Fundamental Descriptors Research Articles

A Two‐in‐One Strategy to Simultaneously Boost the Site Density and Turnover Frequency of Fe−N−C Oxygen Reduction Catalysts

Site density and turnover frequency are the two fundamental kinetic descriptors that determine the oxygen reduction activity of iron‐nitrogen‐carbon (Fe−N−C) catalysts. However, it remains a grand challenge to simultaneously optimize these two parameters in a single Fe−N−C catalyst. Here we show that treating a typical Fe−N−C catalyst with ammonium iodine (NH4I) vapor via a one‐step chemical vapor deposition process not only increases the surface area and porosity of the catalyst (and thus enhanced exposure of active sites) via the etching effect of the in‐situ released NH3, but also regulates the electronic structure of the Fe−N4 moieties by the iodine dopants incorporated into the carbon matrix. As a result, the NH4I‐treated Fe−N−C catalyst possesses both high values in the site density of 2.15×1019 sites g−1 (×2 enhancement compared to the untreated counterpart) and turnover frequency of 3.71 electrons site−1 s−1 (×3 enhancement) that correspond to a high mass activity of 12.78 A g−1, as determined by in‐situ nitrite stripping technique. Moreover, this catalyst exhibits an excellent oxygen reduction activity in base with a half‐wave potential (E1/2) of 0.924 V and acceptable activity in acid with E1/2 = 0.795 V, and superior power density of 249.1 mW cm−2 in zinc‐air batteries.

A Two-in-One Strategy to Simultaneously Boost the Site Density and Turnover Frequency of Fe-N-C Oxygen Reduction Catalysts.

Site density and turnover frequency are the two fundamental kinetic descriptors that determine the oxygen reduction activity of iron-nitrogen-carbon (Fe-N-C) catalysts. However, it remains a grand challenge to simultaneously optimize these two parameters in a single Fe-N-C catalyst. Here we show that treating a typical Fe-N-C catalyst with ammonium iodine (NH4I) vapor via a one-step chemical vapor deposition process not only increases the surface area and porosity of the catalyst (and thus enhanced exposure of active sites) via the etching effect of the in-situ released NH3, but also regulates the electronic structure of the Fe-N4 moieties by the iodine dopants incorporated into the carbon matrix. As a result, the NH4I-treated Fe-N-C catalyst possesses both high values in the site density of 2.15×1019 sites g-1 (×2 enhancement compared to the untreated counterpart) and turnover frequency of 3.71 electrons site-1 s-1 (×3 enhancement) that correspond to a high mass activity of 12.78 A g-1, as determined by in-situ nitrite stripping technique. Moreover, this catalyst exhibits an excellent oxygen reduction activity in base with a half-wave potential (E1/2) of 0.924 V and acceptable activity in acid with E1/2 = 0.795 V, and superior power density of 249.1 mW cm-2 in zinc-air batteries.

Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models.

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization and analysis of scalar fields. The uncertainty inherent in data (e.g., observational and experimental data, approximations in simulations, and compression), however, creates uncertainty regarding critical point positions. Uncertainty in critical point positions, therefore, cannot be ignored, given their impact on downstream data analysis tasks. In this work, we study uncertainty in critical points as a function of uncertainty in data modeled with probability distributions. Although Monte Carlo (MC) sampling techniques have been used in prior studies to quantify critical point uncertainty, they are often expensive and are infrequently used in production-quality visualization software. We, therefore, propose a new end-to-end framework to address these challenges that comprises a threefold contribution. First, we derive the critical point uncertainty in closed form, which is more accurate and efficient than the conventional MC sampling methods. Specifically, we provide the closed-form and semianalytical (a mix of closed-form and MC methods) solutions for parametric (e.g., uniform, Epanechnikov) and nonparametric models (e.g., histograms) with finite support. Second, we accelerate critical point probability computations using a parallel implementation with the VTK-m library, which is platform portable. Finally, we demonstrate the integration of our implementation with the ParaView software system to demonstrate near-real-time results for real datasets.

Simple and Efficient Equivariant Message-Passing Neural Network Model for Non-local Potential Energy Surfaces.

