By considering the topology of chiral crystals, a new type of massless fermion, connected with giant arc-like surface states, are predicted. Such Kramers–Weyl fermions should manifest themselves in a ...wide variety of chiral materials.
Pesticides released intentionally into the environment and through various processes contaminate the environment. Although pesticides are associated with many health hazards, there is a lack of ...monitoring of these contaminants. Traditional chromatographic methods—high-performance liquid chromatography, capillary electrophoresis, and mass spectrometry—are effective for the analysis of pesticides in the environment but have certain limitations such as complexity, time-consuming sample preparation, and the requirement of expensive apparatus and trained persons to operate. Over the past decades, acetylcholinesterase (AChE) inhibition-based biosensors have emerged as simple, rapid, and ultra-sensitive tools for pesticide analysis in environmental monitoring, food safety, and quality control. These biosensors have the potential to complement or replace the classical analytical methods by simplifying or eliminating sample preparation and making field-testing easier and faster with significant decrease in cost per analysis. This article reviews the recent developments in AChE inhibition-based biosensors, which include various immobilization methods, different strategies for biosensor construction, the advantages and roles of various matrices used, analytical performance, and application methods for constructing AChE biosensors. These AChE biosensors exhibited detection limits and linearity in the ranges of 1.0×10-11 to 42.19 μM (detection limits) and 1.0×10−11–1.0×10−2 to 74.5–9.9×103μM (linearity). These biosensors were stable for a period of 2 to 120days. The future prospects for the development of better AChE biosensing systems are also discussed.
Applying a temperature gradient in a magnetic material generates a voltage that is perpendicular to both the heat flow and the magnetization. This phenomenon is the anomalous Nernst effect (ANE), ...which was long thought to be proportional to the value of the magnetization. However, more generally, the ANE has been predicted to originate from a net Berry curvature of all bands near the Fermi level (EF). Subsequently, a large anomalous Nernst thermopower (SyxA) has recently been observed in topological materials with no net magnetization but a large net Berry curvature Ωn(k) around EF. These experiments clearly fall outside the scope of the conventional magnetization model of the ANE, but a significant question remains. Can the value of the ANE in topological ferromagnets exceed the highest values observed in conventional ferromagnets? Here, we report a remarkably high SyxA-value of ~6.0 µV K−1 in the ferromagnetic topological Heusler compound Co2MnGa at room temperature, which is approximately seven times larger than any anomalous Nernst thermopower value ever reported for a conventional ferromagnet. Combined electrical, thermoelectric, and first-principles calculations reveal that this high-value of the ANE arises from a large net Berry curvature near the Fermi level associated with nodal lines and Weyl points.Energy conversion: Heat- recovery magnets identifiedThermoelectric devices that convert heat into electricity may benefit from the unusual temperature sensitivity of cobalt–manganese–gallium (Co2MnGa) ferromagnets. When one end of a magnetized metal is made hot and the other cold, redistribution of electrons creates an electric voltage perpendicular to the temperature gradient. Satya N. Guin from the Max Planck Institute for Chemical Physics of Solids in Dresden, Germany, and colleagues now report how certain class of material can boost the electrical power produced from “waste heat” source using transverse thermoelectric effect. When the team applied magnetic fields to Co2MnGa and characterized its transverse electrical response to temperature gradient, they saw voltage generation several times higher than expected. Computer simulations indicated that the crystal geometry distorted the energy levels available to electron making it easier for electrons to move when thermally excited.
The stepwise amperometric biosensor fabrication process and immobilized acetylcholinesterase inhibition in pesticide solution.
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• Constructed a novel composite material using Fe
3O
4NP ...and c-MWCNT at Au electrode for electrocatalysis. • The properties of nanoparticles modified electrodes were studied by SEM, FTIR, CVs and EIS. • The biosensor exhibited good sensitivity (0.475
mA
μM
−1) • The half life of electrode was 2 months. • The sensor was suitable for trace detection of OP pesticide residues in milk and water.
An acetylcholinesterase (AChE) purified from maize seedlings was immobilized covalently onto iron oxide nanoparticles (Fe
3O
4NP) and carboxylated multi walled carbon nanotubes (c-MWCNT) modified Au electrode. An organophosphorus (OP) biosensor was fabricated using this AChE/Fe
3O
4/c-MWCNT/Au electrode as a working electrode, Ag/AgCl as standard and Pt wire as an auxiliary electrode connected through a potentiostat. The biosensor was based on inhibition of AChE by OP compounds/insecticides. The properties of nanoparticles modified electrodes were studied by scanning electron microscopy (SEM), Fourier transform infrared (FTIR), cyclic voltammograms (CVs) and electrochemical impedance spectroscopy (EIS). The synergistic action of Fe
3O
4NP and c-MWCNT showed excellent electrocatalytic activity at low potential (+0.4
V). The optimum working conditions for the sensor were pH 7.5, 35
°C, 600
μM substrate concentration and 10
min for inhibition by pesticide. Under optimum conditions, the inhibition rates of OP pesticides were proportional to their concentrations in the range of 0.1–40
nM, 0.1–50
nM, 1–50
nM and 10–100
nM for malathion, chlorpyrifos, monocrotophos and endosulfan respectively. The detection limits were 0.1
nM for malathion and chlorpyrifos, 1
nM for monocrotophos and 10
nM for endosulfan. The biosensor exhibited good sensitivity (0.475
mA
μM
−1), reusability (more than 50 times) and stability (2 months). The sensor was suitable for trace detection of OP pesticide residues in milk and water.
