To meet the energy needs batteries and supercapacitors are evolved as a promising candidate from the class of energy storage devices. The growth in the development of new 2D electrode materials ...brings a new revolution in energy storage devices with a comprehensive investigation. MXene, a new family of 2D metal carbides, nitrides and carbonitrides due to their attractive electrical and electrochemical properties e.g. hydrophilicity, conductivity, surface area, topological structure have gained huge attention. In this review, we discussed different MXene synthesis routes using different etchants e.g. hydrofluoric acid, ammonium hydrazine, lithium fluoride, and hydrochloric acid, etc showing that fluorine formation is compulsory to etch the aluminum layer from its precursor. Due to the advantage of large interlayer spacing between the MXene layers in MXene, the effect of intercalation on the performance of batteries and supercapacitors using MXene as electrodes by various sized cations are reviewed. Different MXene hybrids as supercapacitor electrodes will also be summarized. Lastly, the conclusion and future scope of MXene to be done in various supercapacitor applications are also presented.
High electrical conductivity and superior redox properties of metal sulfide-based supercapacitors have attracted much attention in recent years. The simple and cost-effective method in the ...fabrication of high-performance supercapacitors is currently in high demand. In this paper, low-cost one-dimensional copper sulfide (Cu
2
S) electrodes are synthesized on glass as well as on flexible substrates such as polyethylene terephthalate (PET) and polypropylene (PP). The effect of the deposition quantity of Cu
2
S-1:1 on the glass substrate is also discussed. The synthesis of copper sulfide was done at room temperature by reducing copper sulphate pentahydrate using ascorbic acid as a reducing agent in sodium thiosulphate with 2 h of total reaction time. Scanning electron microscopy and x-ray diffraction characterizations are performed to validate the formation of Cu
2
S hollow rods. Electrochemical measurements such as cyclic voltammetry, galvanostatic charge-discharge, and electrochemical impedance spectroscopy are performed using a Metrohm Autolab workstation. Cyclic voltammetry is performed to measure the capacitance of Cu
2
S-based supercapacitors in which the ratio of copper sulphate and sodium thiosulphate was varied from 1:0.5 to 1:1.5 with a step size of 0.5, and the deposition quantity of Cu
2
S-1:1 film was also varied on glass substrate from 1 mg to 2 mg. The results show that the device with a 1:1 ratio shows the highest capacitance, i.e., 587 mF/cm
2
as compared to the devices fabricated with a 1:0.5 ratio, 1:1.5 ratio, and 1:1 ratio with greater deposition. This is mainly because the 1:1 ratio has less resistance and has a hollow rod structure which allows the electrolyte ions to penetrate in Cu
2
S active material and thus, facilitates fast electron transport resulting in high-performance supercapacitors. Further, to understand the increased capacitive properties of a copper sulfide-based supercapacitor, processes involving charge transfer and mass transport are investigated by performing electrochemical impedance spectroscopy (EIS). The radius on the EIS plot of Cu
2
S-1:1 is smaller as compared to the other three samples on the glass substrate. Also, the resistance of Cu
2
S-1:1 with greater deposition is more than the Cu
2
S-1:1 sample because the increased amount of electrode material leads to increased paths for the electrolyte ions to interact with the electrode material. Further, this paper also discusses the successful fabrication of the supercapacitor devices on flexible PP substrate using 1-D Cu
2
S for the first time. The results show that the capacitance value on the flexible substrate is on par with that of glass substrates. Also, the synthesized copper sulfide 1:1 sample exhibits excellent stability with the capacitance retention of 85.7%, 91.1%, 86.18%, and 92.8%, respectively, on PP, glass, PET, and Cu
2
S-1:1 with more deposition on glass substrate after 3500 cycles.
Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert’s opinion. ...Artificial Intelligence has recently penetrated COVID-19, especially deep learning paradigms. There are
nine kinds
of classification systems in this study, namely
one
deep learning-based CNN,
five
kinds of transfer learning (TL) systems namely VGG16, DenseNet121, DenseNet169, DenseNet201 and MobileNet,
three
kinds of machine-learning (ML) systems, namely artificial neural network (ANN), decision tree (DT), and random forest (RF) that have been designed for classification of COVID-19 segmented CT lung against Controls. Three kinds of characterization systems were developed namely (a) Block imaging for COVID-19 severity index (CSI); (b) Bispectrum analysis; and (c) Block Entropy. A cohort of Italian patients with 30 controls (990 slices) and 30 COVID-19 patients (705 slices) was used to test the performance of three types of classifiers. Using K10 protocol (90% training and 10% testing), the best accuracy and AUC was for DCNN and RF pairs were
99.41 ± 5.12%, 0.991 (
p
< 0.0001)
, and
99.41 ± 0.62%, 0.988 (
p
< 0.0001)
, respectively, followed by other ML and TL classifiers. We show that diagnostics odds ratio (DOR) was higher for DL compared to ML, and both, Bispecturm and Block Entropy shows higher values for COVID-19 patients. CSI shows an association with Ground Glass Opacities (0.9146,
p
< 0.0001). Our hypothesis holds true that deep learning shows superior performance compared to machine learning models. Block imaging is a powerful novel approach for pinpointing COVID-19 severity and is clinically validated.
Electrostatic self-assembly of macroions is an emerging area with great potential in the development of nanoscale functional objects, where photo-irradiation responsiveness can either elevate or ...suppress the self-assembly. The ability to control the size and shape of macroion assemblies would greatly facilitate the fabrication of desired nano-objects that can be harnessed in various applications such as catalysis, drug delivery, bio-sensors, and actuators. Here, we demonstrate that a polyelectrolyte with a size of 5 nm and multivalent counterions with a size of 1 nm can produce well-defined nanostructures ranging in size from 10-1000 nm in an aqueous environment by utilizing the concept of electrostatic self-assembly and other intermolecular non-covalent interactions including dipole-dipole interactions. The
- and photoresponsiveness of polyelectrolytes and azo dyes provide diverse parameters to tune the nanostructures. Our findings demonstrate a facile approach to fabricating and manipulating self-assembled nanoparticles using light and neutron scattering techniques.
Wilson’s disease (WD) is caused by copper accumulation in the brain and liver, and if not treated early, can lead to severe disability and death. WD has shown white matter hyperintensity (WMH) in the ...brain magnetic resonance scans (MRI) scans, but the diagnosis is challenging due to (i) subtle intensity changes and (ii) weak training MRI when using artificial intelligence (AI). Design and validate seven types of high-performing AI-based computer-aided design (CADx) systems consisting of 3D optimized classification, and characterization of WD against controls. We propose a “conventional deep convolution neural network” (cDCNN) and an “improved DCNN” (iDCNN) where rectified linear unit (ReLU) activation function was modified ensuring “differentiable at zero.” Three-dimensional optimization was achieved by recording accuracy while changing the CNN layers and augmentation by several folds. WD was characterized using (i) CNN-based feature map strength and (ii) Bispectrum strengths of pixels having higher probabilities of WD. We further computed the (a) area under the curve (AUC), (b) diagnostic odds ratio (DOR), (c) reliability, and (d) stability and (e) benchmarking. Optimal results were achieved using 9 layers of CNN, with 4-fold augmentation. iDCNN yields superior performance compared to cDCNN with accuracy and AUC of 98.28 ± 1.55, 0.99 (
p
< 0.0001), and 97.19 ± 2.53%, 0.984 (
p
< 0.0001), respectively. DOR of iDCNN outperformed cDCNN fourfold. iDCNN also outperformed (a) transfer learning–based “Inception V3” paradigm by 11.92% and (b) four types of “conventional machine learning–based systems”:
k
-NN, decision tree, support vector machine, and random forest by 55.13%, 28.36%, 15.35%, and 14.11%, respectively. The AI-based systems can potentially be useful in the early WD diagnosis.
Graphical Abstract
Chitinase and surfactin-mediated biocontrol of Rhizoctonia solani and Fusarium oxysporum causing wilt and root rot of Fagopyrum esculentum respectively has been studied in this communication. ...Bacillus pumilus MSUA3 as a potential bacterial strain strongly inhibited the growth of R. solani and F. oxysporum involving the chitinolytic enzymes and an antibiotic surfactin. Plant growth promoting attributes seem to be involved in plant growth promotion and yield attributes. The action of cell-free culture supernatant (CFCS) was found deleterious to F. oxysporum and R. solani even in the heat-treated (boiled/autoclaved) CFCS. The possible involvement of surfactin in disease control was revealed by colony PCR amplification of SrfA. Chitinolytic enzyme and antibiotic surfactin evidenced differential biocontrol of F. oxysporum and R. solani by B. pumilus MSUA3. A significant reduction in disease index under gnotobiotic conditions and productivity enhancement of F. esculentum using vermiculite-based bioformulation revealed B. pumilus MSUA3 as a successful potential biocontrol agent (BCA) and an efficient plant growth promoting rhizobacterium (PGPR) for disease management and productivity enhancement of buckwheat crop.