Machine learning potential has become increasingly successful in atomistic simulations. Many of these potentials are based on an atomistic representation in a local environment, but an efficient description of nonlocal interactions that exceed a common local environment remains a challenge. Herein, we propose a simple and efficient equivariant model, EquiREANN, to effectively represent a nonlocal potential energy surface. It relies on a physically inspired message-passing framework, where the fundamental descriptors are linear combinations of atomic orbitals, while both invariant orbital coefficients and the equivariant orbital functions are iteratively updated. We demonstrate that this EquiREANN model is able to describe the subtle potential energy variation due to the nonlocal structural change with high accuracy and little extra computational cost than an invariant message passing model. Our work offers a generalized approach to create equivariant message-passing adaptations of other advanced local many-body descriptors.

Reverse degree-based topological indices study of molecular structure in triangular ϒ-graphyne and triangular ϒ-graphyne chain

Topological indices are mathematical descriptors of the structure of a molecule that can be used to predict its properties. They are derived from the graph theory, which describes the topology of a molecule and its connectivity. The main objective is mathematical modeling and topological properties of ϒ-graphyne. Current research focuses on two structures made from hexagonal honeycomb graphite lattices named triangular ϒ-graphyne and triangular ϒ-graphyne chains. The authors have simultaneously computed the first and second Reverse Zagreb indices, reverse hyper-Zagreb indices, and their polynomials. This research also derives mathematical closed-form formulas for some of its fundamental degree-based molecular descriptors. Researchers have been trying to synthesize a novel carbon form called Graphyne. For over a decade but with no success. Recently, some researchers have made a breakthrough in generating Carbons elusive allotrope and solved a long-standing problem in carbon materials. This wonder material is created to rival the conductivity of graphene but with control. These results opened new ways of research in the fields of semiconductors, electronics and optics. Furthermore, graphical and tabular results will help to investigate the structure-property relationships in γ-graphyne.

Ambient Electrochemical Ammonia Synthesis: From Theoretical Guidance to Catalyst Design.

Ammonia, a vital component in the synthesis of fertilizers, plastics, and explosives, is traditionally produced via the energy-intensive and environmentally detrimental Haber-Bosch process. Given its considerable energy consumption and significant greenhouse gas emissions, there is a growing shift toward electrocatalytic ammonia synthesis as an eco-friendly alternative. However, developing efficient electrocatalysts capable of achieving high selectivity, Faraday efficiency, and yield under ambient conditions remains a significant challenge. This review delves into the decades-long research into electrocatalytic ammonia synthesis, highlighting the evolution of fundamental principles, theoretical descriptors, and reaction mechanisms. An in-depth analysis of the nitrogen reduction reaction (NRR) and nitrate reduction reaction (NitRR) is provided, with a focus on their electrocatalysts. Additionally, the theories behind electrocatalyst design for ammonia synthesis are examined, including the Gibbs free energy approach, Sabatier principle, d-band center theory, and orbital spin states. The review culminates in a comprehensive overview of the current challenges and prospective future directions in electrocatalyst development for NRR and NitRR, paving the way for more sustainable methods of ammonia production.

Are inter-limb differences in change of direction velocity and angle associated with inter-limb differences in kinematics and kinetics following anterior cruciate ligament reconstruction?

