers of wind and water erosion for river sediments in a typical coarse sandy area in the middle reaches of the Yellow River

Warmer temperatures and the combined effects of wind and water erosion leads to serious soil loss. Identifying the contribution of different drivers to wind and water compound erosion can improve soil erosion management in the watershed. Accordingly, we calculated the erosion energy based on energy theory and applied the mutation test and trend analysis to explore environmental drivers, runoff, and sediment changes for the Kuyehe River Basin. Rainfall erosion energy, wind erosion energy, air temperature, NDVI, and land cover were selected as potential drivers to quantify the contribution of sediment change using a partial least squares regression model. The results indicated that rainfall erosion energy decreased, wind erosion energy increased and then decreased, temperature increased, and NDVI increased during the period 1990–2020. The relationship between runoff and sediment transport in the basin shifted from more water and more sediment to more water and less sediment, and the timing of the mutation was different for runoff and sediment, with the runoff mutation occurring in 1997 and the sediment mutation occurring during 2003–2004. After sediment mutations, wind, NDVI, and land cover of forests showed higher importance for sediment transport changes, while precipitation and temperature were less important. In addition, the importance of cropland, shrub and grassland, and other land cover types varied considerably between sub-basins. Over 70 % of the contribution to sediment change was due to land cover change, while the cumulative contribution of forest and NDVI was about 30 %. The study identified the key drivers of sediment transport changes in the basin and provided valuable insights for future studies of wind and water compound erosion at the basin scale.

Evaluation framework for bitumen-aggregate interfacial adhesion incorporating pull-off test and fluorescence tracing method

The interfacial adhesion between bitumen and aggregate is crucial to maintain the mechanical performance of asphalt pavement. Numerous indicators characterize the bonding ability of bitumen to aggregate; however, indicators based on limited conditions are used infrequently to effectively evaluate the interfacial adhesion between bitumen and aggregate. This study introduces a method to assess adhesion and moisture damage resistance at the bitumen-aggregate interface by combining the pull-off test with the fluorescence tracing method. The improved pull-off test considers different bitumen film thicknesses, ambient temperatures, material combinations, and moisture damage conditions, and the fluorescence tracing method and surface energy parameters are utilized to precisely detect the extent of bitumen adherence to the aggregate. The findings of this study are summarized as follows. (1) The bitumen stripping ratio characterizes the influence of ambient temperature and bitumen film thickness, and the pull-off strength/work quantifies the influence of ambient temperature and material combination. (2) The indicators derived from surface free energy theory exhibit lower sensitivity to material combinations compared with the pull-off performance indicators, with a coefficient of variation for both adhesion and stripping work of approximately 6 % compared to 14.29 %–70.13 % for the pull-off performance indicators. (3) The pull-off strength indicator dominates the pull-off performance evaluation, with the bitumen stripping ratio necessary at 0°C and pull-off work significant at 40°C to evaluate adhesive properties. In addition, the bitumen stripping ratio increment should be considered alongside pull-off strength loss ratio when estimating anti-moisture damage performance. (4) The fluorescence tracing method shows great potential in terms of enhancing the accuracy of bitumen stripping ratio detection in the pull-off test, with bitumen stripping ratio discrepancies at 0°C–40°C ranging from 0.98 % to 7.16 % for 70#-Limestone and 2.10–9.21 % for 70#-Granite. The methodology presented in this study represents a promising framework to determine the interfacial adhesive properties of asphalt mixtures with high accuracy.

Fractional holographic dark energy

Holographic dark energy theories present a fascinating interface to probe late-time cosmology, as guided by contemporary ideas about quantum gravity. In this work, we present a new holographic dark energy scenario designated “Fractional Holographic Dark Energy” (FHDE). This model extends the conventional framework of HDEs by incorporating specific features from fractional calculus and fractional Wheeler-De Witt equation recently applied, e.g., in cosmological settings. In this manner, we retrieve a novel form of HDE energy density. We then show how FHDE can provide a consistent picture of the evolution of the late-time universe even with the simple choice of the Hubble horizon as the IR (infrared) cutoff. We provide detailed descriptions of the cosmological evolution, showing how the fractional calculus ingredients can alleviate quite a few issues associated with the conventional HDE scenario. Concretely, we compute and plot diagrams using the Hubble horizon cutoff for HDE. The density parameters for DE and dark matter (DM), the deceleration parameter, and the DE EoS parameter indicate how the universe may evolve within our “FHDE” model, fitting within an appropriate scenario of late-time cosmology.

