Neighboring Atoms Research Articles

Nonlocality and strength of interatomic interactions inducing the topological phonon phase transition

Understanding phonon behavior in semiconductors from a topological physics perspective offers opportunities to uncover extraordinary phenomena related to phonon transport and electron–phonon interactions. While various types of topological phonons have been reported in different crystalline solids, their microscopic origins remain quantitatively unexplored. In this study, analytical interatomic force constant (IFC) models are employed for wurtzite GaN and AlN to establish relationships between phonon topology and real-space IFCs. The results demonstrate that variations in the strength and nonlocality of IFCs can induce phonon phase transitions in GaN and AlN through band reversal, leading to the emergence of new Weyl phonons at the boundaries and within the Brillouin zones. Among the observed Weyl points, some remain identical in both materials under simple IFC modeling, while others exhibit variability depending on the specific case. Compared to the strength of the IFCs, nonlocal interactions have a significantly larger impact on inducing topological phonon phase transitions, particularly in scenarios modeled by the IFC model and the SW potential. The greater number of the third nearest neighbor atoms in wurtzite AlN provides more room for variations in the topological phonon phase than in GaN, resulting in more substantial changes in AlN.

Synergistic effects of interstitial and substitutional doping on the thermoelectric properties of PbS

Lead sulfide (PbS) is widely recognized as a promising n-type thermoelectric material for use in the middle-temperature range. Although it already exhibits favorable electronic and thermal properties, its thermoelectric performance could be further enhanced by addressing the disparity between the light and heavy bands in the conduction band, thereby optimizing electrical transport, and by modifying the strength of its chemical bonds to reduce lattice thermal conductivity. In this study, we demonstrate that introducing just small amounts of antimony (Sb) into PbS generates a unique combination of interstitial and substitutional doping that leads to a significant improvement in both directions. Substitutional doping enhances the degeneracy between the light and heavy bands, increasing carrier mobility. At the same time, interstitial doping introduces a new resonance state near the Fermi level, providing an additional channel for electron transport while boosting carrier concentration. These synergistic effects lead to a marked increase in the power factor of PbS, achieving an average power factor (PFavg) of 1.07 mW m−1 K−2 across the temperature range of 320–873 K. Moreover, Sb substitution for Pb induces a shift in the surrounding S atoms toward Sb, weakening their bonds with neighboring Pb atoms. This shift results in a coexistence of strong and weak chemical bonds, which effectively reduces lattice thermal conductivity. Additionally, the defect structures introduced by Sb doping effectively scatter phonons, further lowering lattice thermal conductivity. As a result, PbS doped with 0.5% Sb exhibits a figure of merit (ZT) of 0.73 at 873 K, which is approximately three times higher than that of undoped PbS.

Prediction of Protein Secondary Structures Based on Substructural Descriptors of Molecular Fragments

The accurate prediction of secondary structures of proteins (SSPs) is a critical challenge in molecular biology and structural bioinformatics. Despite recent advancements, this task remains complex and demands further exploration. This study presents a novel approach to SSP prediction using atom-centric substructural multilevel neighborhoods of atoms (MNA) descriptors for protein molecular fragments. A dataset comprising over 335,000 SSPs, annotated by the Dictionary of Secondary Structure in Proteins (DSSP) software from 37,000 proteins, was constructed from Protein Data Bank (PDB) records with a resolution of 2 Å or better. Protein fragments were converted into structural formulae using the RDKit Python package and stored in SD files using the MOL V3000 format. Classification sequence–structure–property relationships (SSPR) models were developed with varying levels of MNA descriptors and a Bayesian algorithm implemented in MultiPASS software. The average prediction accuracy (AUC) for eight SSP types, calculated via leave-one-out cross-validation, was 0.902. For independent test sets (ASTRAL and CB513 datasets), the best SSPR models achieved AUC, Q3, and Q8 values of 0.860, 77.32%, 70.92% and 0.889, 78.78%, 74.74%, respectively. Based on the created models, a freely available web application MNA-PSS-Pred was developed.

Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures

We develop a technique for predicting the Curie temperature of magnetic materials using density functional theory calculations suitable to include in high-throughput frameworks. We apply four different models, including physically relevant observables, and assess numerical constants by studying 32 ferro- and ferrimagnets. With the best-performing model, the Curie temperature can be predicted with a mean absolute error of approximately 126 K. As predictive factors, the models consider either the energy differences between the magnetic ground state and a magnetically disordered paramagnetic state, or the average constraining fields acting on magnetic moments in a disordered local moments calculation. Additionally, the energy differences are refined by incorporating the magnetic entropy of the paramagnetic state and the number of nearest magnetic neighbors of the magnetic atoms. The most advanced model is found to extend well into Fe1−xCox alloys, indicating the potential efficacy of utilizing our model in designing materials with tailored Curie temperatures by altering alloy compositions. This examination can illuminate the factors influencing magnetic transition temperatures in magnetic materials and provide insights into how they can be employed to make quantitative predictions of Curie temperatures. Our approach is not restricted to specific crystal structures or chemical compositions. It offers a more cost-effective alternative, in terms of human time and need for hands-on oversight, to other density functional theory methods for predicting the Curie temperature. As a result, it provides a practical strategy for conducting high-throughput screening for new technologically applicable magnetic materials. Alternatively, it can complement ML-based screening of magnetic materials by integrating physical principles into such approaches, thereby enhancing their prediction accuracy. Published by the American Physical Society 2024

Intracellular Redox Environment Determines Cancer‐normal Cell Selectivity of Selenium Nanoclusters

AbstractElucidating the chemical structure and intracellular action mechanisms is still the critical limit for the clinical translation of nanomedicines. Intracellular redox environments originating from cell metabolism are key factors affecting internalized drug efficacy. Herein, we engineer Se−Se/Se−S bond to assemble selenium (Se) nanoclusters (SeClus) with intracellular redox environment‐driven selective structure. Chemical structure analysis reveals that, the bonding of sulfur atom in intermediates to the two neighboring or interposition Se atoms in Se rings is the key internal driving force for SeClus formation. This nanocluster can be predominantly transformed to selenocysteine to facilitate selenoproteins synthesis in normal cells, while metabolize to cytotoxic SeO32− based on the oxidative intracellular redox environment of cancer cells. Resultantly, SeClus exhibit significant cell proliferation inhibition ability to cancer cells and impressive safety to normal cells. Taken together, this study not only clarifies the chemical nature of the atom engineering of SeClus, but also elucidates its intracellular redox environment‐oriented anticancer mechanism.

Intracellular Redox Environment Determines Cancer-normal Cell Selectivity of Selenium Nanoclusters.

Elucidating the chemical structure and intracellular action mechanisms is still the critical limit for the clinical translation of nanomedicines. Intracellular redox environments originating from cell metabolism are key factors affecting internalized drug efficacy. Herein, we engineer Se-Se/Se-S bond to assemble selenium (Se) nanoclusters (SeClus) with intracellular redox environment-driven selective structure. Chemical structure analysis reveals that, the bonding of sulfur atom in intermediates to the two neighboring or interposition Se atoms in Se rings is the key internal driving force for SeClus formation. This nanocluster can be predominantly transformed to selenocysteine to facilitate selenoproteins synthesis in normal cells, while metabolize to cytotoxic SeO3 2- based on the oxidative intracellular redox environment of cancer cells. Resultantly, SeClus exhibit significant cell proliferation inhibition ability to cancer cells and impressive safety to normal cells. Taken together, this study not only clarifies the chemical nature of the atom engineering of SeClus, but also elucidates its intracellular redox environment-oriented anticancer mechanism.

Machine-Learning-Assisted Screening of Nanocluster Electrocatalysts: Mapping and Reshaping the Activity Volcano for the Oxygen Reduction Reaction.

