Using the High-Entropy Approach to Obtain Multimetal Oxide Nanozymes: Library Synthesis, In Silico Structure-Activity, and Immunoassay Performance.

High-entropy nanomaterials exhibit exceptional mechanical, physical, and chemical properties, finding applications in many industries. Peroxidases are metalloenzymes that accelerate the decomposition of hydrogen peroxide. This study uses the high-entropy approach to generate multimetal oxide-based nanozymes with peroxidase-like activity and explores their application as sensors in ex vivo bioassays. A library of 81 materials was produced using a coprecipitation method for rapid synthesis of up to 100 variants in a single plate. The A and B sites of the magnetite structure, (AA')(BB'B'')2O4, were substituted with up to six different cations (Cu/Fe/Zn/Mg/Mn/Cr). Increasing the compositional complexity improved the catalytic performance; however, substitutions of single elements also caused drastic reductions in the peroxidase-like activity. A generalized linear model was developed describing the relationship between material composition and catalytic activity. Binary interactions between elements that acted synergistically or antagonistically were identified, and a single parameter, the mean interaction effect, was observed to correlate highly with catalytic activity, providing a valuable tool for the design of high-entropy-inspired nanozymes.

Investigating the role of flight phase and task difficulty on low-time pilot performance, gaze dynamics and subjective situation awareness during simulated flight.

Gaze behaviour has been used as a proxy for information processing capabilities that underlie complex skill performance in real-world domains such as aviation. These processes are highly influenced by task requirements, expertise and can provide insight into situation awareness (SA). Little research has been done to examine the extent to which gaze behaviour, task performance and SA are impacted by various task manipulations within the confines of early-stage skill development. Accordingly, the current study aimed to understand the impact of task difficulty on landing performance, gaze behaviour and SA across different phases of flight. Twenty-four low-time (<300 hours) pilots completed simulated landing scenarios under visual flight rules conditions. Traditional gaze metrics, entropybased metrics, and blink rate provided meaningful insight about the extent to which information processing is modulated by flight phase and task difficulty. The results also suggested that gaze behavior changes compensated for increased task demands and minimized the impact on task performance. Dynamic gaze analyses were shown to be a robust measure of task difficulty and pilot flight hours. Recommendations for the effective implementation of gaze behaviour metrics and their utility in examining information processing changes are discussed.

High-entropy selenides derived from Prussian blue analogues as electrode materials for sodium-ion batteries.

Transition metal selenides (TMS) have received much attention as anode materials for sodium-ion batteries (SIBs) because of their high theoretical capacity and excellent redox reversibility. However, their further development is constrained by the dissolution of transition metal ions and substantial volume changes experienced during cycling. Herein, the high-entropy Prussian blue analogues were selenized by the vapor infiltration method, resulting in the formation of a core-shell structured high-entropy selenides (HESe-6). The core-shell structure with voids and abundant selenium vacancies on the surface effectively mitigates bulk expansion and enhances electronic conductivity. Furthermore, the high-entropy property endows an ultra-stable crystal structure and inhibits the dissolution of metal ions. The ex-situ EIS and in-situ XRD results show that HESe-6 is able to be reversibly transformed into highly conductive ultrafine metal particles upon Na+ embedding, providing more Na+ reactive active sites. In addition, despite the incorporation of up to seven different elements, it exhibits minimal phase transitions during discharge/charge cycles, effectively mitigating stress accumulation. HESe-6 could retain an ultralong-term stability of 765.83 mAh g-1 after 1000 loops even at 1 A g-1. Furthermore, when coupled with the Na3V2(PO4)2O2F cathode, it maintains a satisfactory charge energy density of 303 Wh kg-1 after 300 cycles, which shows promising application prospect in the future.

Machine Learning-Based Design of Superhard High-Entropy Nitride Coatings.