BackgroundQuantifying inter-limb differences in kinematics and kinetics during change of direction is proposed as a means of monitoring rehabilitation following anterior cruciate ligament reconstruction (ACLR). Velocity and centre of mass (CoM) deflection angle are fundamental task descriptors that influence kinematics and kinetics during change of direction. Inter-limb differences in approach velocity and CoM deflection angle have been identified following ACLR and may contribute to the presence of inter-limb differences in kinematics and kinetics during change of direction. Research questionThe aim of this study was to quantify the proportion of variance in kinematic and kinetic inter-limb differences attributable to inter-limb differences in approach velocity and centre of mass deflection angle during a change of direction task. MethodsA cohort of 192 patients (male, 23.8 ± 3.6 years, 6.3 ± 0.4 months post primary ACLR) completed a pre-planned 90° change of direction task on both their operated and non-operated limb. Inter-limb differences in approach velocity and CoM deflection angle were calculated alongside lower-extremity kinematic and kinetic variables. The relationship between inter-limb differences in task-level variables and inter-limb differences in kinematic and kinetic variables was examined using linear regression models. Kinematic and kinetic inter-limb differences were adjusted for inter-limb differences in approach velocity and CoM deflection angle. Adjusted and unadjusted inter-limb differences were submitted to one sample t-tests. ResultsInter-limb differences in approach velocity and centre of mass deflection angle explained 3 – 60% of the variance in kinematic and kinetic inter-limb differences. Statistical inferences remained consistent between adjusted and unadjusted conditions with the exception of hip flexion angle. SignificanceInter-limb differences in task-level features explain a large proportion of the variance in inter-limb differences in several kinematic and kinetic variables. Accounting for this variation reduced the magnitude of kinematic and kinetic inter-limb differences comparable to those previously observed in normative cohorts.

Mechanistic insights into the origin of the oxygen migration barrier

Electronic structure signatures of oxygen ion migration highlight new fundamental descriptors of the oxygen migration barrier based on the electronic structure of the local chemical environment.

Central limit theorems for local network statistics

Summary Subgraph counts, in particular the number of occurrences of small shapes such as triangles, characterize properties of random networks. As a result, they have seen wide use as network summary statistics. Subgraphs are typically counted globally, making existing approaches unable to describe vertex-specific characteristics. In contrast, rooted subgraphs focus on vertex neighbourhoods, and are fundamental descriptors of local network properties. We derive the asymptotic joint distribution of rooted subgraph counts in inhomogeneous random graphs, a model that generalizes most statistical network models. This result enables a shift in the statistical analysis of graphs, from estimating network summaries to estimating models linking local network structure and vertex-specific covariates. As an example, we consider a school friendship network and show that gender and race are significant predictors of local friendship patterns.

Interpretable Machine Learning‐Assisted High‐Throughput Screening for Understanding NRR Electrocatalyst Performance Modulation between Active Center and C‐N Coordination

Understanding the correlation between the fundamental descriptors and catalytic performance is meaningful to guide the design of high‐performance electrochemical catalysts. However, exploring key factors that affect catalytic performance in the vast catalyst space remains challenging for people. Herein, to accurately identify the factors that affect the performance of N2 reduction, we apply interpretable machine learning (ML) to analyze high‐throughput screening results, which is also suited to other surface reactions in catalysis. To expound on the paradigm, 33 promising catalysts are screened from 168 carbon‐supported candidates, specifically single‐atom catalysts (SACs) supported by a BC3 monolayer (TM@VB/C‐Nn = 0–3‐BC3) via high‐throughput screening. Subsequently, the hybrid sampling method and XGBoost model are selected to classify eligible and non‐eligible catalysts. Through feature interpretation using Shapley Additive Explanations (SHAP) analysis, two crucial features, that is, the number of valence electrons (Nv) and nitrogen substitution (Nn), are screened out. Combining SHAP analysis and electronic structure calculations, the synergistic effect between an active center with low valence electron numbers and reasonable C‐N coordination (a medium fraction of nitrogen substitution) can exhibit high catalytic performance. Finally, six superior catalysts with a limiting potential lower than −0.4 V are predicted. Our workflow offers a rational approach to obtaining key information on catalytic performance from high‐throughput screening results to design efficient catalysts that can be applied to other materials and reactions.

Age of Acquisition of Personality Terms: Implications for Personality Theory.

Analysis of the age of acquisition (AoA) of personality terms represents a genetic method for the study of the individual personality lexicon and offers a potential alternative to correlational analysis for identifying the fundamental personality descriptors among the thousands of terms that appear in language. In the present study, the relationship between AoA, word frequency, word desirability, and factor loading in the Big Five and Hexaco models of 274 and 408 personality adjectives was analyzed. It was found that young children (2nd graders or younger) acquire personality terms that represent traits at the core of the broad personality factors in the Big Five and Hexaco models slightly earlier than words that represent more peripheral traits. In older children beyond second grade, the correlation between factor loading and AoA is weak. Words that describe the broad openness and stability/emotionality aspects of personality are learned later than words for the other broad factors. Word frequency (in book texts) and desirability have a weak negative correlation with AoA. It is hypothesized that the AoA of a personality term reflects the importance of the corresponding trait for children and may be used as one criterion for ranking facet level traits independent of the broad factors.

