A hybrid machine learning algorithm for studying magnetized nanofluid flow containing gyrotactic microorganisms via a vertically inclined stretching surface.

The novelty of the present work is to acquire continuous functions as solutions rather than the discrete ones that traditional numerical methods generally produce and to minimize simulation times and higher computation costs that are the fundamental barriers to employing any numerical method. In this study, a novel hybrid finite element-based machine learning algorithm utilizing the Levenberg-Marquardt scheme with backpropagation in a neural network (LMBNN) is presented to analyze the nanofluid flow in the presence of magnetohydrodynamics and gyrotactic microorganisms through a vertically inclined stretching surface in a porous medium. Finite Element Method is used to generate the minimum reference dataset for LMBNN by varying six flow parameters in the form of six scenarios. Surface plots are utilized to understand how these scenarios affect velocity, temperature, concentration of nanoparticles, and density of motile microorganisms. Regression analysis, error histogram analysis, and fitness curves based on mean square error all support the LMBNN's effectiveness and dependability. Results reveal that temperature increases with the rise in Brownian motion and thermophoresis parameter, whereas the reverse trend has been noticed for Prandtl number. The motile microorganism density number decreases with the rise in Prandtl numbers but improves with the porosity parameter.

GA-coupled ANN model for predicting porosity in alginate gel scaffolds.

Alginate gel scaffolds are biocompatible and biodegradable materials that have been used in a variety of tissue engineering applications. The porosity of alginate gel scaffolds is an important property that affects their performance. However, it is difficult to predict the porosity of alginate gel scaffolds accurately. In this study, a GA-coupled ANN model was developed to predict the porosity of alginate gel scaffolds. The model was trained on a dataset of 107 scaffolds with known porosities. The model was able to achieve a mean absolute error of 0.13, which suggests that it is able to accurately predict the porosity of alginate gel scaffolds. The alginate scaffold was fabricated by a microfluidic technique using a syringe pump and a flow device. The crosslinker solution was poured into the Petri dish to crosslink the polymer to the gel structure. The Archimedes method was used to determine the scaffold's apparent porosity. The artificial neural network has been used to model the porosity of the gel scaffold using the input parameters such as alginate-pluronic viscosity, surface tension, and contact angle etc. The maximum porosity was modelled to be 96.4 % using GA whereas the experimental value for the same was measured to be 92.8 ± 2 %. A 3.7% variation in the porosity was found from modelled value. To the best of our knowledge, this study is the first to develop an integrated ANN-coupled GA model to predict the maximum porosity of the gel scaffold. The result indicates that artificial intelligence has great potential for optimizing the parameters to fabricate the gel scaffold that can be used for tissue engineering applications.

Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network.

In this research, we demonstrate a Deep Convolutional Neural Network-based classification model for the detection of monkeypox. Monkeypox can be difficult to diagnose clinically in its early stages since it resembles both chickenpox and measles in symptoms. The early diagnosis of monkeypox helps doctors cure it more quickly. Therefore, pre-trained models are frequently used in the diagnosis of monkeypox, because the manual analysis of a large number of images is labor-intensive and prone to inaccuracy. Therefore, finding the monkeypox virus requires an automated process. The large layer count of convolutional neural network (CNN) architectures enables them to successfully conceptualize the features on their own, thereby contributing to better performance in image classification. The scientific community has recently articulated significant attention in employing artificial intelligence (AI) to diagnose monkeypox from digital skin images due primarily to AI's success in COVID-19 identification. The VGG16, VGG19, ResNet50, ResNet101, DenseNet201, and AlexNet models were used in our proposed method to classify patients with monkeypox symptoms with other diseases of a similar kind (chickenpox, measles, and normal). The majority of images in our research are collected from publicly available datasets. This study suggests an adaptive k-means clustering image segmentation technique that delivers precise segmentation results with straightforward operation. Our preliminary computational findings reveal that the proposed model could accurately detect patients with monkeypox. The best overall accuracy achieved by ResNet101 is 94.25%, with an AUC of 98.59%. Additionally, we describe the categorization of our model utilizing feature extraction using Local Interpretable Model-Agnostic Explanations (LIME), which provides a more in-depth understanding of particular properties that distinguish the monkeypox virus.

