Green-Mediated Synthesis of NiCo2O4 Nanostructures Using Radish White Peel Extract for the Sensitive and Selective Enzyme-Free Detection of Uric Acid.

The ability to measure uric acid (UA) non-enzymatically in human blood has been demonstrated through the use of a simple and efficient electrochemical method. A phytochemical extract from radish white peel extract improved the electrocatalytic performance of nickel-cobalt bimetallic oxide (NiCo2O4) during a hydrothermal process through abundant surface holes of oxides, an alteration of morphology, an excellent crystal quality, and increased Co(III) and Ni(II) chemical states. The surface structure, morphology, crystalline quality, and chemical composition were determined using a variety of analytical techniques, including powder X-ray diffraction (XRD), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HR-TEM), and X-ray photoelectron spectroscopy (XPS). The electrochemical characterization by CV revealed a linear range of UA from 0.1 mM to 8 mM, with a detection limit of 0.005 mM and a limit of quantification (LOQ) of 0.008 mM. A study of the sensitivity of NiCo2O4 nanostructures modified on the surface to UA detection with amperometry has revealed a linear range from 0.1 mM to 4 mM for detection. High stability, repeatability, and selectivity were associated with the enhanced electrochemical performance of non-enzymatic UA sensing. A significant contribution to the full outperforming sensing characterization can be attributed to the tailoring of surface properties of NiCo2O4 nanostructures. EIS analysis revealed a low charge-transfer resistance of 114,970 Ohms that offered NiCo2O4 nanostructures prepared with 5 mL of radish white peel extract, confirming an enhanced performance of the presented non-enzymatic UA sensor. As well as testing the practicality of the UA sensor, blood samples from human beings were also tested for UA. Due to its high sensitivity, stability, selectivity, repeatability, and simplicity, the developed non-enzymatic UA sensor is ideal for monitoring UA for a wide range of concentrations in biological matrixes.

Biogenic Preparation of ZnO Nanostructures Using Leafy Spinach Extract for High-Performance Photodegradation of Methylene Blue under the Illumination of Natural Sunlight.

To cope with environmental pollution caused by toxic emissions into water streams, high-performance photocatalysts based on ZnO semiconductor materials are urgently needed. In this study, ZnO nanostructures are synthesized using leafy spinach extract using a biogenic approach. By using phytochemicals contained in spinach, ZnO nanorods are transformed into large clusters assembled with nanosheets with visible porous structures. Through X-ray diffraction, it has been demonstrated that leafy spinach extract prepared with ZnO is hexagonal in structure. Surface properties of ZnO were altered by using 10 mL, 20 mL, 30 mL, and 40 mL quantities of leafy spinach extract. The size of ZnO crystallites is typically 14 nanometers. In the presence of sunlight, ZnO nanostructures mineralized methylene blue. Studies investigated photocatalyst doses, dye concentrations, pH effects on dye solutions, and scavengers. The ZnO nanostructures prepared with 40 mL of leafy spinach extract outperformed the degradation efficiency of 99.9% for the MB since hydroxyl radicals were primarily responsible for degradation. During degradation, first-order kinetics were observed. Leafy spinach extract could be used to develop novel photocatalysts for the production of solar hydrogen and environmental hydrogen.

Charged Particles Transverse Momentum and Pseudorapidity Distribution in Hadronic Collisions at LHC Energies.

We present an analysis of the pseudorapidity η and transverse momentum pT distributions of charged hadrons in pp collisions for the kinematic range of 01 GeV/c at 0.9 and 2.36 TeV within the experimental errors, while Dire overshoots and Vicia undershoots the data by 50% each. At 7 TeV, the Dire module presents a good prediction, whereas the Simple and Vincia modules underestimate the data within 30% and 50%. Comparing the Simple module of the Pythia model and the predictions of the CRMC models with the experimental data shows that at 0.9 TeV, EPOS-LHC has better results than the others. At 2.36 GeV, the cosmic rays Monte Carlo (CRMC) models have better prediction than the Simple module of Pythia at low pT, while QGSJETII-04 predicts well at high pT. QGSJETII-04 and EPOS-LHC have closer results than the Pythia-Simple and Sibyll2.3d at 7 TeV. In the case of the pseudorapidity distributions, only the Pythia-Simple reproduced the experimental measurements at all energies. The Dire module overestimates, while Vincia underestimates the data in decreasing order of discrepancy (20%, 12%, 5%) with energy. All CRMC models underestimate the data over the entire η range at all energies by 20%. The angular ordering of partons and the parton fragmentation could be possible reasons for this deviation. Furthermore, we used the two-component standard distribution to fit the pT spectra to the experimental data and extracted the effective temperature (Teff) and the multiplicity parameter (N0). It is observed that Teff increases with the increase in the center of mass energy. The fit yielded 0.20368±0.01, 0.22348±0.011, and 0.24128±0.012 GeV for 0.9, 2.36, and 7 TeV, respectively. This shows that the system at higher energies freezes out earlier than lower ones because they quickly attain the equilibrium state.