Investigation of Pharmaceutical Importance of 2H-Pyran-2-One Analogues via Computational Approaches

Highly functionalized spirocyclic ketals were synthesized through asymmetric oxidative spirocyclization via carbanion-induced ring transformation of 2H-pyran-2-ones with 1,4-cyclohexandione monoethyleneketal under alkaline conditions. Further acidic-hydrolysis of obtained spirocyclic ketals yields highly substituted 2-tetralone in good yield. Computational analysis based on the DFT calculations and MD simulations has been performed in order to predict and understand global and local reactivity properties of newly synthesized derivatives. DFT calculations covered fundamental reactivity descriptors such as molecular electrostatic potential and average local ionization energies. Nitrogen atom and benzene rings have been recognized as the most important molecular sites from these aspects. Additionally, to predict whether studied compounds are stable towards the autoxidation mechanism, we have also studied the bond dissociation energies for hydrogen abstraction and identified the derivative which might form potentially genotoxic impurities. Interactions with water, including both global and local aspects, have been covered thanks to the MD simulations and calculations of interaction energies with water, counting of formed hydrogen interactions, and radial distribution functions. MD simulations were also used to identify which excipient could be used together with these compounds, and it has been established that the polyvinylpyrrolidone polymer could be highly compatible with these compounds, from the aspect of calculated solubility parameters.

Correlating structural rules with electronic properties of ligand-protected alloy nanoclusters.

Thiolate protected gold nanoclusters (TPNCs) are a unique class of nanomaterials finding applications in various fields, such as biomedicine, optics, and catalysis. The atomic precision of their structure, characterized through single crystal x-ray diffraction, enables the accurate investigation of their physicochemical properties through electronic structure calculations. Recent experimental efforts have led to the successful heterometal doping of TPNCs, potentially unlocking a large domain of bimetallic TPNCs for targeted applications. However, how TPNC size, bimetallic composition, and location of dopants influence electronic structure is unknown. To this end, we introduce novel structure-property relationships (SPRs) that predict electronic properties such as ionization potential (IP) and electron affinity (EA) of AgAu TPNCs based on physically relevant descriptors. The models are constructed by first generating a hypothetical AgAu TPNC dataset of 368 structures with sizes varying from 36 to 279 metal atoms. Using our dataset calculated with density functional theory (DFT), we employed systematic analyses to unravel size, composition, and, importantly, core-shell effects on TPNC EA and IP behavior. We develop generalized SPRs that are able to predict electronic properties across the AgAu TPNC materials space. The models leverage the same three fundamental descriptors (i.e., size, composition, and core-shell makeup) that do not require DFT calculations and rely only on simple atom counting, opening avenues for high throughput bimetallic TPNC screening for targeted applications. This work is a first step toward finely controlling TPNC electronic properties through heterometal doping using high throughput computational means.

Investigation of the reactivity properties of a thiourea derivative with anticancer activity by DFT and MD simulations.

Spectroscopic analysis of 1-(2-fluorophenyl)-3-[3-(trifluoromethyl)phenyl]thiourea (FPTT) is reported. Experimental and theoretical analyses of FPTT, with molecular dynamics (MD) simulations, are reported for finding different parameters like identification of suitable excipients, interactions with water, and sensitivity towards autoxidation. Molecular dynamics and docking show that FPTT can act as a potential inhibitor for new drug. Additionally, local reactivity, interactivity with water, and compatibility of FPTT molecule with frequently used excipients have been studied by combined application of density functional theory (DFT) and MD simulations. Analysis of local reactivity has been performed based on selected fundamental quantum-molecular descriptors, while interactivity with water was studied by calculations of radial distribution functions (RDFs). Compatibility with excipients has been assessed through calculations of solubility parameters, applying MD simulations. Graphical abstract Reactive sites identified.