Competing Magnetic Interactions and Field-Induced Metamagnetic Transition in Highly Crystalline Phase-Tunable Iron Oxide Nanorods.

The inherent existence of multi phases in iron oxide nanostructures highlights the significance of them being investigated deliberately to understand and possibly control the phases. Here, the effects of annealing at 250 °C with a variable duration on the bulk magnetic and structural properties of high aspect ratio biphase iron oxide nanorods with ferrimagnetic Fe3O4 and antiferromagnetic α-Fe2O3 are explored. Increasing annealing time under a free flow of oxygen enhanced the α-Fe2O3 volume fraction and improved the crystallinity of the Fe3O4 phase, identified in changes in the magnetization as a function of annealing time. A critical annealing time of approximately 3 h maximized the presence of both phases, as observed via an enhancement in the magnetization and an interfacial pinning effect. This is attributed to disordered spins separating the magnetically distinct phases which tend to align with the application of a magnetic field at high temperatures. The increased antiferromagnetic phase can be distinguished due to the field-induced metamagnetic transitions observed in structures annealed for more than 3 h and was especially prominent in the 9 h annealed sample. Our controlled study in determining the changes in volume fractions with annealing time will enable precise control over phase tunability in iron oxide nanorods, allowing custom-made phase volume fractions in different applications ranging from spintronics to biomedical applications.

Predictive modelling and optimization of an airlift bioreactor for selenite removal from wastewater using artificial neural networks and particle swarm optimization.

Selenite (Se4+) is the most toxic of all the oxyanion forms of selenium. In this study, a feed forward back propagation (BP) based artificial neural network (ANN) model was developed for a fungal pelleted airlift bioreactor (ALR) system treating selenite-laden wastewater. The performance of the bioreactor, i.e., selenite removal efficiency (REselenite) (%) was predicted through two input parameters, namely, the influent selenite concentration (ICselenite) (10 mg/L - 60 mg/L) and hydraulic retention time (HRT) (24 h - 72 h). After training and testing with 96 sets of data points using the Levenberg-Marquardt algorithm, a multi-layer perceptron model (2-10-1) was established. High values of the correlation coefficient (0.96 ≤ R ≤ 0.98), along with low root mean square error (1.72 ≤ RMSE ≤ 2.81) and mean absolute percentage error (1.67 ≤ MAPE ≤ 2.67), clearly demonstrate the accuracy of the ANN model (> 96%) when compared to the experimental data. To ensure an efficient and economically feasible operation of the ALR, the process parameters were optimized using the particle swarm optimization (PSO) algorithm coupled with the neural model. The REselenite was maximized while minimizing the HRT for a preferably higher range of ICselenite. Thus, the most favourable optimum conditions were suggested as: ICselenite - 50.45 mg/L and HRT - 24 h, resulting in REselenite of 69.4%. Overall, it can be inferred that ANN models can successfully substitute knowledge-based models to predict the REselenite in an ALR, and the process parameters can be effectively optimized using PSO.

Bioprospecting of novel peroxidase from Streptomyces coelicolor strain SPR7 for carcinogenic azo dyes decolorization.

Peroxidase (POX) is a heme-containing oxidoreductase, its voluminous immuno-diagnostic and bioremediatory intuitions have incited optimization and large scale-generation from novel microbial repertoires. Azo dyes are the most detrimental classes of synthetic dyes and they are the common ecotoxic industrial pollutants in wastewater. In addition, azo dyes are refractory to degradation owing to their chemical nature, comprising of azoic linkages, amino moieties with recalcitrant traits. Moreover, they are major carcinogenic and mutagenic on humans and animals, whereby emphasizing the need for decolorization. In the present study, a novel POX from Streptomyces coelicolor strain SPR7 was investigated for the deterioration of ecotoxic dyestuffs. The initial medium component screening for POX production was achieved using, One Factor at a Time and Placket-Burman methodologies with starch, casein and temperature as essential parameters. In auxiliary, Response Surface Methodology (RSM) was recruited and followed by model validation using Back propagation algorithm (BPA). RSM-BPA composite approach prophesied that combination of starch, casein, and temperature at optimal values 2.5%, 0.035% and 35 °C respectively, has resulted in 7 folds enhancement of POX outturn (2.52 U/mL) compared to the unoptimized media (0.36 U/mL). The concentrated enzyme decolorized 75.4% and 90% of the two azo dyes with lignin (10 mM), respectively. Hence, this investigation confirms the potentiality of mangrove actinomycete derived POX for elimination of noxious azo dyes to overcome their carcinogenic, mutagenic and teratogenic effects on humans and aquatic organisms.