In computational heterogeneous catalysis, Sabatier's principle-based activity volcano plots provide an intuitive guide to catalyst design but impose a fundamental constraint on the maximum achievable catalytic performance. Recently, subnano clusters have emerged as an exciting platform, offering high noble metal utilization and superior performance for various reactions compared to extended surfaces, reflecting a complex structure-activity relationship in the non-scalable regime. However, understanding their non-monotonic catalytic activity, attributed to the large configurational space and their fluxional identity, poses a formidable challenge. Here, we present a machine learning (ML) framework that captures the non-monotonic trends in oxygen reduction reaction (ORR) activity at the subnanometer scale, attributed to their dynamic fluxional characteristics. We demonstrate a size-dependent shifting and reshaping of the ORR activity volcano, with Au replacing Pt at the peak. Leveraging only upon the non-ab initio geometric and electronic properties, our trained ML model accurately captures the site-specific adsorption energies of intermediates at the subnanometer regime. To account for the inconsistent trend in activity, we analyzed the correlation between electronic and geometric properties. Our findings reveal that the d-filling and coupling matrix of the neighboring metal atom significantly influences the intermediate adsorption on the local chemical environment compared to the d-band center. Following this analysis, we utilized ML to map the catalyst distribution in the activity volcano and identified the five best sub-nano electrocatalysts, demonstrating overpotential values lower than or comparable to the Pt(111) surface for the ORR. This study provides intuitive guidelines for the rational designing of highly efficient electrocatalysts for fuel cell applications while modifying the activity volcano plots for electrocatalysts at the subnanometer regime.

Interatomic Coulombic electron capture beyond the virtual photon approximation.

Via the interatomic Coulombic electron capture (ICEC) process, an electron can be captured by an atom or a molecule, while the binding and excess energy is transferred, via a long-range Coulomb interaction, to a neighboring atom or molecule. The transferred energy can be used to ionize or electronically excite the neighboring species. When the two species are asymptotically far apart, an analytical formula for the ICEC cross sections can be derived. The latter can then be estimated using only the energies and the photoionization cross sections of each species. In this work, we develop an analytical model that allows us to predict the ICEC cross sections when the size of the involved species is comparable to the distance between the two entities. Using abinitio R-matrix results for various systems, we show that the new model reduces the error of the asymptotic formula by two orders of magnitude on average while only using parameters that can be taken from the properties of each species.

First-principle study origin of ferromagnetism in non-magnetic perovskite BaSnO3 doped with 2p-X (X=C, N)

In this study, our goal is to analyze the origin of magnetization in non magnetic cubic structure perovskite BaSnO3 doped with 2p-X (X=C, N) using the full potential linearized augmented plane wave (FP-LAPW) method based on density functional theory (DFT). For the exchange and correlation potential we have applied the generalized gradient approximation (GGA) and the GGA plus-modified Becke-Johnson potential (mBJ-GGA). The results show that BaSnO3 doped with C then with N and finally with C and N exhibit half-metallic ferromagnetism behavior with the integer magnetic moment of 1, 2 and 3 μB per cell respectively. The origin of the ferromagnetism that occurs within these compounds is mainly caused by the p-p hybridization between 2p-impurities and its neighboring oxygen atoms. These results allowed to conclude that doped perovskite could provide a new type of materials, called half-metallic for future spintronic devices.

PerCNet: Periodic complete representation for crystal graphs

Crystal molecules are considered as graph structures in different representation methods. A reasonable crystal representation method should capture the local and global information. However, existing methods only consider the local information of crystal molecules by modeling the bond distance and bond angle of first-order neighbors of atoms, which leads to the issue that different crystals will have the same representation. To solve this many-to-one issue, we consider the global information by further considering dihedral angles. We propose a periodic complete representation of graph modeling and a calculation algorithm for infinite extended crystal materials. A theoretical proof for the representation that satisfies the periodic completeness is provided. Based on the proposed representation, we then propose a network for predicting crystal material properties, PerCNet, with a specially designed message-passing mechanism. To our best known, we are the first work that ensures the representation corresponds one-to-one with the crystal material based on graph modeling. Extensive experiments are conducted on two large-scale real-world material benchmark datasets. The PerCNet achieves the best performance among baseline methods in terms of MAE. Our code is available at https://github.com/JiaoHuang111/PerCNet.