Limited by the inefficiency of the conventional trial-and-error method and the boundless compositional design space of high-entropy alloys (HEAs), accelerating the discovery of superior-performing high-entropy nitride (HEN) coatings remains a formidable challenge. Herein, the superhard HEN coatings were designed and prepared using the rapidly developing data-driven model machine learning (ML). A database containing hardness and different features of HEN coatings was established and categorized into four subsets covering the information on composition, composition-physical descriptors, composition-technique parameters, and composition-physical descriptors-technique parameters. Feature engineering was employed to reduce dimensionality and interpret the impact of features on the evolution of hardness. Both root mean squared error (RMSE) and decision coefficient (R2) were applied to assess the predictive accuracy of ML models with different subsets, proportions of test set, and algorithms. The model with best predicted performance was used to explore superhard HEN coatings in a predefined virtual space. Among the generated 5-/6-/7-/8-component (excluding N) systems, the coating possessing highest hardness was individually selected for further preparation. Four newly prepared coatings achieved the superhard level with an average prediction error of 7.83%. The morphology, chemical composition, structure, and hardness of the newly prepared coatings were discussed. The nanocrystal-amorphous nanocomposite structure of the novel AlCrNbSiTiN coating with the highest hardness of 45.77 GPa was revealed. The results demonstrated that ML can effectively guide the design and composition optimization of superb-performance protective HEN coatings.

A-Site Engineering of the High-Entropy Perovskite Pr0.4La0.4Ba0.4Sr0.4Ca0.4Fe2O5+δ Cathode for Intermediate-Temperature SOFCs.

Mixed-oxygen ionic and electronic conduction is crucial for the cathode materials of solid oxide fuel cells, ensuring high efficiency and low-temperature operation. However, the electronic and oxygen ionic conductivity of traditional Fe-based layered perovskite cathode materials is low, resulting in insufficient oxygen reduction reactivity. Herein, a type of high-entropy perovskite oxide consisting of five equimolar metals, Pr0.4La0.4Ba0.4Sr0.4Ca0.4Fe2O5+δ (PLBSCF), a high-performance cobalt-free cathode derived from the PrBaFe2O5+δ (PBF), is proposed. Such A-site engineering could not only increase the oxygen vacancy concentration of PLBSCF but also give higher conductivity than PBF, thus significantly reducing the polarization impedance of the symmetric cell to only 0.052 Ω·cm2 at 750 °C. The good output performance of a single cell is also realized. The peak power density of the single cell with PLBSCF-Ce0.9Gd0.1O2-δ (GDC) as the cathode at 750 °C was 0.853 W·cm-2. Additionally, the single cell with the PLBSCF cathode exhibits a good durable performance of 100 h at 750 °C. Combining the distribution of relaxation time analysis, it can be seen that the enhancement of the oxygen reduction reaction is due to the reduction of intermediate-frequency and low-frequency resistance, indicating an improvement in the charge transfer process and adsorption/dissociation process of molecular oxygen.

Additively manufactured high-entropy alloys for hydrogen storage: Predictions.

This review paper covers an analysis of the empirical calculations, additive manufacturing (AM) and hydrogen storage of refractory high-entropy alloys undertaken to determine the structural compositions, particularly focusing on their applicability in research and experimental settings. The inventors of multi-component high-entropy alloys (HEAs) calculated that trillions of materials could be manufactured from elements in the periodic table, estimating a vast number, N = 10^100, using Stirling's approximation. The significant contribution of semi-empirical parameters such as Gibbs free energy ΔG, enthalpy of mixing ΔH mix , entropy of mixing ΔS mix , atomic size difference Δδ, valence electron concentration VEC, and electronegativity difference Δχ are to predict BCC and/or FCC phases in HEAs. Additive manufacturing facilitates the determination of refractory HEAs systems with the most stable solid-solution and single-phase, and their subsequent hydrogen storage capabilities. Hydride materials, especially those from HEAs manufactured by AM as bulk and solid materials, have great potential for H2 storage, with storage capacities that can be as high as 1.81 wt% of H2 adsorbed for a ZrTiVCrFeNi system. Furthermore, laser metal deposition (LMD) is the most commonly employed technique for fabricating refractory high entropy alloys, surpassing other methods in usage, thus making it particularly suitable for H2 storage.

Ultra-Small High-Entropy Alloy as Multi-Functional Catalyst for Ammonia Based Fuel Cells.