Artificial Neural Networks for the Prediction of Monkeypox Outbreak.

While the world is still struggling to recover from the harm caused by the widespread COVID-19 pandemic, the monkeypox virus now poses a new threat of becoming a pandemic. Although it is not as dangerous or infectious as COVID-19, new cases of the disease are nevertheless being reported daily from many countries. In this study, we have used public datasets provided by the European Centre for Disease Prevention and Control for developing a prediction model for the spread of the monkeypox outbreak to and throughout the USA, Germany, the UK, France and Canada. We have used certain effective neural network models for this purpose. The novelty of this study is that a neural network model for a time series monkeypox dataset is developed and compared with LSTM and GRU models using an adaptive moment estimation (ADAM) optimizer. The Levenberg-Marquardt (LM) learning technique is used to develop and validate a single hidden layer artificial neural network (ANN) model. Different ANN model architectures with varying numbers of hidden layer neurons were trained, and the K-fold cross-validation early stopping validation approach was employed to identify the optimum structure with the best generalization potential. In the regression analysis, our ANN model gives a good R-value of almost 99%, the LSTM model gives almost 98% and the GRU model gives almost 98%. These three model fits demonstrated that there was a good agreement between the experimental data and the forecasted values. The results of our experiments show that the ANN model performs better than the other methods on the collected monkeypox dataset in all five countries. To the best of the authors' knowledge, this is the first report that has used ANN, LSTM and GRU to predict a monkeypox outbreak in all five countries.

Artificial neural networks for prediction of COVID-19 in India by using backpropagation.

The COVID-19 pandemic has affected thousands of people around the world. In this study, we used artificial neural network (ANN) models to forecast the COVID-19 outbreak for policymakers based on 1st January to 31st October 2021 of positive cases in India. In the confirmed cases of COVID-19 in India, it's critical to use an estimating model with a high degree of accuracy to get a clear understanding of the situation. Two explicit mathematical prediction models were used in this work to anticipate the COVID-19 epidemic in India. A Boltzmann Function-based model and Beesham's prediction model are among these methods and also estimated using the advanced ANN-BP models. The COVID-19 information was partitioned into two sections: training and testing. The former was utilized for training the ANN-BP models, and the latter was used to test them. The information examination uncovers critical day-by-day affirmed case changes, yet additionally unmistakable scopes of absolute affirmed cases revealed across the time span considered. The ANN-BP model that takes into consideration the preceding 14-days outperforms the others based on the archived results. In forecasting the COVID-19 pandemic, this comparison provides the maximum incubation period, in India. Mean square error, and mean absolute percent error have been treated as the forecast model performs more accurately and gets good results. In view of the findings, the ANN-BP model that considers the past 14-days for the forecast is proposed to predict everyday affirmed cases, especially in India that have encountered the main pinnacle of the COVID-19 outbreak. This work has not just demonstrated the relevance of the ANN-BP techniques for the expectation of the COVID-19 outbreak yet additionally showed that considering the incubation time of COVID-19 in forecast models might produce more accurate assessments.

Emergent magnetism and exchange bias effect in iron oxide nanocubes with tunable phase and size.