Exact average many-body interatomic interaction model for random alloys

Understanding the physical origin of mechanisms in random alloys that lead to the formation of microstructures requires an understanding of their average behavior and, equally important, the role of local fluctuations around the average. Material properties of random alloys can be computed using direct simulations on random configurations. However, some properties are very difficult to compute, for others it is not even fully understood how to compute them using random sampling, in particular, interaction energies between multiple defects. To that end, we develop an atomistic model that does the averaging on the level of interatomic potentials. With such an average interatomic interaction model the problem of averaging via random sampling is bypassed since the problem of computing material properties on random configurations reduces to the problem of computing material properties on single crystals, the average alloy, which can be done using standard techniques.To be predictive, we develop our average model on the class of linear machine-learning interatomic potentials (MLIPs). To that end, using tools from higher-order statistics, we derive an analytic expansion of average many-body per-atom energies of linear MLIPs in terms of average tensor products of the feature vectors of the underlying machine-learning model that scales linearly with the size of an atomic neighborhood. In order to avoid forming higher-order tensors composed of products of feature vectors, we develop an implementation using equivariant tensor network (ETN) potentials, a class of linear MLIPs, that contracts the feature vectors to small-sized tensors, and then takes the average. We validate the average ETN potential by demonstrating the convergence of direct Monte Carlo simulations to the exact value for properties of the NbMoTaW medium-entropy alloy. Simulating average properties of dislocations has thus far only been possible with average embedded atom method potentials that, however, predict artificial polarized cores. Here, we show that average ETN potentials predict the compact screw dislocation core structure, in agreement with density functional theory. Hence, we anticipate that our model will become useful for understanding mechanistic origins of material properties and for developing predictive models of mechanical properties of random alloys.

OH Regulator of Amorphous CrOx on Defect-Rich Ultrafine Pd Nanowires Boosts Electrocatalytic Ethanol Oxidation.

Reasonable construction of high activity and selectivity electrocatalysts is crucial to achieve efficient ethanol oxidation reaction (EOR). However, the oxidation of ethanol tends to produce CO species that poison the active centers of the EOR electrocatalysts. Herein, a unique amorphous CrOx-protected defect-rich ultrafine Pd nanowires (CrOx-Pd NWs) is developed. On the one hand, the CrOx layer can act as a protective layer to maintain the structure of the nanowire. On the other hand, it can play the role of OH regulator to optimize the adsorption energy barrier of intermediate species in Pd nanowire, thereby enhancing the ability of the catalyst to resist CO poisoning. The CrOx-Pd NWs exhibit excellent EOR performance with 3.64 times higher mass activity and 50mV lower CO electro-oxidation potential than commercial Pd black. The results show that the CrOx layer promotes the dissociation of H2O into OHads, while the CrOx transfers electrons to neighboring Pd atoms optimizing the electronic configuration of Pd, thus selectively oxidizing ethanol to acetate and preventing the formation of toxic *CO. This work provides an effective strategy for the synthesis of nanowire materials with oxide/metal interfaces and offers new ideas for the design of catalysts that can efficiently drive EOR.

Evaluation of machine learning models for the accelerated prediction of density functional theory calculated 19F chemical shifts based on local atomic environments