Criterion of perturbativity with the mass-dependent beta function in extended Higgs models

In order to realize an electroweak first-order phase transition, a category of extended Higgs models with relatively large self-coupling constants is often considered. In such a scenario, the running coupling constants can blow up at an energy scale much below the Planck scale. To clarify the allowed parameter space of the model, it is important to evaluate the scale where perturbative calculation breaks down. In the renormalization group equation analysis using the mass-independent renormalization scheme, we need a matching condition to connect the low energy theory and the high energy theory. Consequently, the analysis depends on the details of the matching condition. On the other hand, an analysis with the mass-dependent beta function can be performed in a simpler way because the threshold effect is automatically included in the beta function. In this paper, using the mass-dependent beta function, we discuss the application limit of perturbative calculations at the one-loop level. First, we explain the essence of our method and compare our results with those based on the mass-independent beta function in a toy model with two complex scalar fields. We then apply our method to a more realistic model, i.e., the inert doublet model. We find that, in our method, in which the threshold effect is automatically included, the energy scale where the perturbative calculation breaks down can be higher than the one using the mass-independent beta function, especially when the blowup scale is relatively low. Published by the American Physical Society 2024

Investigation on mechanism of mechanical activation and chemical reactions in CMP of diamond assisted by hydroxyl free radicals

Chemical mechanical polishing (CMP) is a crucial manufacturing process for diamond substrate applications. However, due to the extremely high hardness and chemical stability of diamond, its CMP efficiency is extremely low. This study aims to achieve microscopic simulation of atomic oxidation removal at the three-phase interface between diamond abrasive particle, diamond substrate, and hydroxyl free radicals (∙OH) aqueous solution. The mechanical activation behavior, microscopic mechanism, and feasibility of elementary reactions in the ∙OH aqueous solution environment was studied using various perspectives of radial distribution function, central atomic coordination state, atomic bonding and removal forms, energy, electron orbitals, and density functional theory (DFT). The simulation results indicate that during the process of diamond atomic oxidation removal, the atomic density at the first adjacent position centered on one atom decreases, and the stable covalent bond structure is disrupted, resulting in an amorphous phase transition. The chemical adsorption reaction generates C-O and C–H bonds. The C atoms mainly separate from the diamond substrate by the forming products such as carbon monoxide (CO), carbon dioxide (CO2), and carbon chains. The mechanical activation behavior indirectly reduces the activation energy of chemical reactions, while increasing the absolute value of adsorption energy and improving the ability of chemical adsorption solution atoms H and O, as well as ∙OH groups. Based on DFT, it has been verified that the intrusion process of ∙OH groups in the elementary reactions involving the typical product C*O2 is an exothermic reaction that can occur spontaneously, and the energy barrier needs to be overcome by the mechanical action of abrasive particles. At the same time, the energetics of the oxidation of diamond C atoms to generate C*O2 have successfully confirmed that the combined action of ∙OH and diamond abrasive particles is expected to achieve efficient oxidation removal and finishing polishing of diamond atoms.

Uncertainty-informed selection of CMIP6 Earth system model subsets for use in multisectoral and impact models

Abstract. Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Energy of functional brain states correlates with cognition in adolescent-onset schizophrenia and healthy persons.

Adolescent-onset schizophrenia (AOS) is rare, under-studied, and associated with more severe cognitive impairments and poorer outcomes than adult-onset schizophrenia. Neuroimaging has shown altered regional activations (first-order effects) and functional connectivity (second-order effects) in AOS compared to controls. The pairwise maximum entropy model (MEM) integrates first- and second-order factors into a single quantity called energy, which is inversely related to probability of occurrence of brain activity patterns. We take a combinatorial approach to study multiple brain-wide MEMs of task-associated components; hundreds of independent MEMs for various sub-systems are fit to 7 Tesla functional MRI scans. Acquisitions were collected from 23 AOS individuals and 53 healthy controls while performing the Penn Conditional Exclusion Test (PCET) for executive function, which is known to be impaired in AOS. Accuracy of PCET performance was significantly reduced among AOS compared to controls. A majority of the models showed significant negative correlation between PCET scores and the total energy attained over the fMRI. Across all instantiations, the AOS group was associated with significantly more frequent occurrence of states of higher energy, assessed with a mixed effects model. An example MEM instance was investigated further using energy landscapes, which visualize high and low energy states on a low-dimensional plane, and trajectory analysis, which quantify the evolution of brain states throughout this landscape. Both supported patient-control differences in the energy profiles. Severity of psychopathology was correlated positively with energy. The MEM integrated representation of energy in task-associated systems can help characterize pathophysiology of AOS, cognitive impairments, and psychopathology.