We report a systematic investigation of the magnetic properties including the exchange bias (EB) effect in an iron oxide nanocube system with tunable phase and average size (10, 15, 24, 34, and 43 nm). X-ray diffraction and Raman spectroscopy reveal the presence of Fe3O4, FeO, andα-Fe2O3phases in the nanocubes, in which the volume fraction of each phase varies depending upon particle size. While the Fe3O4phase is dominant in all and tends to grow with increasing particle size, the FeO phase appears to coexist with the Fe3O4phase in 10, 15, and 24 nm nanocubes but disappears in 34 and 43 nm nanocubes. The nanocubes exposed to air resulted in anα-Fe2O3oxidized surface layer whose thickness scaled with particle size resulting in a shell made ofα-Fe2O3phase and a core containing Fe3O4or a mixture of both Fe3O4and FeO phases. Magnetometry indicates that the nanocubes undergo Morin (of theα-Fe2O3phase) and Verwey (of the Fe3O4phase) transitions at ∼250 K and ∼120 K, respectively. For smaller nanocubes (10, 15, and 24 nm), the EB effect is observed below 200 K, of which the 15 nm nanocubes showed the most prominent EB with optimal antiferromagnetic (AFM) FeO phase. No EB is reported for larger nanocubes (34 and 43 nm). The observed EB effect is ascribed to the strong interfacial coupling between the ferrimagnetic (FiM) Fe3O4phase and AFM FeO phase, while its absence is related to the disappearance of the FeO phase. The Fe3O4/α-Fe2O3(FiM/AFM) interfaces are found to have negligible influence on the EB. Our findings shed light on the complexity of the EB effect in mixed-phase iron oxide nanosystems and pave the way to design exchange-coupled nanomaterials with desirable magnetic properties for biomedical and spintronic applications.

Advances in magnetic field-assisted electrolyte's physicochemical properties and electrokinetic parameters: A case study on the response ability of chloramphenicol on Fe3O4@carbon spheres-based electrochemical nanosensor.

Despite the utilization of external magnetic field (MF) in promoting the intrinsic unique features of magnetic nanomaterials in many different applications has been reported, however the origin of MF-dependent electrochemical behaviors as well as the electrochemical response of analytes at the electrode in sensor applications is still not clear. In this report, the influence of MF on the electrolyte's physicochemical properties (polarization, mass transport, charge/electron transfer) and electrode's properties (conductivity, morphology, surface area, interaction, adsorption capability, electrocatalytic ability) was thoroughly investigated. Herein, the working electrode surface was modified with carbon spheres (CSs), magnetic nanoparticles (Fe3O4NPs), and their nanocomposites (Fe3O4@CSs), respectively. Then, they were directly used to enhance the electrochemical characteristics and response-ability of chloramphenicol (CAP). More interestingly, a series of various kinetic parameters related to the diffusion-controlled process of K3[Fe(CN)6]/K4[Fe(CN)6)] and the adsorption-controlled process of CAP were calculated at the bare electrode and the modified electrodes with and without the presence of MF. These parameters not only exhibit the crucial role of the modification of electrode surface with the proposed materials but also show positive impacts of the presence of external MF. Besides, the mechanism and hypothesis for the enhancements were proposed and discussed in detail, further demonstrating the development potential of using Fe3O4@CS nanocomposites with MF assistant for advanced energy, environmental, and sensor related-applications.

Spin Seebeck Effect in Iron Oxide Thin Films: Effects of Phase Transition, Phase Coexistence, And Surface Magnetism.

Understanding the effects of phase transition, phase coexistence, and surface magnetism on the longitudinal spin Seebeck effect (LSSE) in a magnetic system is essential to manipulate the spin to charge current conversion efficiency for spincaloritronic applications. We aim to elucidate these effects by performing a comprehensive study of the temperature dependence of the LSSE in biphase iron oxide (BPIO = α-Fe2O3 + Fe3O4) thin films grown on Si (100) and Al2O3 (111) substrates. A combination of a temperature-dependent anomalous Nernst effect (ANE) and electrical resistivity measurements show that the contribution of the ANE from the BPIO layer is negligible in comparison to the intrinsic LSSE in the Si/BPIO/Pt heterostructure, even at room temperature. Below the Verwey transition of the Fe3O4 phase, the total signal across BPIO/Pt is dominated by the LSSE. Noticeable changes in the intrinsic LSSE signal for both Si/BPIO/Pt and Al2O3/BPIO/Pt heterostructures around the Verwey transition of the Fe3O4 phase and the antiferromagnetic (AFM) Morin transition of the α-Fe2O3 phase are observed. The LSSE signal for Si/BPIO/Pt is found to be almost 2 times greater than that for Al2O3/BPIO/Pt; however, an opposite trend is observed for the saturation magnetization. Magnetic force microscopy reveals the higher density of surface magnetic moments of the Si/BPIO film in comparison to the Al2O3/BPIO film, which underscores the dominant role of interfacial magnetism on the LSSE signal and thereby explains the larger LSSE for Si/BPIO/Pt.