The Bacillus amyloliquefaciens-SN13 and model crop rice (Oryza sativa) were chosen to understand the complex regulatory networks that govern plant-PGPR interaction under salt stress. During stress, ...inoculation with SN13 significantly increased biomass, relative water content, proline and total soluble sugar in rice while decreased lipid peroxidation and electrolyte leakage. Extensive alterations in gene expression were also observed in rice root transcriptome under stress in the presence of SN13. Rhizobacteria induced changes in expression of a considerable number of photosynthesis, hormone, and stress-responsive genes, in addition to cell-wall and lipid metabolism-related genes under salt stress as compared to salt stress or SN13 inoculation alone, indicating its potential role in reducing the harmful effects of salinity. To validate RNA-seq data, qRT-PCR was performed for selected differentially expressed genes representing various functional categories including metabolism, regulation, stress-response, and transporters. Results indicate qualitative and quantitative differences between roots responses to SN13 under stressed and unstressed conditions. Functional expressions of OsNAM and OsGRAM in yeast showed enhanced tolerance to various abiotic stresses, indicating crucial SN13-rice interaction in imparting beneficial effects under stress. This is first detailed report on understanding molecular mechanism underlying beneficial plant-microbe interaction in any economically important model crop plant under abiotic stress.
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► Constructed AChE biosensor based on AChE/Fe3O4NPs/c-MWCNT/ITO electrode. ► Enzyme electrode was characterized by AFM, FTIR, CV and EIS. ► Detection limit and working range of ...biosensor were 0.1nM and 0.1–100nM. ► Half life of enzyme electrode was 3 months. ► Biosensor measured pesticides in environmental and food samples.
A method is described for the construction of a highly sensitive electrochemical biosensor for the detection of malathion, chlorpyrifos, monocrotophos and endosulfan based on covalent immobilization of acetylcholinesterase (AChE) on iron oxide nanoparticles (Fe3O4NPs)-decorated carboxylated multi-walled carbon nanotubes (c-MWCNTs) electrodeposited onto indium tin oxide (ITO)-coated glass plate. Transmission electron microscopic (TEM) and UV analysis of nanocomposite materials demonstrated that Fe3O4NPs were well deposited on the outer walls of c-MWCNTs. The modified electrode was characterized by atomic force microscopy (AFM), cyclic voltammetry (CV), Fourier transform infrared (FTIR) spectroscopy and electrochemical impedance spectroscopy (EIS). The resulting biosensor exhibited a linear response for acetylthiocholine in a concentration range of 0.1–700μmolL−1 with a remarkable sensitivity of 0.402mA/μmolL−1. Under optimum conditions, the inhibition rates of pesticides were proportional to their concentrations in the range of 0.1–70nmolL−1, 0.1–50nmolL−1, 0.1–70nmolL−1 and 0.1–100nmolL−1 for malathion, chlorpyrifos, monocrotophos and endosulfan, respectively. The detection limit of the biosensor for all pesticides was 0.1nmolL−1 at a signal-to-noise ratio of 3. The biosensor showed good reproducibility, no interference by metal ions and long-term stability. The measurement results obtained by the present biosensor were in good agreement with those obtained by the standard gas chromatography–mass spectrometry method. The biosensor was employed for the determination of pesticides in environmental and food samples.
•Decision Tree, SVM, and Naive Bayes classification devices were used by Deepti and Dilip to diagnose diabetes.•The goal was to classify large amounts of the separator. PIMA Indian database was ...used.•Naive Bayes, scored 76.30 per cent, received the best accuracy. Six separators were used.•The LIBSVM wallet F select script selects four characteristics and selects 9 and 20 characteristics.•The sugar prediction can be more accurate with 72 percent precision.
Diabetes mellitus is a disease commonly called Diabetes. Diabetes is among the most frequent diseases globally. This disease affects internationally with different ailments and complications in majority of peoples. Diabetes is a chronic disease with the ability to create a global medical care crisis. In compliance with International Diabetes Federation 382 million people are reportedly living with diabetes throughout the entire world. The diabetes disorder can't be cured but it can control, diagnosis and prediction of diabetes is vital to restrain the death rate as a result of its seriousness globally. Many In recent years, machine learning (ML) algorithms have been used in the prediction of diabetes. A clever predictive model utilizing deep modelling is commonly advised with the aid of conditional data collection to forecast the severity and particular risk factor of diabetics. In this article, to resolve this problem we employed the Interpretable Filter based Convolutional Neural Network (IF-CNN) prediction model and Pet Dog-Smell Sensing (PD-SS) algorithm that can automatically predict the diabetes from PIMA Indian diabetes datasets. This may enhance the general strategy of disease prediction in patients database that may solve the issues faced by traditional algorithms employing the Deep Neural Network (DNN) methods. The automated extraction, selection and classification of attributes, disease forecast is the hard task with aggressive performance for your PIMA information which may be implemented with the projected Deep Learning version economically. This may enhance the general plan of disease prediction from patients database that may solve the issues faced by traditional algorithms employing the Interpretable Filter established Deep Learning version prediction model to diagnose and predict the diabetes disease in multi-level databases. The intention of this research is to create a method that could carry out early diabetes predictions for a more reliable patient by including findings of SVM and CNN-LSTM(Long Short-Term Memory) machine learning methods also IF-CNN achieved 96.26% accuracy.
