Conventional Theory Research Articles

Balanced d-Band Model: A Framework for Balancing Redox Reactions in Lithium-Sulfur Batteries.

Managing the redox reactions of polysulfides is crucial for improving the performance of lithium-sulfur batteries (LSBs). Herein, we introduce a progressive theoretical framework: the balanced d-band model, which is based on classical d-band center theory. Specifically, by optimizing the position of the d-band center in the middle between the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) of each sulfur species, balanced and fast oxidation and reduction reactions of the sulfur species can be achieved simultaneously. To validate this theory, we synthesized a catalyst featuring an in situ phosphorized heterostructure (NOP) based on nickel oxide (NiO), which effectively optimizes the d-band center at the middle between the HOMO and LUMO of each sulfur species. Aided by the balanced oxidation and reduction kinetics of the sulfur species, the NOP-based cell achieved a high reversible capacity, superior cycling stability, and prolonged cycle life. This study extends the conventional d-band center theory and introduces an innovative theoretical model to expand our understanding of the internal reaction mechanisms in LSBs.

One-step leaping evolution from an autosomal pair to the heteromorphic sex chromosomes.

Sex chromosomes evolve from an autosomal pair after the acquisition of a sex-determining gene. The primary sex chromosomes are homomorphic in both sexes and often undergo heteromorphism in either sex (XY in males or ZW in females) in association with chromosome rearrangements such as inversion, which creates a non-recombining region, called a stratum. Then, multiple strata may form by sequential inversions and extend the non-recombining region, where gene divergence accelerates, and degeneration of the Y or W chromosome progressively occurs. Contrast to the conventional theory, we propose a shortcut in heteromorphic sex chromosome evolution, where an autosomal pair directly evolves into a heteromorphic sex chromosome pair. We illustrate this with two frog cases where Y chromosome or autosome, which is morphologically inverted, was introgressed from another species through interspecific hybridization, instantly forming a new heteromorphic sex chromosome pair. This event resulted in a distinct non-recombining region immediately after hybridization. The introduction of an inverted chromosome from a different species may be associated with benefits in morphology, breeding behavior, hybrid viability, sex determination, and recovery of the sex ratio of the hybrids. We discuss the molecular mechanisms driving preferential mutations in the introduced, inverted chromosome through interspecific hybridization.

Emergence of global value chains as key driver behind industrial development: evidence from participating African countries

Purpose The purpose of this paper is to examine the role of global value chains (GVC) in industrial development of emerging economies, with particular focus on participating African countries. The findings of this study are expected to provide insight on the need for more developing countries to participate in GVC. Design/methodology/approach This study is built upon the neoclassical and endogenous growth theories, which postulate that savings, physical capital and human capital are the fundamental drivers of development in productive sectors of the economy. The investigation, covering the period 1980–2021, is carried out by using the unrestricted error correction model and dynamic ordinary least squares model. Findings The results of this study reveal that GVC stands as the dominant factor driving industrial development, compared to savings, physical capital and human capital. The findings, therefore, seem to contradict the postulation of conventional theories. The policy implications of the findings are not far-fetched. First, industrial development in the participating African countries has benefited largely from GVC; hence, it is necessary to encourage more participation. Second, industrial development also benefited from the control variables (savings, physical capital and human capital), hence the need to sustain their complementary role. Thirdly, only three African countries are actively participating in GVC, which suggests that more countries need to join, to facilitate industrial development. Originality/value Previous studies have not given adequate attention to African countries that participate in GVC, thus creating a void that needs to be filled. This study, therefore, produced results that are relevant to policy-making on industrial development in African countries.

Vehicle Lane Changing Game Model Based on Improved SVM Algorithm

In order to improve the autonomous lane-changing performance of unmanned vehicles, this paper aims to solve the problem of inaccurate decision classification in traditional support vector machine (SVM) algorithms applied to the lane-changing decision-making stage of intelligent driving vehicles. By using game theory-related theories and combining the improved support vector machine (SSA-SVM) method, a vehicle autonomous lane-changing strategy based on game theory is established. The optimized SVM method has certain advantages for vehicle lane-changing decision-making with a small sample size in actual production processes. The lane-changing decision judgment accuracy rate of the SSA-SVM algorithm model can reach 93.6% compared with the SVM algorithm model without algorithm optimization; the SSA-SVM algorithm model has obvious advantages in decision performance and running speed. Therefore, the proposed new algorithm can effectively solve the problem of the objective consideration of the payoff function in conventional decision game theory.

