Lowest Energy Configuration Research Articles

IoT based real-time water quality monitoring system in water treatment plants (WTPs)

This study presents the development and implementation of an Internet of Things (IoT)-based real-time water quality monitoring system tailored for water treatment plants (WTPs). The system integrates advanced sensor technologies to continuously monitor key water quality parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), and temperature. Data collected by these sensors is transmitted through a robust communication network to a centralized monitoring platform that utilizes cloud-based storage and analytics. The system's design includes a PLC-based control mechanism, allowing for flexible setup modifications and the easy addition of new monitoring parameters. The IoT-based system, powered by a low-energy 29-W configuration, offers accurate and reliable data with a minimal error margin of 0.1–0.2 across various parameters. The research highlights the system's ability to provide real-time alerts, historical data logging, and remote monitoring, all of which contribute to enhanced operational efficiency, proactive maintenance, and informed decision-making. This innovative approach to water quality management not only improves the effectiveness of WTP operations but also ensures environmental sustainability and public health safety. The study underscores the significant potential of IoT technologies in revolutionizing water quality monitoring practices.

Self-assembled properties of water cuboids

Although liquid water and ice are characterised by predominantly tetrahedral coordination, small water clusters often exhibit a cubic structural motif. In this article, the stability of hydrogen-bonded water nanostructures in the form of n 1×n 2×n 3 cuboids is investigated. The requirement for complete hydrogen bonding imposes a strong constraint on their structure. The equations of the donor–acceptor balance are derived, from which all possible forms of completely hydrogen-bonded cuboids are deduced. The flexible non-additive AMOEBA potential is used for geometric optimisation of the set of configurations with different directions of hydrogen (H-) bonds. It has been established that the majority of completely hydrogen-bonded structures are formed by one layer of fused cubes. The lowest energy configurations of the, in fact, bilayer structures are formed by trans-configurations of transverse pairs of molecules, although some H-bonds are broken in this case. It is noted that the structure of the cuboids may result from the assembly of short nanotubes and smaller cuboids. On the other hand, unrealised H-bonds at the corners of the cuboids make it possible to assemble larger bilayer structures.

Qudit-based variational quantum eigensolver using photonic orbital angular momentum states.

Solving the electronic structure problem is a notorious challenge in quantum chemistry and material science. Variational quantum eigensolver (VQE) is a promising hybrid classical-quantum algorithm for finding the lowest-energy configuration of a molecular system. However, it typically requires many qubits and quantum gates with substantial quantum circuit depth to accurately represent the electronic wave function of complex structures. Here, we propose an alternative approach to solve the electronic structure problem using VQE with a single qudit. Our approach exploits a high-dimensional orbital angular momentum state of a heralded single photon and notably reduces the required quantum resources compared to conventional multi-qubit-based VQE. We experimentally demonstrate that our single-qudit-based VQE can efficiently estimate the ground state energy of hydrogen (H2) and lithium hydride (LiH) molecular systems corresponding to two- and four-qubit systems, respectively. We believe that our scheme opens a pathway to perform a large-scale quantum simulation for solving more complex problems in quantum chemistry and material science.

Introducing KICK-MEP: exploring potential energy surfaces in systems with significant non-covalent interactions.

Exploring potential energy surfaces (PES) is fundamental in computational chemistry, as it provides insights into the relationship between molecular energy, geometry, and chemical reactivity. We introduce Kick-MEP, a hybrid method for exploring the PES of atomic and molecular clusters, particularly those dominated by non-covalent interactions. Kick-MEP computes the Coulomb integral between the maximum and minimum electrostatic potential values on a 0.001 a.u. electron density isosurface for two interacting fragments. This approach efficiently estimates interaction energies and selects low-energy configurations at reduced computational cost. Kick-MEP was evaluated on silicon-lithium clusters, water clusters, and thymol encapsulated within Cucurbit[7]uril, consistently identifying the lowest energy structures, including global minima and relevant local minima. Kick-MEP generates an initial population of molecular structures using the stochastic Kick algorithm, which combines two molecular fragments (A and B). The molecular electrostatic potential (MEP) values on a 0.001 a.u. electron density isosurface for each fragment are used to compute the Coulomb integral between them. Structures with the lowest Coulomb integral are selected and refined through gradient-based optimization and DFT calculations at the PBE0-D3/Def2-TZVP level. Molecular docking simulations for the thymol-Cucurbit[7]uril complex using AutoDock Vina were performed for benchmarking. Kick-MEP was validated across different molecular systems, demonstrating its effectiveness in identifying the lowest energy structures, including global minima and relevant local minima, while maintaining a low computational cost.