Ammonia fuel cells using carbon-neutral ammonia as fuel are regarded as a fast, furious, and flexible next-generation carbon-free energy conversion technology, but it is limited by the kinetically sluggish ammonia oxidation reaction (AOR), oxygen reduction reaction (ORR), and hydrogen evolution reaction (HER). Platinum can efficiently drive these three types of reactions, but its scale-up application is limited by its susceptibility to poisoning and high cost. In order to reduce the cost and alleviate poisoning, incorporating Pt with various metals proves to be an efficient and feasible strategy. Herein, PtFeCoNiIr/C trifunctional high-entropy alloy (HEA) catalysts are prepared with uniform mixing and ultra-small size of 2 ± 0.5 nm by Joule heating method. PtFeCoNiIr/C exhibits efficient performance in AOR (Jpeak = 139.8 A g-1 PGM), ORR (E1/2 = 0.87 V), and HER (E10 = 20.3 mV), outperforming the benchmark Pt/C, and no loss in HER performance at 100 mA cm-2 for 200 h. The almost unchanged E1/2 in the anti-poisoning test indicates its promising application in real fuel cells powered by ammonia. This work opens up a new path for the development of multi-functional electrocatalysts and also makes a big leap toward the exploration of cost-effective device configurations for novel fuel cells.

A comparative evaluation of measures to assess randomness in human-generated sequences.

Whether and how well people can behave randomly is of interest in many areas of psychological research. The ability to generate randomness is often investigated using random number generation (RNG) tasks, in which participants are asked to generate a sequence of numbers that is as random as possible. However, there is no consensus on how best to quantify the randomness of responses in human-generated sequences. Traditionally, psychologists have used measures of randomness that directly assess specific features of human behavior in RNG tasks, such as the tendency to avoid repetition or to systematically generate numbers that have not been generated in the recent choice history, a behavior known as cycling. Other disciplines have proposed measures of randomness that are based on a more rigorous mathematical foundation and are less restricted to specific features of randomness, such as algorithmic complexity. More recently, variants of these measures have been proposed to assess systematic patterns in short sequences. We report the first large-scale integrative study to compare measures of specific aspects of randomness with entropy-derived measures based on information theory and measures based on algorithmic complexity. We compare the ability of the different measures to discriminate between human-generated sequences and truly random sequences based on atmospheric noise, and provide a systematic analysis of how the usefulness of randomness measures is affected by sequence length. We conclude with recommendations that can guide the selection of appropriate measures of randomness in psychological research.

Unraveling Lattice-Distortion Hardening Mechanisms in High-Entropy Carbides.

Uncovering the hardening mechanisms is of great importance to accelerate the design of superhard high-entropy carbides (HECs). Herein, the hardening mechanisms of HECs by a combination of experiments and first-principles calculations are systematically explored. The equiatomic single-phase 4- to 8-cation HECs (4-8HECs) are successfully fabricated by the two-step approach involving ultrafast high-temperature synthesis and hot-press sintering techniques. The as-fabricated 4-8HEC samples possess fully dense microstructures (relative densities of up to ≈99%), similar grain sizes, clean grain boundaries, and uniform compositions. With the elimination of these morphological properties, the monotonic enhancement of Vickers hardness and nanohardness of the as-fabricated 4-8HEC samples is found to be driven by the aggravation of lattice distortion. Further studies show no evident association between the enhanced hardness of the as-fabricated 4-8HEC samples and other potential indicators, including bond strength, valence electron concentration, electronegativity mismatch, and metallic states. The work unveils the underlying hardening mechanisms of HECs and offers an effective strategy for designing superhard HECs.

Unique algorithm for the evaluation of embryo photon emission and viability.

Living cells have spontaneous ultraweak photon emission derived from metabolic reactions associated with physiological conditions. The ORCA-Quest CMOS camera (Hamamatsu Photonics, Japan) is a highly sensitive and essential tool for photon detection; its use with a microscope incubator (Olympus) enables the detection of photons emitted by embryos with the exclusion of harmful visible light. With the application of the second law of thermodynamics, the low-entropy energy absorbed and used by embryos can be distinguished from the higher-entropy energy released and detectable in their environment. To evaluate higher-entropy energy data from embryos, we developed a unique algorithm for the calculation of the entropy-weighted spectral fractal dimension, which demonstrates the self-similar structure of the energy (photons) released by embryos. Analyses based on this structure enabled the distinction of living and degenerated mouse embryos, and of frozen and fresh embryos and the background. This novel detection of ultra-weak photon emission from mouse embryos can provide the basis for the development of a photon emission embryo control system. The ultraweak photon emission fingerprints of embryos may be used for the selection of viable specimens in an ideal dark environment.