Systematic study on the hydrogen abstraction reactions from oxygenated compounds by H and HO2

AbstractTo extend the rule‐based approach for hydrogen abstraction reactions from oxygenated compounds, a systematic investigation was performed to examine the reactivity of gas‐phase hydrogen abstraction reactions from alkyl groups (methyl and ethyl groups) bound to oxygen atoms in five types of oxygenated compounds (alcohols, ethers, formate esters, acetate esters, and carbonate esters) by H atoms and HO2 radicals comprehensively considering rotational conformers. Quantum chemical calculations were conducted at the CBS‐QB3 level for stationary points. Rate constants were determined employing conventional transition state theory (TST). For hydrogen abstraction reactions by H, the rotational conformer distribution partition function was employed to approximate partition functions, owing to the similarity in vibrational energy‐level structures among conformers. In hydrogen abstraction reactions by HO2, the vibrational structures of transition‐state (TS) conformers varied significantly due to the hydrogen bonding, leading to an inappropriate evaluation of rate constants when using the lowest‐energy conformer as a representative. Therefore, the rate constants were calculated by the multi‐structural TST. It was revealed that the differences in functional groups containing O atoms mainly affect the bond dissociation energies of the C–H bonds and the activation energies of hydrogen abstraction reactions only when the C atoms are adjacent to the O atoms. Additionally, it was found that hydrogen bonds formed in the TSs show minor effect on rate parameters for the overall rate constants, apart from the reduction of the pre‐exponential factors for the H‐abstraction reactions from the methylene position of ethyl groups. The comparison with the rate constants from previous studies showed reasonable results, indicating that the rate constants in this study, which thoroughly consider rotational conformers, can be the current best estimates.

Exploring nonlinear correlations among transition metal nanocluster properties using deep learning: a comparative analysis with LOO-CV method and cosine similarity

A novel approach is introduced for the rapid and accurate correlation analysis of nonlinear properties in Transition Metal (TM) clusters utilizing the Deep Leave-One-Out Cross-Validation technique. This investigation demonstrates that the Deep Neural Network (DNN)-based approach offers a more efficient predictive method for various properties of fourth-row TM nanoclusters compared to conventional Density Functional Theory methods, which are computationally intensive and time-consuming. The feature space, also known as descriptors, is established based on a broad spectrum of electronic and physical characteristics. Leveraging the similarities among these clusters, the DNN-based model is employed to explore the correlations among TM cluster properties. The proposed method, in conjunction with cosine similarity, achieves remarkable accuracy up to 10-9 for predicting total energy, lowest vibrational mode, binding energy, and HOMO-LUMO energy gap of TM2, TM3, and TM4nanoclusters. By analyzing correlation errors, the most closely coupled TM clusters are identified. Notably, Mn and Ni clusters exhibit the highest and lowest levels of energy coupling with other TMs, respectively. Generally, energy prediction for TM2, TM3, and TM4clusters exhibit similar trends, while an alternating behavior is observed for vibrational modes and binding energies. Furthermore, Ti, V, and Co demonstrate the highest binding energy correlations with TM2, TM3, and TM4sets, respectively. Regarding energy gap predictions, Ni exhibits the strongest correlation in the smallest TM2clusters, while Cr shows the highest dependence in TM3and TM4sets. Lastly, Zn displays the largest error in HOMO-LUMO energy gap across all sets, indicating distinctive independent energy gap characteristics.

Making a difference with matter in researchers’ positionality dynamics in qualitative inquiry

Scholars have increasingly highlighted the usefulness of new materialist theories and methodologies in attempts to address the perceived shortcomings of conventional theories inspired by social constructionism and post-structuralism. Contributing to these discussions, we utilize a new materialist theory which rests on a monistic ontology drawn from Spinozian and Deleuzian assemblage theory to examine the constitutive role of material elements (e.g. audio recorders, video cameras, and dress/shirts worn during interviews), spatial, and discursive forces in co-creating fluid nonstatic researcher positionalities in qualitative research process. This article is part of a larger ethnographic study conducted by the first author among fisherfolk in Ghana's coastal fishing communities. Our results show that the researcher's fluid positionalities during interview encounters were brought to bear and sustained in space and time through the joint effort of material, discursive, and spatial forces. As qualitative researchers seek ways to ensure better understanding of their study communities through intimate interactions, an attention to the assemblage of material-discursive forces in interview encounters may highlight some of the opportunities and obstacles in qualitative inquiry beyond human agency and negotiations.

