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Abstract Magnetic supported ionic liquids are a unique subclass of ionic liquids that possess the ability to respond to external magnetic fields, combining the advantageous properties of traditional ILs with this magnetic responsiveness. A novel magnetic ionic nanocatalyst of Fe3O4@SiO2@CPTMS-DTPA was prepared by anchoring an ionic liquid, CPTMS-DTPA, onto the surface of silica-modified Fe3O4. The morphology, chemical structure and magnetic property of the magnetic ionic nanocatalyst structure was characterized using scanning electron microscopy, X-ray powder diffraction, Fourier transformation infrared spectroscopy, vibrating sample magnetometer, and thermogravimetric analysis. The results confirmed the successful attachment of the ionic liquid to the magnetic substrate. Subsequently, the magnetic nanocatalyst was employed for the green synthesis of α-aminophosphonate derivatives. The synthesis was achieved via a one-pot, three-component reaction involving various aldehydes, amines, and different trialkyl(aryl) phosphite derivatives. The reactions were conducted under ultrasound conditions for a duration of 10–25 min, resulting in good to excellent product yields (64–97%). Its recyclability was tested for up to five cycles using magnetic separation which makes it a highly efficient method for quickly separating catalysts from the reaction medium without compromising catalytic activity.

Abstract Twisted moiré van der Waals heterostructures hold promise to provide a robust quantum simulation platform for strongly correlated materials and realize elusive states of matter such as topological states in the laboratory. We demonstrated that the moiré bands of twisted transition metal dichalcogenide (TMD) hetero-nanoribbons exhibit non-trivial topological order due to the tendency of valence and conduction band states in K valleys to form giant band gaps when spin-orbit coupling (SOC) is taken into account. Among the features of twisted WS $$_2$$ 2 /MoS $$_2$$ 2 and WSe $$_2$$ 2 /MoSe $$_2$$ 2 , we found that the heavy fermions associated with the topological flat bands and the presence of strongly correlated states, enhance anomalous Hall conductivity (AHC) away from the magic angle. By band analysis, we showed that the topmost conduction bands from the ± K-valleys are perfectly flat and carry a spin/valley Chern number. Moreover, we showed that the non-linear anomalous Hall effect in moiré TMD hetero-nanoribbons can be used to manipulate terahertz (THz) radiation. Our findings establish twisted heterostructures of group-VI TMD nanoribbons as a tunable platform for engineering topological valley quantum phases and THz non-linear Hall conductivity.

Abstract This study introduces a simple method for preparing a new generation of MnO2 nanomaterials (MNMs) using tannic acid as a template. Two shapes of MnO2 NMs, flower-like M1-MnO2 and near-spherical M2-MnO2, were prepared and compared as dual-active nanozymes and contrast agents in magnetic resonance imaging (MRI). Various parameters, including the crystallinity, morphology, magnetic saturation (Ms), surface functionality, surface area, and porosity of the MNMs were investigated. Flower-like M1-MnO2 NMs were biocompatible and exhibited pH-sensitive oxidase and peroxidase mimetic activity, more potent than near-spherical M2-MnO2. Furthermore, the signal intensity and r1 relaxivity strongly depended on the crystallinity, morphology, pore size, and specific surface area of the synthesized MNMs. Our findings suggest that flower-like M1-MnO2 NM with acceptable dual-enzyme mimetic (oxidase-like and peroxidase-like) and T1 MRI contrast activities could be employed as a promising theranostic system for future purposes.

Abstract Athletes who have undergone anterior cruciate ligament reconstruction (ACLR) often exhibit persistently impaired kinematics and strength. Core stability training appears to be effective for reducing high-risk landing mechanics and preventing primary anterior cruciate ligament (ACL) injuries; however, there have been few attempts to examine their effects in athletes who have undergone ACLR. This study aimed to investigate the effect of eight weeks of simple core stability training on core endurance, hip strength, and knee kinematics in ACLR athletes. Twenty-six male athletes (20–30 years old) with a history of ACL surgery with hamstring tendon autograft were randomly divided into training (n = 13) and control groups (n = 13). The training group performed core stability exercises for eight weeks before starting their team training; the control group did not receive any intervention. Both groups continued their regular team schedule. The core endurance, hip muscle strength, and knee kinematics were assessed by the McGill test, a hand-held dynamometer, and video-taping, respectively. Analysis of covariance test was used for data analysis. The training group showed a significant increase in core endurance, hip abductor and external rotator strength, knee flexion angle, and a significant decrease in the knee valgus angle during single-leg landing in post-training tests compared to their baseline tests (P

Abstract Due to the increasing prevalence of chronic kidney disease and its high mortality rate, study of risk factors affecting the progression of the disease is of great importance. Here in this work, we aim to develop a framework for using machine learning methods to identify factors affecting kidney function. To this end classification methods are trained to predict the serum creatinine level based on numerical values of other blood test parameters in one of the three classes representing different ranges of the variable values. Models are trained using the data from blood test results of healthy and patient subjects including 46 different blood test parameters. The best developed models are random forest and LightGBM. Interpretation of the resulting model reveals a direct relationship between vitamin D and blood creatinine level. The detected analogy between these two parameters is reliable, regarding the relatively high predictive accuracy of the random forest model reaching the AUC of 0.90 and the accuracy of 0.74. Moreover, in this paper we develop a Bayesian network to infer the direct relationships between blood test parameters which have consistent results with the classification models. The proposed framework uses an inclusive set of advanced imputation methods to deal with the main challenge of working with electronic health data, missing values. Hence it can be applied to similar clinical studies to investigate and discover the relationships between the factors under study.