Perspectives of Experts in the Selection of Contents for a Course of Differential Equations in Engineering

This study investigates the teaching of Differential Equations in engineering education, focusing on expert opinions from diverse institutional backgrounds. The primary objectives were to understand expert perspectives on essential course content, justify their content choices, measure institutional gaps, and highlight the range of professional profiles. Through qualitative interviews with nine experts from academia and industry, we explored their views on the integration of Differential Equations in engineering curricula. The findings reveal a consensus on the importance of basic concepts to support physical models. Notably, experts highlighted variational calculus and energy representations as critical yet underrepresented areas in Differential Equations courses, essential for modeling complex, nonlinear problems. The study also states on the growing importance of computational methods and software in engineering education, advocating for an integrated approach aligning theoretical concepts with real-world applications. The expert opinions revealed a significant institutional influence on content perception, demonstrating a gap between traditional theoretical focus and modern, application-oriented approaches. The study suggests a need for curricular innovation in differential equations education, emphasizing practical applications and computational methods to bridge this gap between the two institutions considered in which the experts are adhered to.

Nuclear Energy as a Sustainable Source? Examining Media Discourses Surrounding the EU-Complementary Delegated Act

This paper contributes to the understanding of media discourses surrounding the sustainability of nuclear energy, particularly within a crisis context. The study examines the implications of the adoption of the Complementary Delegated Act on EU taxonomy by the European Commission on July 6, 2022, which officially categorises nuclear energy as sustainable. Employing a critical discourse analysis (CDA) methodology, the research explores the discourse strategies–nomination, predication, and argumentation– utilised across 695 news articles. The findings reveal the intricate representation of nuclear energy’s sustainability across the media. The Russian-Ukrainian conflict amplifies favourably the nuclear energy view as a reliable and sustainable energy source. However, the media discourse on nuclear energy’s sustainability reveals a variety of viewpoints. These varying outlooks provide essential insights for shaping effective energy policies. This research contributes valuable insights into understanding how media shapes and influences public perception of sustainability energy policies, amplifying specific actors’ voices and steering discourse toward distinct trajectories. Such insights are crucial in crafting energy policies.

Comment on “Fluid energy theory of membrane”, published by Li et al. [Water Res. 260 (2024) 121900]

Comment on “Fluid energy theory of membrane”, published by Li et al. [Water Res. 260 (2024) 121900]

Optimization of Rock Breaking Parameters by Combing Mechanical Specific Energy Theory and Random Forest Algorithm

Abstract As the key indicators to evaluate drilling efficiency, mechanical rate of penetration (ROP) and mechanical specific energy (MSE) are affected by many uncertain factors. In the actual drilling process, it is often necessary to adjust the parameters to achieve speed and efficiency. A dual-objective model is established considering the maximization of ROP and the minimization of MSE. Which combined with the theory of mechanical specific energy and the principle of multi-objective optimization, the model parameter weights are determined by the feature importance in the random forest regression model, and the value range of rock breaking parameter is optimized, realize the fusion of physical model and data model. The results show that, in the four strata of well X, the average ROP of T2k1 stratum is increased by 48.6% and the average MSE is decreased by 26.6%, the average ROP of T1b3 stratum is increased by 89.9% and the average MSE is decreased by 33.8%, the average ROP of the T1b2 stratum was increased by 41.3% and the average MSE was decreased by 39.0%, the average ROP of the T1b1 stratum was increased by 29.2% and the average MSE was decreased by 37.3%, which met the dual objectives of maximizing the ROP and minimizing the MSE. Compared with the traditional single-objective optimization method, the established dual-objective model is more in line with the needs of complex drilling engineering, and the fusion of physical model and data model can reduce certain human error.