Polarization-Selective Metamaterial Absorber Using Tailored Y-Shaped SRR for UWB Device and Doppler Navigation Aids Applications

In this paper, we present an unprecedented metamaterial absorber design exhibiting exceptional characteristics in electromagnetic wave absorption. The proposed bent Y-shaped structure, fabricated on an FR-4 substrate with copper patches, showcases remarkable performance across a diverse frequency spectrum. Through exhaustive simulations in CST, this design manifests eight distinct resonant frequencies, achieving absorption rates exceeding 90% at each resonance. The resonances, strategically spanning from L-band (3.728 GHz) through S-band, C-band, X-band, Ku-band, and K-band up to 22.664 GHz, signify unparalleled versatility and efficacy in mitigating electromagnetic radiation. It investigates the equivalent circuit parameters of a proposed metamaterial absorber design, focusing on inductance (L), capacitance (C), and resistance (R). This paper investigates the applications of UWB devices at 3.728 GHz and Doppler navigation aids at the 13.4 GHz frequency as regulated by the Federal Communications Commission. It includes a discussion on near-zero refractive Index Metamaterials (NZRIM), highlighting their potential utilization in achieving extraordinary control over wave behaviour. Notably, the absorber's inherent polarization insensitivity fortifies its adaptability in various applications. Additionally, the metamaterial exhibits near-zero or negative permittivity, altering electric response, while simultaneously demonstrating permeability absolute zero throughout all frequency bands sparking new avenues for exploration and challenging conventional electromagnetic theories.

ALMA Observations of Proper Motions of the Dust Clumps in the Protoplanetary Disk MWC 758

To study the dust dynamics in the dust-trapping vortices in the protoplanetary disk around MWC 758, we analyzed the 1.3 mm continuum images of the MWC 758 disk obtained with the Atacama Large Millimeter/submillimeter Array in 2017 and 2021. We detect proper motions of 22 mas and 24 mas in the two dust clumps at radii of 0.″32 and 0.″54 in the disk on the plane of the sky, respectively. On the assumption that the dust clumps are located in the disk midplane, the velocities of the observed proper motions along the azimuthal direction of the inner and outer dust clumps are sub- and super-Keplerian, respectively, and both have angular velocities corresponding to the Keplerian angular velocity at a radius of 0.″46 ± 0.″04. This deviation from the Keplerian motion is not expected in the conventional theory of vortices formed by the Rossby wave instability. The observed non-Keplerian proper motions of the dust clumps are unlikely due to the disk warp and eccentricity, nor are they associated with any predicted planets. The two dust clumps are likely spatially coincident with the infrared spirals. In addition, we detect the changes in the intensity profiles of the dust clumps over the 4 yr span. Therefore, we suggest that the observed proper motions are possibly due to changes in the density distributions in the dust clumps caused by their interaction with the spirals in the disk.

Electrically tunable graded photonic crystal lens based on graphene plasmons.

Controlling the nonlinear relationship between surface plasmon polariton (SPP) mode index and chemical potential of graphene can be used in the field of active transformation optics. Here, we propose an electrically tunable 2D Graded Photonic Crystal (GPC) lens based on graphene SPP platform. Our platform comprises a graphene monolayer integrated into a back-gated structure with nano-patterned gate insulators. When the chemical potential of the graphene surface is designed to operate in the nonlinear region, the designed GPC lens can be continuously transformed between a Maxwell's fish-eye lens and a Luneburg lens by tuning the gate voltage. The range of the lens background chemical potential for allowing this transformation is systematically studied. To compensate for the significant errors inherent in the conventional effective medium theory (EMT) during the homogenization of photonic crystals (PCs), we propose a generalized effective medium theory (GEMT). The validity and accuracy of this approach are verified through comparisons with true values (based on rigorous eigenvalue solutions) and EMT values. Due to its advantages of on-site controls and easy fabrication characteristics, the proposed graphene GPC provides a new way for practical on-chip light manipulation.

Implications of Artificial Intelligence (AI) and machine learning-based fintech for the financial assets related traditional investment theories

New technologies always have an impact on traditional theories. Finance theories are no exception to that. In this paper, we have concentrated on the traditional investment theories in finance. The study examined five investment theories, their assumptions, and their limitation from different works of literature. The study considered Artificial Intelligence (AI) and Machine Learning (ML) as representative of financial technology (fintech) and tried to find out from the literature how these new technologies help to reduce the limitations of traditional theories. We have found that fintech does not have an equal impact on every conventional finance theory. Fintech outperforms all five traditional theories but on a different scale.