Detecting tipping points of complex diseases by network information entropy.

The progression of complex diseases often involves abrupt and non-linear changes characterized by sudden shifts that trigger critical transformations. Identifying these critical states or tipping points is crucial for understanding disease progression and developing effective interventions. To address this challenge, we have developed a model-free method named Network Information Entropy of Edges (NIEE). Leveraging dynamic network biomarkers, sample-specific networks, and information entropy theories, NIEE can detect critical states or tipping points in diverse data types, including bulk, single-sample expression data. By applying NIEE to real disease datasets, we successfully identified critical predisease stages and tipping points before disease onset. Our findings underscore NIEE's potential to enhance comprehension of complex disease development.

Comprehensive assessment of groundwater quality in the Prayagraj District, Ganga Basin.

Urbanization has severely impacted the world water resources especially the shallow groundwater systems. There is a need of a robust method for quantifying the water quality degradation, which is still a challenge for most of the urban centers across the world. In this study, a highly urbanized region of Ganga basin is selected to critically evaluate commonly used WQIs and compare with fuzzy modeling. A total of 28 water samples were collected from diverse sources (surface and groundwaters) in the vicinity of urban region covering an area of 216 km2 during the premonsoon period. TDS, TH, NO3-, and F- values were found to be above the permissible limits in 57%, 89%, 4%, and 7% samples, respectively. The WQIs (entropy and integrated) outputs were found to be similar with 89% of the samples falling under moderate category. Fuzzy modeling was carried out allowing user-defined weighting factors for the most influential ions, and the output suggested 96% of the samples falling under moderate to excellent categories. Based on the chemical results and considering the lithology of the study area, the geochemical reactions controlling the water quality were deduced. This study outlines a systematic approach of evaluating the overall water quality of an urban region highlighting the merits and limitations of WQIs. It also justifies the immediate need to generate more robust data to achieve the sustainable development goals 6 (clean water and sanitation) and 11 (sustainability of cities and human settlement).

Evaluation of the applicability of hospital-affiliated green spaces to patient recovery using the entropy weight method and grey relational analysis.

Introduction: Hospital affiliated green spaces can help patients recover and recover their physical functions, promote physical and mental relaxation, enhance health awareness, and improve overall health. However, there are still significant questions about how to scientifically construct hospital affiliated green spaces. This study examines the impact of hospital green spaces on patient rehabilitation through scientific evaluation methods, providing reference for the scientific construction of hospital affiliated green spaces. Applicability evaluation was conducted on the affiliated green spaces of three hospitals in Harbin. An evaluation system covering plants, space, accessibility, rehabilitation functions, and promotional and educational functions has been constructed. The entropy weight method is used to determine the weight of indicators, and the grey correlation analysis method is used to evaluate the suitability of green space for patient rehabilitation. Methods: The experimental results showed that the landscape accessibility index had the highest weight (0.3005) and the plant index had the lowest weight (0.1628), indicating that caring for special needs is the foundation of hospital landscapes, and plants have subtle and long-term effects on physical and mental health. In the evaluation of the rehabilitation applicability of the affiliated green spaces of various hospitals, the second hospital has the highest grey correlation degree (0.8525), followed by the tumor hospital (0.5306) and the fifth hospital (0.4846). It can be seen that the green space of the second hospital has high applicability for patient rehabilitation, but the green space of the tumor hospital and the fifth hospital needs to be improved and developed. Results and discussion: The evaluation criteria used in this study are comprehensive. The landscaping at the Third Hospital is well-planned with good plant configuration and reasonable spatial layout. However, there is insufficient consideration for accessibility in the landscape design, and the details are lacking. The rehabilitation and educational functions of the landscape are inadequate, with limited outdoor activities and low road safety. The hospital's affiliated green spaces should adhere to the principle of "appropriate scale, comprehensive functionality, and educational leisure," integrating rehabilitation and educational functions while increasing the variety of outdoor activities. In the future, emphasis should be placed on exploring the integration of landscape and rehabilitation to provide a functional site that is convenient for visiting, with improved rehabilitation facilities and an educational and enjoyable environment. The design should incorporate elements that contribute to a sense of well-being, including roads and.