Solvent-Regulated Formation of Metal/Metal-Oxo Nodes in Two Indium Metal-Organic Frameworks: Syntheses, Structures, Selective Gas Adsorption and Fluorescence Sensor Properties.

Two water-stable indium metal-organic frameworks, (NH2Me2)3[In3(BTB)4] ⋅ 12DMA ⋅ 4.5H2O (In-MOF-1) and (NH2Me2)9[In9O6(BTB)8(H2O)4(DMSO)4] ⋅ 27DMSO ⋅ 21H2O (In-MOF-2) (BTB=4,4',4''-benzene-1,3,5-tribenzoate) with 3D interpenetrated structure has been constructed by regulating solvents. Structure analysis revealed that In-MOF-1 has a three-dimensional (3D) structure with a single metal core, while In-MOF-2 features an octahedron cage constructed by three kinds of metal clusters to further form a 3D structure. The fluorescence investigations showed that In-MOF-1 and In-MOF-2 are potential MOF-based fluorescent sensors to detect acetone and Fe3+ ions in EtOH or water with high sensitivity, excellent selectivity, recyclability and a low limit of detection. Moreover, the fluorescence mechanisms of In-MOF-1 and In-MOF-2 toward acetone and Fe3+ ions were further explained. In addition, In-MOF-2 has higher thermal and framework stability than In-MOF-1. The activated In-MOF-2 presents a high BET surface area of 998.82 m2g-1 and a pore size distribution of 8 to 16 Å. At the same time, In-MOF-2 exhibits high selective CO2 adsorption for CO2/CH4 and CO2/N2, respectively. Furthermore, the adsorption sites and adsorption isotherms were predicted using grand canonical Monte Carlo (GCMC) simulations, and the adsorption energy of the lowest-energy adsorption configuration was calculated using molecular dynamics (MD) simulations.

Massive Assessment of the Geometries of Atmospheric Molecular Clusters.

Atmospheric molecular clusters are important for the formation of new aerosol particles in the air. However, current experimental techniques are not able to yield direct insight into the cluster geometries. This implies that to date there is limited information about how accurately the applied computational methods depict the cluster structures. Here we massively benchmark the molecular geometries of atmospheric molecular clusters. We initially assessed how well different DF-MP2 approaches reproduce the geometries of 45 dimer clusters obtained at a high DF-CCSD(T)-F12b/cc-pVDZ-F12 level of theory. Based on the results, we find that the DF-MP2/aug-cc-pVQZ level of theory best resembles the DF-CCSD(T)-F12b/cc-pVDZ-F12 reference level. We subsequently optimized 1283 acid-base cluster structures (up to tetramers) at the DF-MP2/aug-cc-pVQZ level of theory and assessed how more approximate methods reproduce the geometries. Out of the tested semiempirical methods, we find that the newly parametrized atmospheric molecular cluster extended tight binding method (AMC-xTB) is most reliable for locating the correct lowest energy configuration and yields the lowest root mean square deviation (RMSD) compared to the reference level. In addition, we find that the DFT-3c methods show similar performance as the usually employed ωB97X-D/6-31++G(d,p) level of theory at a potentially reduced computational cost. This suggests that these methods could prove to be valuable for large-scale screening of cluster structures in the future.

Mechanism of Fatigue-Life Extension Due to Dynamic Strain Aging in Low-Carbon Steel at High Temperature.

An enhancement in fatigue life for ferrite-pearlite low-carbon steel (LCS) at high temperature (HT) has been discovered, where it increased from 190,873 cycles at room temperature (RT) to 10,000,000 cycles at 400 °C under the same stress conditions. To understand the mechanism behind this phenomenon, the evolution of microstructure and dislocation density during fatigue tests was comprehensively investigated. High-power X-ray diffraction (XRD) was employed to analyze the evolution of total dislocation density, while Electron Backscatter Diffraction (EBSD) and High-Resolution EBSD (HR-EBSD) were conducted to reveal the evolutions of kernel average misorientation (KAM), geometrically necessary dislocations (GND) and elastic strains. Results indicate that the enhancement was attributed to the dynamic strain aging (DSA) effect above the upper temperature limit, where serration and jerky flow disappeared but hindrance of dislocations persisted. Due to the DSA effect, periods of increase and decrease in the total dislocations were observed during HT fatigue tests, and the fraction of screw dislocations increased continuously, caused by viscous movement of the screw dislocations. Furthermore, the increased fraction of screw dislocations resulted in a lower energy configuration, reducing slip traces on sample surfaces and preventing fatigue-crack initiation.