An improved practical approach for estimating catchment‐scale response functions through wavelet analysis

AbstractCatchment‐scale response functions, such as transit time distribution (TTD) and evapotranspiration time distribution (ETTD), are considered fundamental descriptors of a catchment's hydrologic and ecohydrologic responses to spatially and temporally varying precipitation inputs. Yet, estimating these functions is challenging, especially in headwater catchments where data collection is complicated by rugged terrain, or in semi‐arid or sub‐humid areas where precipitation is infrequent. Hence, we developed practical approaches for estimating both TTD and ETTD from commonly available tracer flux data in hydrologic inflows and outflows without requiring continuous observations. Using the weighted wavelet spectral analysis method of Kirchner and Neal [2013] for δ18O in precipitation and stream water, we calculated TTDs that contribute to streamflow via spatially and temporally variable flow paths in a sub‐humid mountain headwater catchment in Arizona, United States. Our results indicate that composite TTDs (a combination of Piston Flow and Gamma TTDs) most accurately represented this system for periods up to approximately 1 month, and that a Gamma TTD was most appropriate thereafter during both winter and summer seasons and for the overall time‐weighted TTD; a Gamma TTD type was applicable for all periods during the dry season. The TTD results also suggested that old waters, i.e., beyond the applicable tracer range, represented approximately 3% of subsurface contributions to streamflow. For ETTD and using δ18O as a tracer in precipitation and xylem waters, a Gamma ETTD type best matched the observations for all seasons and for the overall time‐weighted pattern, and stable water isotopes were effective tracers for the majority of vegetation source waters. This study addresses a fundamental question in mountain catchment hydrology; namely, how do the spatially and temporally varying subsurface flow paths that support catchment evapotranspiration and streamflow modulate water quantity and quality over space and time.

Structure of communities in semantic networks of biomedical research on disparities in health and sexism

ResumenIntroducción. Como una iniciativa para mejorar la calidad de la atención sanitaria, en la investigación biomédica se ha incrementado la tendencia centrada en el estudio de las disparidades en salud y sexismo.Objetivo. Caracterizar la evidencia científica sobre la disparidad en salud definida como la brecha existente entre la distribución de la salud y el posible sesgo por sexo en el acceso a los servicios médicos.Materiales y métodos. Se hizo una búsqueda simultánea de la literatura científica en la base de datos Medline PubMed de dos descriptores fundamentales: Healthcare disparities y Sexism. Posteriormente, se construyó una red semántica principal y se determinaron algunas subunidades estructurales (comunidades) para el análisis de los patrones de organización de la información. Se utilizó el programa de código abierto Cytoscape para el analisis y la visualización de las redes y el MapEquation, para la detección de comunidades. Asimismo, se desarrolló código ex profeso disponible en un repositorio de acceso público. Resultados. El corpus de la red principal mostró que los términos sobre las enfermedades del corazón fueron los descriptores de condiciones médicas más concurrentes. A partir de las subunidades estructurales, se determinaron los patrones de información relacionada con las políticas públicas, los servicios de salud, los factores sociales determinantes y los factores de riesgo, pero con cierta tendencia a mantenerse indirectamente conectados con los nodos relacionados con condiciones médicas.Conclusiones. La evidencia científica indica que la disparidad por sexo sí importa para la calidad de la atención de muchas enfermedades, especialmente aquellas relacionadas con el sistema circulatorio. Sin embargo, aún se percibe un distanciamiento entre los factores médicos y los sociales que dan lugar a las posibles disparidades por sexo.