Study of Bulk Properties of Strange Particles in Au+Au Collisions at sNN = 54.4 GeV.

We analyzed the transverse momentum pT spectra of various strange hadrons KS0, Λ(Λ¯) and Ξ-(Ξ¯+) at mid-rapidity (y) in different centrality intervals from Au+Au collisions at sNN= 54.4 GeV. The pT spectra of these strange hadrons are investigated by the Tsallis-like distribution, which satisfactorily fits the experimental data. The bulk properties of the medium produced in ultra-relativistic heavy-ion collisions at the kinetic freeze-out are reflected by measuring the hadron spectra. The effective temperature T, transverse flow velocity ßT, and mean pT along with other parameters that are strongly dependent on centrality and particle specie are extracted. The effective temperature of multi-strange particle (Ξ-(Ξ¯+)) is larger as compared to singly-strange particles Λ(Λ¯) and KS0. Furthermore, the kinetic freeze-out temperature T, transverse flow velocity ßT. and mean pT (⟨pT⟩) show a decreasing trend towards lower centrality, while the entropy parameter q increases from central to peripheral collisions. In addition, a positive correlation of ⟨pT⟩ and T and a negative correlation of q and T are also reported.

Pseudorapidity dependence of the bulk properties of hadronic medium in pp collisions at 7 TeV.

The measured charged particle [Formula: see text] spectra in proton-proton collisions obtained by the CMS experiment at CERN is compared with the simulation results of EPOS-LHC and Pythia8.24 models at 7 TeV center-of-mass energy. The Pythia8.24 model describes the experimental data very well, particularly in the high [Formula: see text] region. The model also predicts the [Formula: see text] spectra for [Formula: see text] [Formula: see text] [Formula: see text] < 2.4 at 0 [Formula: see text] [Formula: see text] [Formula: see text] 6 [Formula: see text]. The EPOS-LHC model underpredicts the [Formula: see text] spectra from 0.1 to 2 [Formula: see text] in all [Formula: see text] bins for about 20% and the [Formula: see text] spectrum from 0.1 to 4.2 [Formula: see text] for [Formula: see text] [Formula: see text] [Formula: see text] < 2.4 by about 15% while reasonably predicts well for [Formula: see text] > 4.2 [Formula: see text] within the experimental errors. Furthermore, to get information about collective properties of the hadronic matter, modified Hagedorn function with embedded transverse flow velocity and thermodynamically consistent Tsallis distribution functions are used to fit the experimental data and simulated results. The values of [Formula: see text] show that the functions fit the data and simulation results well. The parameter extracted by the functions: [Formula: see text], [Formula: see text], and [Formula: see text] decreases with increasing [Formula: see text]. The decrease in [Formula: see text] with increasing [Formula: see text] is due to the large energy deposition in lower rapidity bins producing rapid expansion due to large pressure gradient resulting quick expansion of the fireball. Similarly, large energy transfer in the lower pseudo-rapidity bin results in higher degree of excitation of the system which results larger values of [Formula: see text] and [Formula: see text]. The values of the fit constant [Formula: see text] increase with [Formula: see text] where the values of [Formula: see text] extracted from Pythia8.24 are closer to the data than the EPOS-LHC model. The Pythia8.24 model has better prediction than the EPOS-LHC model which might be connected to its flow-like features and color re-connections resulting from different Parton interactions in the initial and final state.

Study of Kinetic Freeze-Out Parameters as a Function of Rapidity in pp Collisions at CERN SPS Energies.

We used the blast wave model with the Boltzmann-Gibbs statistics and analyzed the experimental data measured by the NA61/SHINE Collaboration in inelastic (INEL) proton-proton collisions at different rapidity slices at different center-of-mass energies. The particles used in this study were π+, π-, K+, K-, and p¯. We extracted the kinetic freeze-out temperature, transverse flow velocity, and kinetic freeze-out volume from the transverse momentum spectra of the particles. We observed that the kinetic freeze-out temperature is rapidity and energy dependent, while the transverse flow velocity does not depend on them. Furthermore, we observed that the kinetic freeze-out volume is energy dependent, but it remains constant with changing the rapidity. We also observed that all three parameters are mass dependent. In addition, with the increase of mass, the kinetic freeze-out temperature increases, and the transverse flow velocity, as well as kinetic freeze-out volume decrease.