Long-range enhancement for fluorescence and Raman spectroscopy using Ag nanoislands protected with column-structured silica overlayer

We demonstrate long-range enhancement of fluorescence and Raman scattering using a dense random array of Ag nanoislands (AgNIs) coated with column-structured silica (CSS) overlayer of over 100 nm thickness, namely, remote plasmonic-like enhancement (RPE). The CSS layer provides physical and chemical protection, reducing the impact between analyte molecules and metal nanostructures. RPE plates are fabricated with high productivity using sputtering and chemical immersion in gold(I)/halide solution. The RPE plate significantly enhances Raman scattering and fluorescence, even without proximity between analyte molecules and metal nanostructures. The maximum enhancement factors are 107-fold for Raman scattering and 102-fold for fluorescence. RPE is successfully applied to enhance fluorescence biosensing of intracellular signalling dynamics in HeLa cells and Raman histological imaging of oesophagus tissues. Our findings present an interesting deviation from the conventional near-field enhancement theory, as they cannot be readily explained within its framework. However, based on the phenomenological aspects we have demonstrated, the observed enhancement is likely associated with the remote resonant coupling between the localised surface plasmon of AgNIs and the molecular transition dipole of the analyte, facilitated through the CSS structure. Although further investigation is warranted to fully understand the underlying mechanisms, the RPE plate offers practical advantages, such as high productivity and biocompatibility, making it a valuable tool for biosensing and biomolecular analysis in chemistry, biology, and medicine. We anticipate that RPE will advance as a versatile analytical tool for enhanced biosensing using Raman and fluorescence analysis in various biological contexts.

Reconceptualizing Constitutionalism in the AI Run Algorithmic Society

Abstract This Article is dedicated to what is arguably one of the most significant tests to which constitutionalism has been subject to in recent times. It examines the theoretical and practical challenges to constitutionalism arising from the profound technological changes under the influence of artificial intelligence (AI) in our emerging algorithmic society. The unprecedented rapid development of AI technology has not only rendered conventional theories of modern constitutionalism obsolete, but it has also created an epistemic gap in constitutional theory. As a result, there is a clear need for a new, compelling constitutional theory that adequately accounts for the scale of technological change by accurately capturing it, engaging with it, and ultimately, responding to it in a conceptually and normatively convincing way.

Discovery and characterization of a dense sub-Saturn TOI-6651b

We report the discovery and characterization of a transiting sub-Saturn exoplanet TOI-6651b using PARAS-2 spectroscopic observations. The host, TOI-6651 (mV ≈ 10.2), is a sub-giant, metal-rich G-type star with [Fe / H] = 0.225−0.0450.044[Fe/H] = 0.225−0.045+0.044, Teff = 5940 ± 110 K, and log g = 4.087−0.032+0.035. Joint fitting of the radial velocities from PARAS-2 spectrograph and transit photometric data from Transiting Exoplanet Survey Satellite (TESS) reveals a planetary mass of 61.0−7.9+7.6 M⊕ and radius of 5.09−0.26+0.27 R⊕, in a 5.056973−0.000018+0.000016 day orbit with an eccentricity of 0.091−0.062+0.096. TOI-6651b has a bulk density of 2.52−0.44+0.52 g cm−3, positioning it among the select few known dense sub-Saturns and making it notably the densest detected with TESS. TOI-6651b is consistent with the positive correlation between planet mass and the host star’s metallicity. We find that a considerable portion ≈87% of the planet’s mass consists of dense materials such as rock and iron in the core, while the remaining mass comprises a low-density envelope of H/He. TOI-6651b lies at the edge of the Neptunian desert, which will be crucial for understanding the factors shaping the desert boundaries. The existence of TOI-6651b challenges conventional planet formation theories and could be a result of merging events or significant atmospheric mass loss through tidal heating, highlighting the complex interplay of dynamical processes and atmospheric evolution in the formation of massive dense sub-Saturns.

Electronic interactions in Dirac fluids visualized by nano-terahertz spacetime interference of electron-photon quasiparticles.

Ultraclean graphene at charge neutrality hosts a quantum critical Dirac fluid of interacting electrons and holes. Interactions profoundly affect the charge dynamics of graphene, which is encoded in the properties of its electron-photon collective modes: surface plasmon polaritons (SPPs). Here, we show that polaritonic interference patterns are particularly well suited to unveil the interactions in Dirac fluids by tracking polaritonic interference in time at temporal scales commensurate with the electronic scattering. Spacetime SPP interference patterns recorded in terahertz (THz) frequency range provided unobstructed readouts of the group velocity and lifetime of polariton that can be directly mapped onto the electronic spectral weight and the relaxation rate. Our data uncovered prominent departures of the electron dynamics from the predictions of the conventional Fermi-liquid theory. The deviations are particularly strong when the densities of electrons and holes are approximately equal. The proposed spacetime imaging methodology can be broadly applied to probe the electrodynamics of quantum materials.