Unraveling the mechanism of carbonic anhydrase IX inhibition by alkaloids from Ruta chalepensis: A synergistic analysis of in vitro and in silico data

Due to the pivotal role of carbonic anhydrase IX (CA IX) in pathological conditions, there's a pressing need for novel inhibitors to improve patient outcomes and clinical management. Herein, we investigated the inhibitory efficacy of six alkaloids from Ruta chalepensis against CA IX through in vitro inhibition assay and computational modeling. Skimmianine and maculosidine displayed significant inhibitory activity in vitro, with low IC50 values of 105.2 ± 3.2 and 295.7 ± 14.1 nM, respectively. Enzyme kinetics analyses revealed that skimmianine exhibited a mixed inhibition mode, contrasting with the noncompetitive inhibition mechanism observed for the reference drug (acetazolamide), as indicated by intersecting lines in the Lineweaver-Burk plots. The findings of docking calculations revealed that skimmianine and maculosidine exhibited extensive polar interactions with the enzyme. These alkaloids demonstrate substantial binding interactions and occupy identical binding site as acetazolamide, thereby enhancing their efficacy as inhibitors of CA IX. Utilizing a 100 ns molecular dynamics (MD) simulation, the dynamic interactions between isolated alkaloids and CA IX were intensively assessed. Analysis of diverse MD parameters revealed that skimmianine and maculosidine displayed consistent trajectories and notable energy stabilization during their interaction with CA IX. The findings of MM/PBSA analysis depicted the minimum binding free energy for skimmianine and maculosidine. In addition, the Potential Energy Landscape (PEL) analysis revealed distinct and stable conformational states for the CA IX-ligand complexes, with Skimmianine showing the most stable and lowest energy configuration. These computational findings align with experimental results, emphasizing the potential efficacy of skimmianine and maculosidine as inhibitors of CA IX.

Hybrid Ising Machine and Reinforcement Learning Approach to Folding Lattice Proteins

The hydrophobic polarity (HP) protein folding model is a simplified computation model utilized to study the folding of proteins into their three-dimensional structures. Understanding how proteins fold and misfold is fundamental to designing effective, safe drugs, allowing for targeted therapies and better patient outcomes. The amino acids of a protein can be categorized as either hydrophobic or polar, where the hydrophobic beads tend to cluster together in a properly folded protein to minimize exposure to the surrounding environment. Determining the lowest energy configurations of the HP model is a non-deterministic polynomial (NP) hard problem. One method is formulating the HP model as a quadratic unconstrained binary optimization (QUBO) problem which can be mapped to an Ising machine. In the Ising model, the lowest energy state of the system represents the correct lattice configuration of the folded protein. The Ising machine technique utilized of simulated annealing allows for future application of the work on an integrated silicon photonic chip. Alternatively, reinforcement learning designed with an energy-based reward function can optimize the HP model by adapting to the environment without the consequence of remaining in local minima like that of the Ising machine. A hybrid pipeline combining the Ising machine and reinforcement learning sequentially was designed to determine the two-dimensional lattice configurations of various protein chain lengths and sequences, combining the parallel optimization of the Ising machine on an integrated photonic platform and the adaptability of the reinforcement learning algorithm. Results found that this hybrid model improved the accuracy and ability of determining the lowest energy state in contrast to the Ising machine alone as well as improved the efficiency by reducing the number of iterations required when compared solely to reinforcement learning. Future work focuses on implementing a parallel pipeline approach as well as adapting to a three-dimensional model.

Enhancing ZnO/Si Heterojunction Solar Cells: A Combined Experimental And Simulation Approach