Allium cepa L. is an important medicinal and food plant enormously affected by salinity in terms of its growth and quality. This experiment investigates ameliorative potential of NO donor sodium ...nitroprusside (SNP) on chromosomal aberrations and physiological parameters in A. cepa L. roots exposed to salinity stress. Roots with different concentrations of NaCl (25, 50, and 100 mM) alone, and in combination with 100 µM SNP were analyzed for mitotic aberrations, DNA damage, proline, malondialdehyde (MDA) content, and ascorbate-glutathione (AsA-GSH) cycle after 120 h of salinity treatments. Results revealed that salinity stress increased chromosomal aberrations, MDA, proline accumulation, and severely hampered the AsA-GSH cycle function. The comet assay revealed a significant (p ≤ 0.05) enhancement in tail length (4.35 ± 0.05 µm) and olive tail moment (3.19 ± 0.04 µm) at 100 mM NaCl exposure. However, SNP supplementation decreased total percent abnormalities, while increased the prophase, metaphase, anaphase, and telophase indexes. Moreover, ascorbate peroxidase and glutathione reductase activities increased with AsA/DHA and GSH/GSSG ratios, respectively. Results suggest that SNP supplementation alleviates salinity stress responses by improving AsA-GSH cycle and proline accumulation. Based on present findings, NO supplementation could be recommended as a promising approach for sustainable crop production under salinity stress.
Allium cepa L. response to salt stress has been investigated but its role on chromosomal changes and DNA damage are less investigated therefore, our focus is to explore NO supplementation effects on cytological aberrations and biochemical responses in A. cepa L. roots under salinity stress.
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•Best pretreatment achieve delignification, recover carbohydrate & improve biomethanation.•Physically pretreated biomass achieve >50% biodegradation (BD) and reach up to 92%•Chemical ...pretreatment induce 59% cellulose & 69% lignin losses & achieve 88.3% BD.•High temperature with and without chemical pretreatment leads to recalcitrant formation.•Bacterial pretreatment is superior to fungal and algal pretreatment.
Agro-waste (wheat straw) is considered most effective (heating value 16 MJ/kg) for energy recovery through anaerobic digestion (AD). However, the complex lignocellulosic structure obstructs their biotransformation and is a rate-limiting step of the AD process. The pretreatment of agro-waste could remove the physical and chemical barriers and accelerate the downstream AD process. The physical pretreatments (mechanical, thermal, sonication) improve the biodegradation kinetics of wheat straw (>50% and can reach up to 92%) but require more energy. High external or internal heat sources may lead to the formation of recalcitrant compounds, i.e., furan derivatives from cellulose. Chemical pretreatment is effective for rapid reaction rate but is uneconomical due to high chemical cost. It may produce toxic intermediary products, and hinder microbial activities in AD. The ionic liquids are emerging chemical pretreatment and are renewable, recoverable, difficult to oxidize, and bio-based salts. The chemical pretreatment induces cellulose loss up to 59% and lignin up to 69%, while the bio-degradability is as high as 88.3%. In biological pretreatment, bacterial pretreatment is superior to fungal and algal pretreatments, and also helps in the substrate bio-degradation during AD. The FTIR, TGA-DTG, XRD, and SEM technologies can validate the efficiency of the pretreatment method, such as modifications in lignocellulosic functional groups, mass loss, crystal feature, surface topography, and morphology of lignocellulosic biomass. Different mathematical models, especially the modified Gompertz model, are employed to facilitate the insight into the AD research. Energy use and balance are necessary for the pretreatment process to figure out the real synergic effects rather than aiming to achieve enhanced methane production.
Topology, a mathematical concept, has recently become a popular and truly transdisciplinary topic encompassing condensed matter physics, solid state chemistry, and materials science. Since there is a ...direct connection between real space, namely atoms, valence electrons, bonds, and orbitals, and reciprocal space, namely bands and Fermi surfaces, via symmetry and topology, classifying topological materials within a single-particle picture is possible. Currently, most materials are classified as trivial insulators, semimetals, and metals or as topological insulators, Dirac and Weyl nodal-line semimetals, and topological metals. The key ingredients for topology are certain symmetries, the inert pair effect of the outer electrons leading to inversion of the conduction and valence bands, and spin–orbit coupling. This review presents the topological concepts related to solids from the viewpoint of a solid-state chemist, summarizes techniques for growing single crystals, and describes basic physical property measurement techniques to characterize topological materials beyond their structure and provide examples of such materials. Finally, a brief outlook on the impact of topology in other areas of chemistry is provided at the end of the article.
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