The promises and perils of a mid-career pivot

Astrophysics to physical oceanography. String theory to machine learning. High energy theory to biophysics. Such leaps in research focus can be challenging—and rewarding.

Evaluation of gum arabic and gelatine coacervated microcapsule morphology and core oil encapsulation efficiency by combining the spreading coefficient and two component surface energy theory

Microcapsules containing various flavour/fragrance oils with different properties were fabricated using gelatine and gum arabic by complex coacervation. The surface properties (surface polarity and the spreading coefficients) of core oils were investigated in order to evaluate their effects on the capsule morphology and encapsulation efficiency based on a spreading coefficient and two component surface energy theory. Contact angles, interfacial tensions, and surface polarities were measured, and results were discussed with respect to the internal structure as well as encapsulation efficiency of different oil microcapsules. The thermodynamic spreading coefficients theory did not give an exactly accurate prediction of capsule morphology using high molecular weight biopolymer as the wall material in this work. Notwithstanding, the morphology predictions for different oil microcapsules are holistically consistent with the values of their encapsulation efficiency. Also, it has been found that the encapsulation efficiency increased with the decreasing surface polarity of the core oil holistically.

Size and Shape Effect on Melting Temperature of Metallic Nanocrystals

The melting temperature serves as a pivotal physical property governing the thermal stability of metallic nanocrystals, notably exhibiting substantial variability with respect to size and dimensionality. While several quantitative models exist to elucidate how the melting temperature correlates with the size and dimensionality of metallic nanocrystals, these models often fall short of capturing the synergistic influence of both factors comprehensively. To address this gap, our study employs a novel thermodynamic framework grounded in cohesive energy theory, requiring no arbitrary adjustable parameters. We find that, under constant conditions, the melting temperature of metallic nanocrystals diminishes as their size decreases. In terms of dimensionality, we establish a hierarchy as follows: nanoparticles > nanowires > thin films. Moreover, we reveal a non-linear relationship between the melting temperature and the inverse of dimensionality. Through rigorous validation via both simulations and empirical experiments, we corroborate the high accuracy of this thermodynamic model in predicting the variations in the melting temperature of metallic nanocrystals due to changes in size and dimensionality. The model in this study is primarily applicable to metallic nanocrystals and the potential applicability to other types of nanocrystals under certain conditions is briefly mentioned..

ANALYTICAL DESCRIPTION AND ALGORITHM OF CALCULATIONS OF MECHANICS OF STRUCTURES WITH BERNOULLI – EULER BEAMS USING SCAS KIDY

The problems of developing a universal analytical description and algorithm for automatic computer calculations of dynamics, statics and kinetostatics of mechanical models of structures including a beam lattice are considered. This can be calculations of transient processes, steady-state free and forced oscillations, determination of equilibrium positions and stress-strain states under static and dynamic loads, etc. The structure itself can be flat or spa- tial, stationary or moving on a plane or in space. Various instruments and devices can be attached to it. Arbitrary connections can be taken into ac- count. It is shown how it is possible to succinctly, using a special language for preparing computer data, analytically describe a part of a structure rep- resenting a beam lattice. Based on the theory of elasticity of Bernoulli-Euler beams, 2 forms of the canonical representation of the potential energy of an elastic beam are obtained. This makes it possible to introduce into the language of description of mechanical models of the special computer algebra system KiDyM (SCAS KiDyM) a new beam element, for which the position of the coordinate systems associated with the extreme sections is indi- cated. The positions of these sections are determined by lattice nodes, like solid bodies. Angular and linear coordinates of such solid bodies give gen- eralized coordinates of the mechanical model. An algorithm has been developed for the formation of elements adopted to describe mechanical models in SCAS KiDyM. Thus, the elastic structure of the mechanical model is formed. Using the available tools in this program, equations of dynamics and statics are automatically constructed, that is, a mathematical model is formed, and dynamic and static calculations can be carried out. The proposed method is demonstrated in detail on the example of calculating the deformation of an elastic lattice, with is the basis of a UAV (unmanned aerial vehi- cle). The results are compared with calculations using the ANSYS program