Charge-Compensation-Driven Failure Mechanism of Ni Redox in Fe-Rich Ni/Fe/Mn-Based O3-Type Layered Oxide Sodium-Ion Cathodes.

For cathode materials of sodium-ion batteries, O3-type Ni/Fe/Mn-based (Na-NFM) layered oxides have garnered extensive attention because of high economic viability, environmental friendliness, and the potential for high energy density. Among them, Fe-rich compositions exhibit higher initial charge capacity and lower bill-of-material costs, while they fade rapidly and exhibit low initial coulombic efficiency, hindering their commercialization prospects. In this work, we investigate the failure of Fe-rich Na-NFM materials through X-ray absorption spectroscopy methods. The results reveal a combined failure mechanism that encompasses not only the conventional theory of Fe migration but also an abnormal Ni-redox deterioration, which has not yet been reported. More factors related to the failure of Fe-rich Na-NFM layered oxides are discussed in detail. These findings are expected to inspire targeted research efforts toward Fe-rich Na-NFM materials, thereby accelerating the practical application of sodium-ion batteries.

Fixed time convergence approach for multiplayer iterated dilemmas with continuous action

This paper introduces a novel fixed-time scheme for analyzing the convergence of the multiplayer continuous action-iterated dilemma (CAID). In contrast to conventional evolutionary game theory, which restricts players to binary strategies of cooperation or defection, CAID allows for continuous strategies and offers players a spectrum of choices. Additionally, two discount rates are incorporated in the strategy dynamics to reflect the imprecision in players' learning from strategic differences. The proposed algorithm ensures a fixed-time convergence of consensus between the player strategies. As a result, the settling time of consensus is independent of the initial strategy. Lyapunov stability theory is employed to assess convergence within a fixed time. Moreover, simulation results demonstrate the superiority of the proposed algorithm, converging more quickly and with fewer iterations than the recently established results.

A deep equivariant neural network approach for efficient hybrid density functional calculations

Hybrid density functional calculations are essential for accurate description of electronic structure, yet their widespread use is restricted by the substantial computational cost. Here we develop DeepH-hybrid, a deep equivariant neural network method for learning the hybrid-functional Hamiltonian as a function of material structure, which circumvents the time-consuming self-consistent field iterations and enables the study of large-scale materials with hybrid-functional accuracy. Our extensive experiments demonstrate good reliability as well as effective transferability and efficiency of the method. As a notable application, DeepH-hybrid is applied to study large-supercell Moiré-twisted materials, offering the first case study on how the inclusion of exact exchange affects flat bands in magic-angle twisted bilayer graphene. The work generalizes deep-learning electronic structure methods to beyond conventional density functional theory, facilitating the development of deep-learning-based ab initio methods.

NbTi: A nontrivial puzzle for the conventional theory of superconductivity

NbTi: A nontrivial puzzle for the conventional theory of superconductivity

Mesoscopic coupled plasticity-damage model of rough surface considering size-dependent plasticity

Metallic materials exhibit significant size effect at mesoscopic scale have been revealed by many experiments. Thus, it is essential to consider size effect when conducting mesoscopic scale studies in tribology. However, there is no constitutive model considering both ductile fracture and size effects of rough surface in the literature. At the same time, the simulation of friction and wear at the mesoscopic scale suffer from computational inefficiency using the conventional theory of mechanism-based strain gradient (CMSG) plasticity theory. To address the aforementioned challenges, we propose a mesoscopic coupled plasticity-damage model by combining the coupled plasticity-damage model and the simplified CMSG plasticity theory. This model can take into account not only the effect of stress state, temperature, strain rate on plasticity and fracture, but also the effect of the effective plastic strain gradient. Meanwhile, the model is able to solve the effective plastic strain gradient by a simplified method, thereby enhancing the computational efficiency. Then, model parameters for bearing bushing material of an engine are calibrated. Finally, scratch simulation and scratch tests of bearing bushing material at the mesoscopic scale are conducted to validate the effectiveness of the mesoscopic constitutive model. The residual scratch depth and width, coefficient of friction at different normal loads are investigated by experiment and simulation, respectively. Results proved the effectiveness of mesoscopic coupled plasticity-damage model, which is applicable for investigating issues related to wear, friction, and contact.