High-Entropy Metal Nitride Embedded in Concave Porous Carbon Enabling Polysulfide Conversion in Lithium-Sulfur Batteries.

The practical implementation of lithium-sulfur batteries is severely hindered by the rapid capacity fading due to the solubility of the intermediate lithium polysulfides (LiPSs) and the sluggish redox kinetics. Herein, high-entropy metal nitride nanocrystals (HEMN) embedded within nitrogen-doped concave porous carbon (N-CPC) polyhedra are rationally designed as a sulfur host via a facile zeolitic imidazolate framework (ZIF)-driven adsorption-nitridation process toward this challenge. The configuration of high-entropy with incorporated metal manganese (Mn) and chromium (Cr) will optimize the d-band center of active sites with more electrons occupied in antibonding orbitals, thus promoting the adsorption and catalytic conversion of LiPSs. While the concave porous carbon not only accommodates the volume change upon the cycling processes but also physically confines and exposes active sites for accelerated sulfur redox reactions. As a result, the resultant HEMN/N-CPC composites-based sulfur cathode can deliver a high specific capacity of 1274 mAh g-1 at 0.2 C and a low capacity decay rate of 0.044% after 1000 cycles at 1 C. Moreover, upon sulfur loading of 5.0 mg cm-2, the areal capacity of 5.0 mAh cm-2 can still be achieved. The present work may provide a new avenue for the design of high-performance cathodes in Li-S batteries.

On comparative analysis of a two dimensional star gold structure via regression models.

In this research, the star gold structure with beta graphene is thoroughly examined. We mainly focus on computing degree-based topological indices, which provide information about the network's connectivity and complexity as well as structural features. In addition, we compute an entropy measure to represent the uncertainty, information richness, and degree of unpredictability in the network. Furthermore, this study explores the relationships between topological descriptors and entropy using regression models that are logarithmic, linear, and quadratic. By merging these regression models, we uncover hidden patterns and understand the underlying ideas governing the network's behaviour. Our findings shed light on the connection between topological indices and entropy. This work improves our understanding of star gold structure dynamics and provides a visual framework for interpreting their behaviour.

Similarity measure method of near-infrared spectrum combined with multi-attribute information.

Due to the high-dimensionality, redundancy, and non-linearity of the near-infrared (NIR) spectra data, as well as the influence of attributes such as producing area and grade of the sample, which can all affect the similarity measure between samples. This paper proposed a t-distributed stochastic neighbor embedding algorithm based on Sinkhorn distance (St-SNE) combined with multi-attribute data information. Firstly, the Sinkhorn distance was introduced which can solve problems such as KL divergence asymmetry and sparse data distribution in high-dimensional space, thereby constructing probability distributions that make low-dimensional space similar to high-dimensional space. In addition, to address the impact of multi-attribute features of samples on similarity measure, a multi-attribute distance matrix was constructed using information entropy, and then combined with the numerical matrix of spectral data to obtain a mixed data matrix. In order to validate the effectiveness of the St-SNE algorithm, dimensionality reduction projection was performed on NIR spectral data and compared with PCA, LPP, and t-SNE algorithms. The results demonstrated that the St-SNE algorithm effectively distinguishes samples with different attribute information, and produced more distinct projection boundaries of sample category in low-dimensional space. Then we tested the classification performance of St-SNE for different attributes by using the tobacco and mango datasets, and compared it with LPP, t-SNE, UMAP, and Fisher t-SNE algorithms. The results showed that St-SNE algorithm had the highest classification accuracy for different attributes. Finally, we compared the results of searching the most similar sample with the target tobacco for cigarette formulas, and experiments showed that the St-SNE had the highest consistency with the recommendation of the experts than that of the other algorithms. It can provide strong support for the maintenance and design of the product formula.

The Decoherent Arrow of Time and the Entanglement Past Hypothesis.