The introduction of fluorine in compounds plays a crucial role in drug development as it greatly influences their final pharmacokinetic and dynamic properties. Due to the prevalence of fluorine in FDA-approved drugs in recent years, identifying the mechanisms driving their chemical transformations has become crucial in the drug discovery landscape. 19F NMR spectroscopy is a powerful analytical technique that allows for the examination of fluorine-containing compounds, offering valuable information about their structure, dynamics, and reactivity. NMR spectra can be interpreted through the leveraging of Density Functional Theory (DFT). However, the screening of compounds and discovery of feasible drug candidates is limited due to its computational cost. Here, we present a machine learning approach to accelerate the prediction of DFT-calculated 19F NMR chemical shifts. The fluorine atoms’ features in the models were derived from their local three-dimensional environments, representing their neighboring atoms within a radius of n Å away from the given fluorine atom in the compound. A comparative analysis of thirteen regression models was conducted using features extracted from 501 fluorinated compounds in our laboratory’s chemical inventory. Among the models, Gradient Boosting Regression (GBR) exhibited the highest performance, achieving a mean absolute error of 3.31 ppm with a local environment radius of 3 Å. This demonstrates a comparable accuracy to DFT calculations while reducing computational time from several hundred seconds to milliseconds. 3 Å was also found to be the most optimal radius across all models when encoding features for local atomic environments.

Characterizing the surface compositions of supported bimetallic PtSn clusters: Effects of cluster-support interactions and surface adsorbates

PtSn bimetallic clusters on TiO2(110) and highly oriented pyrolytic graphite (HOPG) surfaces have been characterized by scanning tunneling microscopy, low energy ion scattering (LEIS), X-ray photoelectron spectroscopy, and temperature programmed desorption (TPD); density functional theory (DFT) calculations have also been performed to better understand adsorption of CO and D2 on the PtSn surfaces. On TiO2 at coverages of 2 ML of Pt and 2 ML of Sn, exclusively bimetallic clusters are formed for both orders of deposition because clusters of the first metal completely cover the surface such that all atoms of the second metal are incorporated into the existing clusters. In contrast, on HOPG, the high mobility and weak cluster-support interactions on HOPG result in much larger 2 ML monometallic clusters (∼30 Å high) that do not completely cover the surface, and deposition of the second metal produces larger clusters as well as smaller ones. Despite the difference in cluster morphologies for the different orders of deposition and supports, the LEIS experiments demonstrate that in all cases, the PtSn clusters are rich in Sn at the surface, as expected based on the lower surface free energy for Sn compared to Pt. Furthermore, the +0.2 eV shift in the Sn(3d5/2) binding energy observed on all surfaces in the presence of Pt is consistent with PtSn alloy formation. Deposition of 2 ML of Sn on TiO2 produces two-dimensional clusters with oxidation of Sn and reduction of titania at the cluster-support interface, but addition of Pt to the Sn clusters causes Sn to diffuse away from this interface, leaving Sn in the metallic state. TPD experiments on 2 ML Pt/TiO2 with increasing coverages of Sn show that the number of adsorption sites for D2 sharply decreases to nearly zero at 0.5 ML, while CO adsorption decreases to zero only at much higher Sn coverages of 2 ML. DFT studies for Sn modified Pt surfaces and bulk structures demonstrate that for CO adsorption at low Sn coverages (≤0.25 ML), the strong Pt-CO interactions induce diffusion of Pt to the cluster surface and the formation of a bulk Pt3Sn alloy, whereas D2 adsorption does not lead to interactions with the Pt surface that are strong enough to induce alloy formation. A single Sn adatom prevents D2 adsorption on four neighboring Pt atoms via site-blocking and the donation of electron density to Pt.

Carbothermal Reduction-Assisted Synthesis of a Carbon-Supported Highly Dispersed PtSn Nanoalloy for the Oxygen Reduction Reaction.

Exploring high-performance and low-platinum-based electrocatalysts to accelerate the oxygen reduction reaction (ORR) at the air cathode of zinc-air batteries remains crucial. Herein, by combining electroless deposition and carbothermal reduction, a nitrogen-doped carbon-supported highly dispersed PtSn alloy nanocatalyst (PtSn/NC) was prepared for a high-efficiency ORR process. Electrochemical measurements show that PtSn/NC exhibits excellent electrocatalytic ORR activity with a half-wave potential of 0.850 V versus reversible hydrogen electrode (RHE), which is higher than that of commercial Pt/C (0.815 V). The PtSn/NC-based (20 μgPt cm-2) rechargeable Zn-air battery exhibited astonishing performance with a maximum power density of up to 150.1 mW cm-2, as well as excellent rate performance and charge/discharge stability. Physical characterization reveals that carbothermal reduction could compel the transformation of Sn oxide into metallic Sn, which then alloys with the deposited Pt atoms to form the PtSn nanoalloy, in which electrons are transferred from Sn atoms to neighboring Pt atoms, thereby improving the ability of Pt-based active sites to catalyze the ORR process in PtSn/NC by optimizing the unoccupied d-band of Pt atoms. This work provides a reliable and innovative route for the rational design of highly dispersed Pt-based alloy ORR electrocatalysts.