Abstract B-cell lymphoma 6 (BCL6) regulates various genes and is reported to be overexpressed in lymphomas and other malignancies. Thus, BCL6 inhibition or its tagging for degradation would be an amenable therapeutic approach. A library of 2500 approved drugs was employed to find BCL6 inhibitory molecules via virtual screening. Moreover, the 3D core structure of 170 BCL6 inhibitors was used to build a 3D QSAR model and predict the biological activity. The SNP database was analyzed to study the impact on the destabilization of BCL6/drug interactions. Structural similarity search and molecular docking analyses were used to assess the interaction between possible off-targets and BCL6 inhibitors. The tendency of drugs for passive membrane permeability was also analyzed. Lifitegrast (DB11611) had favorable binding properties and biological activity compared to the BI-3802. Missense SNPs were located at the essential interaction sites of the BCL6. Structural similarity search resulted in five BTB-domain containing off-target proteins. BI-3802 and Lifitegrast had similar chemical behavior and binding properties against off-target candidates. More interestingly, the binding affinity of BI-3802 (against off-targets) was higher than Lifitegrast. Energetically, Lifitegrast was less favorable for passive membrane permeability. The interaction between BCL6 and BI-3802 is more prone to SNP-derived variations. On the other hand, higher nonspecific binding of BI-3802 to off-target proteins could bring about higher undesirable properties. It should also be noted that energetically less desirable passive membrane translocation of Lifitegrast would demand drug delivery vehicles. However, further empirical evaluation of Lifitegrast would unveil its true potential.

Abstract Strain engineering is a powerful technique for tuning electronic properties and valley degree of freedom in honeycomb structure of two-dimensional crystals. Carriers in + k and − k (opposite Berry curvature) in transition metal dichalcogenide (TMD) with broken inversion symmetry act as effective magnetic fields, where this polarized valleys are suitable for encoding information. In this work, we study the strained TMD nanoribbons by Slater-Koster tight-binding model, which acquires electronic bands in whole Brillouin zone. From this, we derive a generic profile of strain effect on the electronic band structure of TMD nanoribbons, which shows indirect band gap, and also exhibits a phase transition from semiconductor to metallic by applying uniaxial X-tensile and Y-arc type of strain. Midgap states in strained TMD nanoribbons are determined by calculation of localized density of electron states. Moreover, our findings of anomalous valley Hall conductivity reveal that the creation of pseudogauge fields using strained TMD nanoribbons affect the Dirac electrons, which generate the new quantized Landau level. Furthermore, we demonstrate in strained TMD nanoribbons that strain field can effectively tune both the magnitude and sign of valley Hall conductivity. Our work elucidates the valley Hall transport in strained TMDs due to pseudo-electric and pseudo-magnetic filed will be applicable as information carries for future electronics and valleytronics.

Abstract Magnetism in atomically thin functional materials can be an important phenomenon for exploring two-dimensional magneto-optics. Magneto-optical experimental data have revealed significant Kerr signals in insulator thin films. Here, the magneto-optical Kerr effect of oxygen functionalized and doped hexagonal boron nitride (hBN) has been investigated by performing first-principles calculations. We calculated Kerr angle and Kerr ellipticity for functionalized hBN as an attention-drawn material. Moreover, increasing of oxygen doping percentage leads to the introduction of surface plasmon to hBN. Our findings show that the functionalized hBN can tolerate high-temperature conditions, keeping oxygen atoms bridge-bonded. These giant opto/magnetic responses of insulating 2D materials provide a platform for the potential designing of magneto-optical devices.

Abstract Interfacial encoded properties of polymer adlayers adsorbed on the graphene (GE) and silicon dioxide (SiO2) have been constituted a scaffold for the creation of new materials. The holistic understanding of nanoscale intermolecular interaction of 1D/2D polymer assemblies on substrate is the key to bottom-up design of molecular devices. We develop an integrated multidisciplinary approach based on electronic structure computation [density functional theory (DFT)] and big data mining [machine learning (ML)] in parallel with neural network (NN) and statistical analysis (SA) to design hybrid polymers from assembly on substrate. Here we demonstrate that interfacial pressure and structural deformation of polymer network adsorbed on GE and SiO2 offer unique directions for the fabrication of 1D/2D polymers using only a small number of simple molecular building blocks. Our findings serve as the platform for designing a wide range of typical inorganic heterostructures, involving noncovalent intermolecular interaction observed in many nanoscale electronic devices.