If an asymmetry in time does not arise from the fundamental dynamical laws of physics, it may be found in special boundary conditions. The argument normally goes that since thermodynamic entropy in the past is lower than in the future according to the Second Law of Thermodynamics, then tracing this back to the time around the Big Bang means the universe must have started off in a state of very low thermodynamic entropy: the Thermodynamic Past Hypothesis. In this paper, we consider another boundary condition that plays a similar role, but for the decoherent arrow of time, i.e. the subsystems of the universe are more mixed in the future than in the past. According to what we call the Entanglement Past Hypothesis, the initial quantum state of the universe had very low entanglement entropy. We clarify the content of the Entanglement Past Hypothesis, compare it with the Thermodynamic Past Hypothesis, and identify some challenges and open questions for future research.

Stimulating Electron Delocalization of Lanthanide Elements through High-Entropy Confinement to Promote Electrocatalytic Water Splitting.

High-entropy alloys (HEAs) have aroused extensive attention in the field of catalysis. However, due to the integration of multiple active sites in HEA, it exhibits excessive adsorption behavior resulting in difficult desorption of active species from the catalyst surfaces, which hinders the catalytic efficiency. Therefore, adjusting the adsorption strength of the active site in HEA to enhance the catalytic activity is of great importance. By introducing rare-earth (RE) elements into the high-entropy alloy, the delocalization of 4f electrons can be achieved through the interaction between the multimetal active site and RE, which benefits to regulate the adsorption strength of the HEA surface. Herein, the RE Ce-modified hexagonal-close-packed PtRuFeCoNiZn-Ce/C HEAs are synthesized and showed an excellent electrocatalytic activity for hydrogen evolution reaction and oxygen evolution reaction with ultralow overpotentials of 4, 7 and 156, 132 mV, respectively, to reach 10 mA cm-2 in 0.5 M H2SO4 and 1.0 M KOH solutions, and the assembled water electrolysis cell only requires a voltage of 1.43 V to reach 10 mA cm-2, which is much better than the performance of PtRuFeCoNiZn/C. Combined with the results of in situ attenuated total reflection infrared spectroscopy and density functional theory (DFT), the fundamental reasons for the improvement of catalyst activity come from two aspects: (i) local lattice distortion of HEA caused by the introduction of RE with large atomic radius induces 4f orbital electron delocalization of RE elements and enhances electron exchange between RE and active sites. (ii) The electronegativity difference between the RE element and the active site forms a surface dipole in HEA, which optimizes the adsorption of the active intermediate by the HEA surface site. This study provides an insightful idea for the rational design of high-performance HEA- and RE-based electrocatalysts.

Can geroscience be translated into healthcare?

As an introduction to this special issue on geroscience, the present work focuses on the complexity of disentangling biomolecular mechanisms of aging from biopsychosocial causes of accelerated aging. Due to this complexity, the biomolecular aging hallmarks of frailty and multimorbidity as predominant aging phenotypes in geriatrics reflect single aspects of the aging process. A possible approach to facilitate the integration of geroscience into healthcare might be to consider aging as the dynamic ratio between damage accumulation at the molecular and cellular level and resilience as strategies that prevent or repair such damage. There is a large body of evidence to show that geroscience has the potential to change healthcare; however, reaching a consensus and translating the best tool to measure aging needs more research on 1) the sensitivity of biomarkers to interventions and 2) the relationship between changes in biomarkers and changes in health trajectories.

Defect-Driven Configurational Entropy in the High-Entropy Oxide Li1.5MO3-δ.

Layered lithiated oxides are promising materials for next generation Li-ion battery cathode materials; however, instability during cycling results in poor performance over time compared to the high capacities theoretically possible with these materials. Here we report the characterizations of a Li1.47Mn0.57Al0.13Fe0.095Co0.105Ni0.095O2.49 high-entropy layered oxide (HELO) with the Li2MO3 structure where M = Mn, Al, Fe, Co, and Ni. Using electron microscopy and X-ray spectroscopy, we identify a homogeneous Li2MO3 structure stabilized by the entropic contribution of oxygen vacancies. This defect-driven entropy would not be attainable in the LiMO2 structure sometimes observed in similar materials as a secondary phase owing to the presence of fewer O sites and a 3+ oxidation state for the metal site; instead, a Li2-γMO3-δ is produced. Beyond Li2MO3, this defect-driven entropy approach to stabilizing novel compositions and phases can be applied to a wide array of future cathode materials including spinel and rock salt structures.