The dominant roles of “electron-density-mechanism” and “chemical-bonding-mechanism” for hydrogen in molybdenum and lithium

Using first-principles calculations, we have systematically studied structures and thermodynamic stability of interstitial H as well as the H-vacancy interaction in molybdenum (Mo) and lithium (Li). Single H atom prefers to occupy tetrahedral interstitial position (TIP) and octahedral interstitial position (OIP) in Mo and Li, respectively, and the solution energies are 0.87 eV and −0.66 eV, respectively. In Mo, mono-vacancy can capture as many as seven H atoms and each H atom prefers to bind onto an isosurface of valence electron density. However, H atoms detach from vacancy to occupy the OIPs outside vacancy in Li. Based on these results, we reveal that the electron-density-mechanism (EDM) and chemical-bonding-mechanism (CBM) cause different properties of H in Mo and Li, respectively. In Mo, since the valence electron density everywhere in interstitial lattice is much high, H atom has to search a place where the valence electron density must be suitable. Accordingly, vacancy can provide an optimal valence electron density region for H dissolution, and the optimal valence electron density is 0.10 electron/Å3 at vacancy. In Li, H atom exhibits the negative solution energy in the interstitial lattice, which promotes H atom to form ionic bond with neighboring Li atom. H atoms do not combine inside vacancy but stay at the OIPs outside vacancy to form ionic bonds with neighboring Li atoms. We believe that the EDM and CBM can be generalized to other transition metals and other alkali metals, respectively.

Water-Driven Surface Lattice Oxygen Activation in MnO2 for Promoted Low-Temperature NH3-SCR.

Water is ubiquitous in various heterogeneous catalytic reactions, where it can be easily adsorbed, chemically dissociated, and diffused on catalyst surfaces, inevitably influencing the catalytic process. However, the specific role of water in these reactions remains unclear. In this study, we innovatively propose that H2O-driven surface lattice oxygen activation in γ-MnO2 significantly enhances low-temperature NH3-SCR. The proton from water dissociation activates the surface lattice oxygen in γ-MnO2, giving rise to a doubling of catalytic activity (achieving 90% NO conversion at 100 °C) and remarkable stability. Comprehensive in situ characterizations and calculations reveal that spontaneous proton diffusion to the surface lattice oxygen reduces the orbital overlap between the protonated oxygen atom and its neighboring Mn atom. Consequently, the Mn-O bond is weakened and the surface lattice oxygen is effectively activated to provide excess oxygen vacancies available for converting O2 into O2-. Therefore, the redox property of Mn-H is improved, leading to enhanced NH3 oxidation-dehydrogenation and NO oxidation processes, which are crucial for low-temperature NH3-SCR. This work provides a deeper understanding and fresh perspectives on the water promotion mechanism in low-temperature NOx elimination.

Determination of the discreteness correction in dislocation equations for sphalerite crystals

Abstract The discreteness correction for simple lattices has been determined, however, there is still no detailed and rigorous derivation of the discreteness correction for compound lattices due to the relatively complex structure (simple lattices have only one set per slip system, while complex lattices have at least two sets). A lattice dynamics model which takes into account the energy increments caused by changes in bond length and bond angle is constructed for sphalerite crystals and the relationships between discreteness parameters and elastic constants are determined for the slip system. Compared to glide set, the discreteness correction for shuffle set is much larger due to it is mainly contributed by the interactions between nearest neighbor atoms. Based on the results obtained from the model, the dislocations in ZnS crystal are investigated. The theoretical prediction result of Peierls stress for shuffle dislocation closely matches the experimental critical shear stress. It is inferred that the initial plastic deformation of ZnS is closely related to the movement of shuffle dislocations.