In this study, we explore the fabrication and optimization of ZnO/Si heterojunction solar cells to enhance their performance through precise control of electron affinity and bandgap properties. ZnO thin films were synthesized using thermal oxidation in a high-vacuum chamber, followed by annealing to improve crystallinity and electrical characteristics. The photovoltaic performance of the ZnO/Si heterojunction solar cells was systematically characterized, and Quantum ESPRESSO simulations were employed to refine the electronic properties of ZnO. Our results show significant improvements in open-circuit voltage, short-circuit current density, and overall conversion efficiency. The optimization of ZnO/Si heterojunction solar cells involves enhancing the electronic properties of ZnO thin films. Quantum ESPRESSO simulations were utilized to optimize the ZnO structure, calculate the band structure and density of states (DOS), and study the effects of Ga and Mg doping on the electronic properties of ZnO. The initial step in our study involved the structural optimization of ZnO to determine its lowest energy configuration. The optimization of the band offset engineering to improve the efficiency of n-ZnO/p-Si photovoltaic cells was found to be critical. Doping ZnO with Ga and Mg improved the band alignment with Si, reduced recombination losses, and enhanced charge carrier mobility. Our findings underscore the potential of optimized ZnO/Si heterojunction solar cells for high-efficiency solar energy conversion, demonstrating their viability as cost-effective and efficient solutions for renewable energy applications. This study highlights the importance of precise material engineering and simulation-driven optimization in developing advanced photovoltaic devices.

Structural investigation of the complexation between vitamin B12 and per- and polyfluoroalkyl substances: Insights into degradation using density functional theory

Environmental remediation of per- and polyfluoroalkyl substances (PFAS) has become a significant research topic in recent years due to the fact that these materials are omnipresent, resistant to degradation and thus environmentally persistent. Unfortunately, they have also been shown to cause health concerns. PFAS are widely used in industrial applications and consumer products. Vitamin B12 (B12) has been identified as being catalytically active towards a variety of halogenated compounds such as PFAS. It has also been shown to be effective when using sulfide as a reducing agent for B12. This is promising as sulfide is readily available in the environment. However, there are many unknowns with respect to PFAS interactions with B12. These include the reaction mechanism and B12's specificity for PFAS with certain functionalization(s). In order to understand the specificity of B12 towards branched PFAS, we examined the atomistic interactions between B12 and eight different PFAS molecules using Density Functional Theory (B3LYP/cc-pVDZ). The PFAS test set included linear PFAS and their branched analogs, carboxylic acid and sulfonic acid headgroups, and aromatic and non-aromatic cyclic structures. Conformational analyses were carried out to determine the lowest energy configurations. This analysis showed that small chain PFAS such as perfluorobutanoic acid interact with the cobalt center of B12. Bulkier PFAS prefer to interact with the amine and carbonyl groups on the sidechains of the B12 ring system. Furthermore, computed complexation energies determined that, in general, branched PFAS (e.g. perfluoro-5-methylheptane sulfonic acid) interact more strongly than linear molecules (e.g. perfluorooctanesulfonic acid). Our results indicate that it may be possible to alter the interactions between B12 and PFAS by synthetically modifying the sidechains of the ring structure.

ArtiSAN: navigating the complexity of material structures with deep reinforcement learning

Abstract Finding low-energy atomic ordering in compositionally complex materials is one of the hardest problems in materials discovery, the solution of which can lead to breakthroughs in functional materials—from alloys to ceramics. In this work, we present the Artificial Structure Arranging Net (ArtiSAN)—a reinforcement learning agent utilizing graph representation that is trained to find low-energy atomic configurations of multicomponent systems through a series of atomic switch operations. ArtiSAN is trained on small alloy supercells ranging from binary to septenary. Strikingly, ArtiSAN generalizes to much larger systems of more than a thousand atoms, which are inaccessible with state-of-the-art methods due to the combinatorially larger search space. The performance of the current ArtiSAN agent is tested and deployed on several compositions that can be correlated with known experimental and high-fidelity computational structures. ArtiSAN demonstrates transfer across size and composition and finds physically meaningful structures using no energy evaluation calls once fully trained. While ArtiSAN will require further modifications to capture all variability in structure search, it is a remarkable step towards solving the structural part of the problem of disordered materials discovery.

Global Optimization of Cation Ordering in Perovskites by Recommendation-Based Basin-Hopping.

Cation ordering in multication perovskites is related to many important material properties and performances, but computational determination of the cation ordering remains a major challenge. Here, we propose a new computational approach by introducing a machine learning recommender system into the basin-hopping framework (RBH) for optimizing cation ordering. Taking the electrocatalyst Ba0.5Sr0.5Co0.8Fe0.2O3 (BSCF5582) as a showcase example, we found that the efficiency of RBH in identifying low-energy configurations outperforms the methods of cluster expansion and conventional basin-hopping. The RBH results revealed that the BSCF5582 catalyst tended to have a layered ordering of A-site cations and disordered B-site cations both in bulk and on the surfaces. Further, on the A-site-terminated surface, we found the segregation of large Ba atoms. Similarly, on the A-site- terminated surface of the recently developed Cs0.2Sr0.8Co0.4Fe0.6O3 (CSCF2846) catalyst, layered ordering at the A-site and surface enrichment of large Cs atoms were also found. The layered ordering was robust against thermal effects, as found from molecular dynamics simulations at 800 K. This work provides a new approach for thermodynamic global optimization of chemical ordering in multicomponent materials.

