Study of graphene incorporation into ZnO-PVA nanocomposites modified electrode for sensitive detection of cadmium.

The presence of heavy metals often causes significant health risks, particularly cadmium, which is known for its high toxicity. In this study, a glassy carbon electrode was successfully modified by incorporating ZnO-PVA-Graphene nanocomposite, leveraging the excellent electrical properties and electron mobility of the material. Comprehensive material analysis, including XRD, confirmed that ZnO maintained its hexagonal wurtzite crystal structure despite the addition of graphene. Moreover, FESEM analysis showed that increasing graphene concentration led to a reduction in ZnO particle size by 85, 68, and 52 nm, respectively, accompanied by a decrease in band gap energy, as verified by UV-Vis measurements. Photoluminescence tests were also conducted and the result showed a noticeable blue shift in ZnO-PVA-Graphene nanocomposites compared to ZnO-PVA, specifically in the near band-edge (NBE) UV emission within the 374-379 nm wavelength range. Through I-V characterization, the optimal graphene concentration for cadmium detection was identified as 1.5 wt% in ZnO-PVA-Graphene nanocomposites, showing an approximate ohmic response. Meanwhile, square-wave voltammetry analysis of cadmium concentrations ranging from 0 to 80 ppm produced a coefficient of determination of 0.98926 and a Limit of Detection (LOD) of 9.88 ppm. These results showed the significant potential of ZnO-PVA-Graphene nanocomposites as a promising material for further development as an effective electrode modifier, enhancing the sensitivity of detection systems.

Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan.

Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the Fei-Tsui reservoir for decades since it's the primary water source for Taipei City. Therefore, this study aims to develop a reliable prediction model to predict D.O. in the Fei-Tsui reservoir for better water quality monitoring. The proposed model is an artificial neural network (ANN) with one hidden layer. Twenty-nine years of water quality data have been used to validate the accuracy of the proposed model. A different number of neurons have been investigated to optimize the model's accuracy. Statistical indices have been used to examine the reliability of the model. In addition to that, sensitivity analysis has been carried out to investigate the model's sensitivity to the input parameters. The results revealed the proposed model capable of capturing the dissolved oxygen's nonlinearity with an acceptable level of accuracy where the R-squared value was equal to 0.98. The optimum number of neurons was found to be equal to 15-neuron. Sensitivity analysis shows that the model can predict D.O. where four input parameters have been included as input where the d-factor value was equal to 0.010. This main achievement and finding will significantly impact the water quality status in reservoirs. Having such a simple and accurate model embedded in IoT devices to monitor and predict water quality parameters in real-time would ease the decision-makers and managers to control the pollution risk and support their decisions to improve water quality in reservoirs.

Effect of magnetic field strength on the alignment of α''-Fe16N2 nanoparticle films.

Aligning the magnetic orientation is one strategy to improve the magnetic performance of magnetic materials. In this study, well-dispersed single-domain core-shell α''-Fe16N2/Al2O3 nanoparticles (NPs) were aligned by vertically applying magnetic fields with various strengths to a Si wafer substrate followed by fixation with resin. X-ray diffraction indicated that the alignment of the easy c-axis of the α''-Fe16N2 crystal and the magnetic orientation of the NPs depended upon the applied magnetic field. Magnetic analysis demonstrated that increasing the magnetic field strength resulted in hysteresis loops approaching a rectangular form, implying a higher magnetic coercivity, remanence, and maximum energy product. The same tendency was also observed when a horizontal magnetic field was applied. The fixation of the easy c-axis alignment of each nanoparticle caused by Brownian rotation under the magnetic field, instead of Néel rotation, was the reason for the enhancement in the magnetic performance. These results on the alignment of the magnetic orientation of α''-Fe16N2 NPs suggest the practical application of high-performance permanent bulk magnets from well-dispersed single-domain α''-Fe16N2/Al2O3 NPs.

Preparation and evaluation of magnetic nanocomposite fibers containing α″-Fe16N2 and α-Fe nanoparticles in polyvinylpyrrolidone via magneto-electrospinning.

Two kinds of ferromagnetic nanocomposite fiber comprising α″-Fe16N2 and α-Fe nanoparticles (NPs), which have the highest magnetic moments as hard and soft magnetic materials, respectively, embedded in polyvinylpyrrolidone (PVP) have been synthesized via the magneto-electrospinning method. Both α″-Fe16N2 and α-Fe were single-domain core-shell NPs with an average outer diameter of 50 nm and Al2O3 as the shell. Ferrofluid precursors used for the electrospinning were prepared by dispersing these NPs in a PVP-toluene-methanol solution. The results show that applying the magnetic field in the same direction as the electric field resulted in smaller and more uniform fiber diameters. Nanocomposite fibers containing α″-Fe16N2 had smaller diameters than those containing α-Fe NPs. These magnetic-field effects on the fiber formation were explained by referring to the kinetic energy of the moving jet in the electrospinning process. In addition, magnetic hysteresis curves showed an enhancement of the magnetic coercivity (H(c)) and remanence (M(r)) by 22.9% and 22.25%, respectively. These results imply a promising possibility of constructing bulk magnetic materials using α″-Fe16N2 NPs, which furthermore reveals attractive features for many other magnetic applications, such as magnetic sensors.