The wireless sensor network (WSN) consist of battery-powered sensor nodes which are self-configured and are deployed for monitoring several physical or environmental conditions such as temperature, ...pressure, humidity, vibration, pollutants etc. The major constraint in most of the WSN applications is the replacement/recharging of the battery contained by the node once it gets exhausted. This limitation reduces the lifetime of WSN. The placement of energy harvesting device within the sensor node may be the best probable solution to recharge the exhausted battery. In this paper, the integration of low cost, light weight and foldable flexible solar cells with WSN has been focused. The aim of this paper is to fabricate the flexible solar cells and showing the potential use of them in WSN. Moreover, the use of flexible solar cell is the better selection for emerging wearable WSN. This paper also describes the various issues in the already developed energy harvesting models and suggested a self-powered model for energy management based on finite state machine (FSM). The proposed models completely avoid the overcharging and the frequent charging of the batteries. This optimal utilization of the battery maximizes the lifetime of WSN network. In the proposed model, the flexible p–i–n solar cells are used to convert solar energy into electrical energy that can charge the battery of the WSN node. Finally, it can be concluded that the node will continue to function actively till the battery lifetime i.e. approximately 25–30 years.
Shear and torsional load on soft solids such as brain white matter purportedly exhibits the Poynting Effect. It is a typical nonlinear phenomenon associated with soft materials whereby they tend to ...elongate (positive Poynting effect) or contract (negative Poynting effect) in a direction perpendicular to the shearing or twisting plane. In this research, a novel 3D micromechanical Finite Element Model (FEM) has been formulated to describe the Poynting effect in bi-phasic modeled brain white matter (BWM) representative volume element (RVE) with axons tracts embedded in surrounding extracellular matrix (ECM) for simulating brain matter's response to pure and simple shear. In the presented BWM 3D FEM, nonlinear Ogden hyper-elastic material model is deployed to interpret axons and ECM material phases. The modeled bi-phasic RVEs have axons tied to the surrounding ECM. In this proof-of-concept (POC) FEM, three simple shear loading configurations and a pure shear case were analyzed. Root mean square deviation (RMSD) was calculated for stress and deformation response plots to understand the effect of axon-ECM orientations and loading conditions on the degree of Poynting behavior. Variations in normal stresses (S11, S22, or S33) perpendicular to the shear plane underscored the significance of axonal fiber-matrix interactions. From the simulated ensemble of cases, a transitional dominance trend was noticed, as simple sheared axons showed pronounced Poynting behavior, but shear deformation build-up in the purely sheared brain model exhibited the highest Poynting behavior at higher strain % limits. At lower strain limits, simple shear imparted across and perpendicular to axonal tract directions emerged as the dominant Poynting effect configurations. At high strains, the stress-strain% plots manifested mild strain stiffening effects and bending stresses in purely sheared axons, substantiated the strong non-linearity in brain tissues’ response.
Introduction: Six-minute walk test (6MWT) has a significant prognostic value in chronic obstructive pulmonary disease (COPD). Those who desaturate early during 6MWT are likely to have frequent ...exacerbations. Aims and Objectives: To follow-up and compare exacerbations and hospitalisations of COPD patients having early desaturation versus nonearly desaturation determined during baseline 6MWT. Methods: It was a longitudinal follow-up study conducted in a tertiary care institute from November 1, 2018 to May 15, 2020 involving 100 COPD patients. A decrease in SpO2 by ≥4% in baseline 6MWT was considered a significant desaturation. If the desaturation occurred within first minute of the 6MWT, the patient was called early desaturator (ED); if it occurred later, the patient was called nonearly desaturator (NED). If the saturation did not fall, then the patient was called nondesaturator. During the follow-up, 12 patients dropped out and 88 remained. Results: Of 88 patients, 55 (62.5%) were desaturators and 33 were nondesaturator. Of 55 desaturators, 16 were ED and 39 were NED. EDs had significantly higher number of severe exacerbations (P <.05), higher hospitalisation (P <.001), and higher BODE index (P <.01) compared to NEDs. The receptor operating characteristic curve and multiple logistic regression analysis showed that previous exacerbations, presence of early desaturation, and distance saturation product during the 6MWT were significant predictors for predicting hospitalizations. Conclusion: Early desaturation can be used as a screening tool for assessing the risk of hospitalization in COPD patients.
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