High ionic conductivity materials Li3YBr6 and Li3LaBr6 for solid-state batteries: first-principles calculations

High ionic conductivity solid-state electrolytes are essential for powerful solid-state lithium-ion batteries. With density functional theory andab initiomolecular dynamics simulations, we investigated the crystal structures of Li3YBr6and Li3LaBr6. The lowest energy configurations with uniform distribution of lithium ions were identified. Both materials have wide electrochemical stability windows (ESW): 2.64 V and 2.57 V, respectively. The experimental ESW for Li3YBr6is 2.50 V. Through extrapolating various temperature diffusion results, the conductivity of Li3YBr6was obtained at room temperature, approximately 3.9 mS cm-1, which is comparable to the experimental value 3.3 mS cm-1. Li3LaBr6has a higher conductivity, a 100% increase compared with Li3YBr6. The activation energies of Li3YBr6and Li3LaBr6through the Arrhenius plot are 0.26 eV and 0.24 eV, respectively, which is also close to the experimental value of 0.30 eV for Li3YBr6. This research explored high ionic conductivity halide materials and will contribute to developing solid-state lithium-ion batteries.

Microstructural decay of matrix and precipitates during rolling contact fatigue in a martensitic dual-hardening bearing steel

We investigate the microstructural degradation during rolling contact fatigue (RCF) in a martensitic dual-hardening bearing steel. The dual-hardening steel makes use of both carbide precipitation and intermetallic precipitation hardening. The microstructural degradation leading to fatigue failure is studied using electron microscopy, atom probe tomography, and synchrotron X-ray diffraction (SXRD). The initial microstructure of the steel consists of tempered martensite with a fine dispersion of secondary M7C3, and NiAl precipitates. During RCF testing at 2.2 GPa contact pressure, ferrite microbands develop and the partial dissolution of NiAl and M7C3 precipitates occur within the ferrite microbands. For the RCF testing at higher contact pressure of 2.8 GPa, nanosized ferrite grains develop in the ferrite microbands. The SXRD analysis reveals a decrease in dislocation density in the sub-surface region experiencing microstructural degradation. This is believed to be associated with the rearrangement of dislocations into low energy configuration cells. We conclude this manuscript by proposing a microstructure decay mechanism for martensitic dual-hardening bearing steel that provides insights in the fatigue initiation process.

Molecular Insights into Adhesion at Interface of Geopolymer Binder and Cement Mortar.

The degradation of concrete and reinforced concrete structures is a significant technical and economic challenge, requiring continuous repair and rehabilitation throughout their service life. Geopolymers (GPs), known for their high mechanical strength, low shrinkage, and durability, are being increasingly considered as alternatives to traditional repair materials. However, there is currently a lack of understanding regarding the interface bond properties between new geopolymer layers and old concrete substrates. In this paper, using advanced computational techniques, including quantum mechanical calculations and stochastic modeling, we explored the adsorption behavior and interaction mechanism of aluminosilicate oligomers with different Si/Al ratios forming the geopolymer gel structure and calcium silicate hydrate as the substrate at the interface bond region. We analyzed the electron density distributions of the highest occupied and lowest unoccupied molecular orbitals, examined the reactivity indices based on electron density functional theory, performed Mulliken charge population analysis, and evaluated global reactivity descriptors for the considered oligomers. The results elucidate the mechanisms of local and global reactivity of the oligomers, the equilibrium low-energy configurations of the oligomer structures adsorbed on the surface of C-(A)-S-H(I) (100), and their adsorption energies. These findings contribute to a better understanding of the adhesion properties of geopolymers and their potential as effective repair materials.