Estimating global and multi-level Thermosphere Neutral Density (TND) is important for studying coupling processes within the upper atmosphere, and for applications like orbit prediction. Models are applied for predicting TND changes, however, their performance can be improved by accounting for the simplicity of model structure and the sampling limitations of model inputs. In this study, a simultaneous Calibration and Data Assimilation (C/DA) algorithm is applied to integrate freely available CHAMP, GRACE, and Swarm derived TND measurements into the NRLMSISE-00 model. The improved model, called 'C/DA-NRLMSISE-00', and its outputs fit to these measured TNDs, are used to produce global TND fields at arbitrary altitudes (with the same vertical coverage as the NRLMSISE-00). Seven periods, between 2003-2020 that are associated with relatively high geomagnetic activity selected to investigate these fields, within which available models represent difficulties to provide reasonable TND estimates. Independent validations are performed with along-track TNDs that were not used within the C/DA framework, as well as with the outputs of other models such as the Jacchia-Bowman 2008 and the High Accuracy Satellite Drag Model. The numerical results indicate an average 52%, 50%, 56%, 25%, 47%, 54%, and 63% improvement in the Root Mean Squared Errors of the short term TND forecasts of C/DA-NRLMSISE00 compared to the along-track TND estimates of GRACE (2003, altitude 490 km), GRACE (2004, altitude 486 km), CHAMP (2008, altitude 343 km), GOCE (2010, altitude 270 km), Swarm-B (2015, altitude 520 km), Swarm-B (2017, altitude 514 km), and Swarm-B (2020, altitude 512 km), respectively., (© 2023. The Author(s).)

The effect of triad application of the phytohormone 24-epibrassinolide (EBL), the polyamine spermine (Spm), and the element silicon (Si) has not yet been considered on plant growth and behavior in water-stressed conditions. We aimed to evaluate the impact of single/dual/triad application of 24-epibrassinolide (EBL), spermine (Spm), and silicon (Si) on the growth, photosynthetic metabolites, and antioxidant enzymes in the maize plant exposed to water stress. This study was conducted as a potential drought resistance system and plants' maintenance against oxidative damage. In this regard, one maize hybrid (Paya) was grown under well-watered and water-deficit conditions (interrupted irrigation at the flowering and the filling seed stages) with and without foliar spraying of EBL, Spm, and/or Si. Drought conditions remarkably reduced growth, productivity, water-related content (RWC), and chlorophyll content. However, the dual and triad applications of EBL (0.1 mg L -1 ), Spm (25 mg L -1 ), and Si (7 mg L -1 ) significantly improved the above parameters. Water stress considerably augmented the levels of H 2 O 2 and MDA. Their content in stress-subjected plants was significantly reduced by triad application. In water-stressed circumstances and after foliar treatments, the activities of superoxide dismutase, catalase, and peroxidase as well as the amounts of total soluble proteins, phenolic compounds, proline, and glycine betaine all improved. Overall, triad application increased the plant's drought resistance and diminished ROS accumulation by raising the scavenging via the enhanced activity of the antioxidant enzymes., (© 2022. The Author(s).)

Global estimation of thermospheric neutral density (TND) on various altitudes is important for geodetic and space weather applications. This is typically provided by models, however, the quality of these models is limited due to their imperfect structure and the sensitivity of their parameters to the calibration period. Here, we present an ensemble Kalman filter (EnKF)-based calibration and data assimilation (C/DA) technique that updates the model's states and simultaneously calibrates its key parameters. Its application is demonstrated using the TND estimates from on-board accelerometer measurements, e.g., those of the Gravity Recovery and Climate Experiment (GRACE) mission (at [Formula: see text] km altitude), as observation, and the frequently used empirical model NRLMSISE-00. The C/DA is applied here to re-calibrate the model parameters including those controlling the influence of solar radiation and geomagnetic activity as well as those related to the calculation of exospheric temperature. The resulting model, called here 'C/DA-NRLMSISE-00', is then used to now-cast TNDs and individual neutral mass compositions for 3 h, where the model with calibrated parameters is run again during the assimilation period. C/DA-NRLMSISE-00 is also used to forecast the next 21 h, where no new observations are introduced. These forecasts are unique because they are available globally and on various altitudes (300-600 km). To introduce the impact of the thermosphere on estimating ionospheric parameters, the coupled physics-based model TIE-GCM is run by replacing the O2, O1, He and neutral temperature estimates of the C/DA-NRLMSISE-00. Then, the non-assimilated outputs of electron density (Ne) and total electron content (TEC) are validated against independent measurements. Assessing the forecasts of TNDs with those along the Swarm-A ([Formula: see text] km), -B ([Formula: see text] km), and -C ([Formula: see text] km) orbits shows that the root-mean-square error (RMSE) is considerably reduced by 51, 57 and 54%, respectively. We find improvement of 30.92% for forecasting Ne and 26.48% for TEC compared to the radio occulation and global ionosphere maps (GIM), respectively. The presented C/DA approach is recommended for the short-term global multi-level thermosphere and enhanced ionosphere forecasting applications., (© 2022. The Author(s).)