Iron Oxide Nanorings and Nanotubes for Magnetic Hyperthermia: The Problem of Intraparticle Interactions.

Magnetic interactions can play an important role in the heating efficiency of magnetic nanoparticles. Although most of the time interparticle magnetic interactions are a dominant source, in specific cases such as multigranular nanostructures intraparticle interactions are also relevant and their effect is significant. In this work, we have prepared two different multigranular magnetic nanostructures of iron oxide, nanorings (NRs) and nanotubes (NTs), with a similar thickness but different lengths (55 nm for NRs and 470 nm for NTs). In this way, we find that the NTs present stronger intraparticle interactions than the NRs. Magnetometry and transverse susceptibility measurements show that the NTs possess a higher effective anisotropy and saturation magnetization. Despite this, the AC hysteresis loops obtained for the NRs (0-400 Oe, 300 kHz) are more squared, therefore giving rise to a higher heating efficiency (maximum specific absorption rate, SARmax = 110 W/g for the NRs and 80 W/g for the NTs at 400 Oe and 300 kHz). These results indicate that the weaker intraparticle interactions in the case of the NRs are in favor of magnetic hyperthermia in comparison with the NTs.

Hybrid magnetic nanoparticles as efficient nanoheaters in biomedical applications.

Heating at the nanoscale is the basis of several biomedical applications, including magnetic hyperthermia therapies and heat-triggered drug delivery. The combination of multiple inorganic materials in hybrid magnetic nanoparticles provides versatile platforms to achieve an efficient heat delivery upon different external stimuli or to get an optical feedback during the process. However, the successful design and application of these nanomaterials usually require intricate synthesis routes and their magnetic response is still not fully understood. In this review we give an overview of the novel systems reported in the last few years, which have been mostly obtained by organic phase-based synthesis and epitaxial growth processes. Since the heating efficiency of hybrid magnetic nanoparticles often relies on the exchange-interaction between their components, we discuss various interface-phenomena that are responsible for their magnetic properties. Finally, followed by a brief comment on future directions in the field, we outline recent advances on multifunctional nanoparticles that can boost the heating power with light and combine heating and temperature sensing in a single nanomaterial.

Isolation of Cancer-Derived Exosomes Using a Variety of Magnetic Nanostructures: From Fe3O4 Nanoparticles to Ni Nanowires.

Isolating and analyzing tumor-derived exosomes (TEX) can provide important information about the state of a tumor, facilitating early diagnosis and prognosis. Since current isolation methods are mostly laborious and expensive, we propose herein a fast and cost-effective method based on a magnetic nanoplatform to isolate TEX. In this work, we have tested our method using three magnetic nanostructures: (i) Ni magnetic nanowires (MNWs) (1500 × 40 nm), (ii) Fe3O4 nanorods (NRs) (41 × 7 nm), and (iii) Fe3O4 cube-octahedral magnetosomes (MGs) (45 nm) obtained from magnetotactic bacteria. The magnetic response of these nanostructures has been characterized, and we have followed their internalization inside canine osteosarcoma OSCA-8 cells. An overall depiction has been obtained using a combination of Fluorescence and Scanning Electron Microscopies. In addition, Transmission Electron Microscopy images have shown that the nanostructures, with different signs of degradation, ended up being incorporated in endosomal compartments inside the cells. Small intra-endosomal vesicles that could be precursors for TEX have also been identified. Finally, TEX have been isolated using our magnetic isolation method and analyzed with a Nanoparticle tracking analyzer (NanoSight). We observed that the amount and purity of TEX isolated magnetically with MNWs was higher than with NRs and MGs, and they were close to the results obtained using conventional non-magnetic isolation methods.

Shell-mediated control of surface chemistry of highly stoichiometric magnetite nanoparticles.