The importance of long range Fe–Fe interactions in magnetically ordered 2D Fe0.2Ni0.8/Ni(001) and Fe0.3Ni0.7/Ni(001) alloy systems

We develop a theoretical approach to study the magnonic properties of the 2D ordered surface Fe–Ni alloys with 20% and 30% Fe on a face-centered cubic Ni(001) surface substrate. The surface alloy nanostructures in stable equilibrium are determined by computing the lowest energy configurations of constitutive supercells of the ordered alloys. The magnetic exchange constants of the ground state of the stable systems are calculated to define their Heisenberg Hamiltonians using the DFT Spin-Polarized Relativistic Korringa–Kohn–Rostoker (SPRKKR) method and considering Fe–Fe interactions up to third nearest neighbors. To determine the propagating exchange spin-wave modes, evanescent modes, and the consequent magnonic properties, the spin dynamics of the magnetic surface alloy nanostructures are solved using the phase field matching theory (PFMT). Our results indicate that the Fe–Fe exchange interactions up to third nearest neighbors are vital for accurately describing their magnonics. In particular, the exchange interactions influence the form of dispersion branches for the high-energy spin-wave modes of the Fe–Ni surface alloys considered. Appropriate Green’s functions are also derived from the PFMT method to compute the local densities of states (LDOS) for the atomic sites at the surface boundary. For the case of 20% Fe surface alloy, the Fe–Fe exchange interactions shift the Fe LDOS peaks to lower frequency intervals. We also distinguish the emergence of remarkable LDOS peaks for the he case of the 30% Fe surface alloy at specific Ni sites. These peaks change positions under second and third nearest neighbor Fe–Fe interactions, heralding new accessible spin-wave channels. Our results contribute to establishing a systematic understanding of the magnonics of the magnetic surface alloys of transition metal (TM) materials. This new knowledge will enable the study of the quantum transport of spin waves at interfacial alloy nanojunctions composed of such materials. The present DFT-PFMT theoretical approach is general and can be applied to other surface TM alloy magnetic nanostructures.

Escaping Kinetic Traps Using Nonreciprocal Interactions.

Kinetic traps are a notorious problem in equilibrium statistical mechanics, where temperature quenches ultimately fail to bring the system to low energy configurations. Using multifarious self-assembly as a model system, we introduce a mechanism to escape kinetic traps by utilizing nonreciprocal interactions between components. Introducing nonequilibrium effects offered by broken action-reaction symmetry in the system pushes the trajectory of the system out of arrested dynamics. The dynamics of the model is studied using tools from the physics of interfaces and defects. Our proposal can find applications in self-assembly, glassy systems, and systems with arrested dynamics to facilitate escape from local minima in rough energy landscapes.

Diphenyl selenoxide: Unraveled peculiarities in methanol and acetonitrile solutions

We hereby report the volumetric, spectroscopic (UV–Vis, FTIR), and voltammetric properties together with the comprehensive atomistic simulations of diphenyl selenoxide (Ph2SeO) in acetonitrile (ACN) and methanol (MEOH). The solvation effects manifested through densimetry, UV–Vis, and FTIR spectra have been rationalized for the first time. The anodic oxidation and cathodic reduction of Ph2SeO proceeded more easily in a polar aprotic solvent, such as ACN, compared to a polar protic solvent, such as MEOH. The independent atomistically precise simulation studies examining the universe of the stationary point molecular configurations provided solid proof for a substantially better solvation performance of MEOH relative to ACN. The higher degree of conformational flexibility of the MEOH molecules, their strong hydrogen bonding with the seleneoxide group, and sterical confinement of the electrophilic interaction center of Ph2SeO unraveled via numerous low-energy molecular configurations altogether stand beyond a higher maximum homogeneous concentration of the amphiphilic seleneoxide solute in MEOH than in ACN. The reported results reveal the peculiarities of the diphenyl seleneoxide-containing solutions. They are addressed to the researchers representing the field of solution chemistry.

Hardness and microstructural evolution of CoCrFeNi high-entropy alloys during severe plastic deformation

In this study, the evolution of hardness and microstructure in a CoCrFeNi high-entropy alloy processed by high-pressure torsion was investigated. An initial increase in hardness was attributed to the rapid increase in low-angle grain boundaries (LAGBs), accompanied by a high density of dislocations. With continuous straining, some LAGBs evolved into high-angle grain boundaries and completed the grain boundary strengthening. A remarkable increase in hardness was achieved in the final stage of hardness enhancement, primarily due to the formation of numerous twins within nanograins, which were mainly formed by the emission of Shockley partial dislocations emerging from grain boundaries. Furthermore, a twin trailing the stacking fault was found in the nanograin, and a critical stress for twin nucleation was estimated to be about 2.16 GPa. Defects with low-energy configurations are closely correlated with the thermal stability of nanocrystalline CoCrFeNi alloys. The defects within nanograins yielded an average strain of about 1.1 % and the corresponding strain energy was identified to about 17 J g−1.