Synergistic effects of vacancy and doping at Ge sublattices on the electronic and magnetic properties of new Janus Ge2PAs monolayer

Recently, 2D materials based on IV-V group have attracted great attention because of their interesting physical properties. In this work, Janus Ge2PAs monolayer with good stability is predicted using first-principles calculations. Our simulations assert its non-magnetic semiconductor nature with an indirect band gap of 1.11(1.75)eV obtained by PBE(HSE06)-based calculations. Further, vacancy defects and doping with transition metals (TMs = Cr and Mn) are explored in order to induce novel physical aspects. It is found significant magnetization of Janus Ge2PAs monolayer induced by Ge vacancies with total magnetic moments between 1.58 and 1.97 μB, where magnetism is produced mainly by atomic species around the defect sites. Herein, single Ge vacancy metalizes the monolayer, while the half-metallicity is obtained by creating a pair Ge vacancies. Regardless doping site, feature-rich diluted magnetic semiconductor and half-metallic behaviors are also obtained in Janus Ge2PAs monolayer by doping with Cr and Mn atom, respectively. In these case, TM impurities originate mainly the system magnetism with total magnetic moments between 2.00 and 3.03 μB. In addition, the coexistence of Ge vacancy and doping is also investigated. The calculated spin density shows an antiparallel spin orientation of Cr dopant and its neighboring atoms, meanwhile a parallel spin orientation is observed for Mn impurity and its neighboring atoms. Consequently, small total magnetic moment of 0.53 μB is obtained by the synergistic effects of doping with Cr and Ge vacancy, meanwhile the coexistence of Mn dopant and Ge vacancy generates a larger value of 4.88/4.89 μB. In all cases, the origin of electronic and magnetic properties is unraveled. Our results introduce new prospective spintronic 2D materials made from a new Janus Ge2PAs monolayer.

Ferrate (VI) oxidation of sulfamethoxazole enhanced by magnetized sludge-based biochar: Active sites regulation and degradation mechanism analysis

Developing non radical systems for antibiotic degradation is crucial for addressing the inefficiency of conventional radical systems. In this study, novel magnetic-modified sludge biochar (MASBC) was synthesized to significantly enhance the oxidative degradation of sulfamethoxazole (SMX) by ferrate (Fe (VI)). In the Fe (VI)/MASBC system, 90.46% of SMX at a concentration of 10 μM and 49.34% of the total organic carbon (TOC) could be removed under optimal conditions of 100 μM of Fe (VI) and 0.40 g/L of MASBC within 10 min. Furthermore, the Fe (VI)/MASBC system was demonstrated with broad-spectrum removal capability towards sulfonamides in single or mixture. Quenching experiments, EPR analyses, and electrochemical experiments revealed that direct electron transfer (DET) and •O2− were mainly responsible for the removal of SMX, with functional groups (e.g., -OH, C=O) and Fe-O (redox of Fe (III)/Fe (II)) acting as the active sites, while the probe experiments showed that Fe (IV)/Fe (V) made a minor contribution to the degradation of SMX. Benefiting from the DET, the Fe (VI)/MASBC system exhibited a wide pH adaptation range (e.g., from 5.0 to 10.0) and strong anti-interference ability. The N atoms and their neighboring atoms in SMX were the prior degradation sites, with the cleavage of bond and ring opening. The degradation products showed low or non-toxicity according to ECOSAR program assessment. The removal of SMX remained within a reasonable range of 71.33%–90.46% over five consecutive cycles. Also, the Fe (VI)/MASBC system was demonstrated to be effectively applied for successful SMX removal in various water matrices, including ultrapure water, tap water, lake water, Yangtze River water, and wastewater. Therefore, this study offered new insights into the mechanism of Fe (VI) oxidation and would contribute to the efficient treatment of organic pollutants.