Magnetite (Fe3O4) nanoparticles are one of the most studied nanomaterials for different nanotechnological and biomedical applications. However, Fe3O4 nanomaterials gradually oxidize to maghemite (γ-Fe2O3) under conventional environmental conditions leading to changes in their functional properties that determine their performance in many applications. Here we propose a novel strategy to control the surface chemistry of monodisperse 12 nm magnetite nanoparticles by means of a 3 nm-thick Zn-ferrite epitaxial coating in core/shell nanostructures. We have carried out a combined Mössbauer spectroscopy, dc magnetometry, X-ray photoelectron spectroscopy and spatially resolved electron energy loss spectroscopy study on iron oxide and Fe3O4/Zn0.6Fe2.4O4 core/shell nanoparticles aged under ambient conditions for 6 months. Our results reveal that while the aged iron oxide nanoparticles consist of a mixture of γ-Fe2O3 and Fe3O4, the Zn-ferrite-coating preserves a highly stoichiometric Fe3O4 core. Therefore, the aged core/shell nanoparticles present a sharp Verwey transition, an increased saturation magnetization and the possibility of tuning the effective anisotropy through exchange-coupling at the core/shell interface. The inhibition of the oxidation of the Fe3O4 cores can be accounted for in terms of the chemical nature of the shell layer and an epitaxial crystal symmetry matching between the core and the shell.

Valence tautomerism in a cobalt-verdazyl coordination compound.

Coordination of 1-isopropyl-3,5-dipyridyl-6-oxoverdazyl to cobalt results in a dication best described in the solid state as a high spin cobalt(ii) ion coordinated to two radical ligands with an S = 3/2 ground state. On dissolution in acetonitrile, the cobalt(ii) form equilibrates with a cobalt(iii) valence tautomer with an S = 1/2 ground state.

Unlocking the Potential of Magnetotactic Bacteria as Magnetic Hyperthermia Agents.

Magnetotactic bacteria are aquatic microorganisms that internally biomineralize chains of magnetic nanoparticles (called magnetosomes) and use them as a compass. Here it is shown that magnetotactic bacteria of the strain Magnetospirillum gryphiswaldense present high potential as magnetic hyperthermia agents for cancer treatment. Their heating efficiency or specific absorption rate is determined using both calorimetric and AC magnetometry methods at different magnetic field amplitudes and frequencies. In addition, the effect of the alignment of the bacteria in the direction of the field during the hyperthermia experiments is also investigated. The experimental results demonstrate that the biological structure of the magnetosome chain of magnetotactic bacteria is perfect to enhance the hyperthermia efficiency. Furthermore, fluorescence and electron microscopy images show that these bacteria can be internalized by human lung carcinoma cells A549, and cytotoxicity studies reveal that they do not affect the viability or growth of the cancer cells. A preliminary in vitro hyperthermia study, working on clinical conditions, reveals that cancer cell proliferation is strongly affected by the hyperthermia treatment, making these bacteria promising candidates for biomedical applications.

Correction: An electron transfer driven magnetic switch: ferromagnetic exchange and spin delocalization in iron verdazyl complexes.

Correction for 'An electron transfer driven magnetic switch: ferromagnetic exchange and spin delocalization in iron verdazyl complexes' by David J. R. Brook et al., Dalton Trans., 2018, 47, 6351-6360.

An electron transfer driven magnetic switch: ferromagnetic exchange and spin delocalization in iron verdazyl complexes.

The verdazyl 'pincer' ligand, 1-isopropyl-3,5-dipyridyl-6-oxoverdazyl (dipyvd), coordinates iron to form a series of pseudooctahedral coordination compounds [Fe(dipyvd)2]n+ (n = 0-3). In the case where n = 2, the molecular geometry and physical and spectral properties are consistent with a low spin (S = 0) iron(ii) ion coordinated by two ferromagnetically coupled radical ligands. Upon one electron reduction, the room temperature effective magnetic moment of the complex jumps from µeff = 2.64 to µeff = 5.86 as a result of spin crossover of the iron atom combined with very strong ferromagnetic coupling of the remaining ligand centered unpaired electron with the metal center. The sign of the exchange is opposite to that observed in other high spin iron/radical ligand systems and appears to be a result of delocalization of the ligand unpaired electron across the whole molecule. The large change in magnetic properties, combined with a delocalized electronic structure and accessible redox potentials, suggests the utility of this and related systems in the development of novel molecular spintronic devices.