Photodegradation of polychlorinated diphenyl sulfides (PCDPSs) under simulated solar light irradiation: Kinetics, mechanism, and density functional theory calculations

The direct photolysis of 25 individual polychlorinated diphenyl sulfides (PCDPSs) substituted with 1–7 chlorine atoms was investigated using a 500-W Xe lamp. Photolysis of PCDPSs followed pseudo-first-order kinetics, with the higher chlorinated diphenyl sulfides generally degrading faster than the lower chlorinated congeners. A quantitative structure-activity relationship model to predict the photolysis rates of PCDPSs was developed using 16 fundamental quantum chemical descriptors. We found that the substitution pattern for chlorine atoms, the dipole moment, and ELUMO − EHOMO were major factors in the photolysis of PCPDSs. The reaction kinetics, products, and photodegradation pathways of 2,2′,3′,4,5-pentachlorodiphenyl sulfide (PeCDPS) suggest hydroxylation, direct photooxidation, the C–S bond cleavage reaction, and hydroxyl substitution were mainly involved in the photodegradation process, leading to the formation of 13 intermediates, detected by an electrospray time-of-flight mass spectrometer. The initial reaction sites of PCDPSs under photolysis were rationalized by density functional theory calculations. Anions (Cl−, SO42−, NO3−, and HCO3−) and Co2+ had no influence on the removal of PeCDPS, while Fe3+, Cu2+, and HA decreased the photolysis efficiency of PeCDPS. This report is the first to develop a logk quantitative structure–property relationships (QSPR) model of 25 PCDPSs and to describe mechanistic pathways for the photolysis of PeCDPS.

Analytical equations for the connectivity matrices and node positions of minimal and extended tensegrity plates

Tensegrity structures are three-dimensional networks of truss members loaded in tension or compression. The location of the end points of the truss members, denoted as the nodes, and the associated node-member connectivity matrices are the fundamental descriptors in the modeling and design of tensegrity structures. This paper presents systematic analytical formulas for such node locations and connectivity matrices for tensegrity plates of two different topologies. The formulas apply to plates of any thickness, diameter, and complexity. As application examples, dynamic simulations demonstrating a strategy for morphing the planar plates toward domes are studied. The presented formulas allow for efficient computations and can be employed in the numerical analysis and design of shape-controllable antennas and mirrors, architectural constructions, and other applications based on tensegrity plate and dome-like structures.

Automated simultaneous assignment of bond orders and formal charges

Bond orders and formal charges are fundamental chemical descriptors. In cheminformatic applications it is necessary to be able to assign these properties to a given molecular structure automatically, given minimal input information. Here we describe a method for determining the bond order and formal charge assignments from only the atom types and connectivity. Our method utilises a graph theoretical description of electron positions. Each electron position assignment is scored according to lookup tables of atomic and bond dissociation energies derived from quantum chemical calculations. We tested three different optimisation methods—local optimisation, an A* pathfinding method, and an FPT optimisation method utilising tree decompositions—for finding the best electron position assignment, from which the bond orders and formal charges are extracted. We show that our method can assign bond orders and formal charges at a high degree of accuracy across a wide range of molecules from two different databases, and that the FPT algorithm provides the best combination of speed and accuracy.

Pectoral girdle and forelimb musculoskeletal function in the echidna ( Tachyglossus aculeatus ): insights into mammalian locomotor evolution

Although evolutionary transformation of the pectoral girdle and forelimb appears to have had a profound impact on mammalian locomotor and ecological diversity, both the sequence of anatomical changes and the functional implications remain unclear. Monotremes can provide insight into an important stage of this evolutionary transformation, due to their phylogenetic position as the sister-group to therian mammals and their mosaic of plesiomorphic and derived features. Here we build a musculoskeletal computer model of the echidna pectoral girdle and forelimb to estimate joint ranges of motion (ROM) and muscle moment arms (MMA)—two fundamental descriptors of biomechanical function. We find that the echidna's skeletal morphology restricts scapulocoracoid mobility and glenohumeral flexion–extension compared with therians. Estimated shoulder ROMs and MMAs for muscles crossing the shoulder indicate that morphology of the echidna pectoral girdle and forelimb is optimized for humeral adduction and internal rotation, consistent with limited in vivo data. Further, more muscles act to produce humeral long-axis rotation in the echidna compared to therians, as a consequence of differences in muscle geometry. Our musculoskeletal model allows correlation of anatomy and function, and can guide hypotheses regarding function in extinct taxa and the morphological and locomotor transformation leading to therian mammals.