Low-Energy Bead-Mill Dispersion of Agglomerated Core-Shell α-Fe/Al2O3 and α″-Fe16N2/Al2O3 Ferromagnetic Nanoparticles in Toluene.

Magnetic materials such as α″-Fe16N2 and α-Fe, which have the largest magnetic moment as hard and soft magnetic materials, are difficult to produce as single domain magnetic nanoparticles (MNPs) because of quasistable state and high reactivity, respectively. The present work reports dispersion of agglomerated plasma-synthesized core-shell α″-Fe16N2/Al2O3 and α-Fe/Al2O3 in toluene by a new bead-mill with very fine beads to prepare single domain MNPs. As a result, optimization of the experimental conditions (bead size, rotation speed, and dispersion time) enables the break-up of agglomerated particles into primary particles without destroying the particle structure. Slight deviation from the optimum conditions, i.e., lower or higher dispersion energy, gives undispersed or broken particles due to fragile core-shell structure against stress or impact force of beads. The dispersibility of α″-Fe16N2/Al2O3 is more restricted than that of α-Fe/Al2O3, because of the preparation conditions. Especially for α″-Fe16N2/Al2O3, no change on crystallinity (98% α″-Fe16N2) or magnetization saturation after dispersion was observed, showing that this method is appropriate to disperse α″-Fe16N2/Al2O3 MNPs. A different magnetic hysteresis behavior is observed for well-dispersed α″-Fe16N2/Al2O3 MNPs, and the magnetic coercivity of these NPs is constricted when the magnetic field close to zero due to magnetic dipole coupling among dispersed α″-Fe16N2 MNPs.

Self-assembly of colloidal nanoparticles inside charged droplets during spray-drying in the fabrication of nanostructured particles.

Studies on self-assembly of colloidal nanoparticles during formation of nanostructured particles by spray-drying methods have attracted a large amount of attention. Understanding the self-assembly phenomenon allows the creation of creative materials with unique structures that may offer performance improvements in a variety of applications. However, current research on the self-assembly of colloidal nanoparticles have been conducted only on uncharged droplet systems. In this report, we first investigated the self-assembly processes of charged colloidal nanoparticles in charged droplets during spray-drying. Silica nanoparticles and polystyrene spheres are used as a model system. To induce a positive or a negative charge on the droplets, we used an electrospray method. Repulsive and attractive interactions between charged colloidal nanoparticles and droplet surface are found to control the self-assembly of colloidal nanoparticles inside the charged droplet. Interestingly, self-assembly of colloidal nanoparticles inside charged droplets under various processing parameters (i.e., droplet charge, droplet diameter, and surface charge, size, and composition of colloidal nanoparticles) allows the formation of unique nanostructured particles, including porous and hollow particles with control over the internal structure, external shape, number of hollow cavities, and shell thickness, in which this level of control cannot be achieved using conventional spray-drying method.

Influences of surface charge, size, and concentration of colloidal nanoparticles on fabrication of self-organized porous silica in film and particle forms.

Studies on preparation of porous material have attracted tremendous attention because existence of pores can provide material with excellent performances. However, current preparation reports described successful production of porous material with only partial information on charges, interactions, sizes, and compositions of the template and host materials. In this report, influences of self-assembly parameters (i.e., surface charge, size, and concentration of colloidal nanoparticles) on self-organized porous material fabrication were investigated. Silica nanoparticles (as a host material) and polystyrene (PS) spheres (as a template) were combined to produce self-assembly porous materials in film and particle forms. The experimental results showed that the porous structure and pore size were controllable and strongly depended on the self-assembly parameters. Materials containing highly ordered pores were effectively created only when process parameters fall within appropriate conditions (i.e., PS surface charge ≤ -30 mV; silica-to-PS size ratio ≤0.078; and silica-to-PS mass ratio of about 0.50). The investigation of the self-assembly parameter landscape was also completed using geometric considerations. Because optimization of these parameters provides significant information in regard to practical uses, results of this report could be relevant to other functional properties.

Experimental evaluation of the pressure and temperature dependence of ion-induced nucleation.

An experimental system for the study of ion-induced nucleation in a SO(2)/H(2)O/N(2) gas mixture was developed, employing a soft x-ray at different pressure and temperature levels. The difficulties associated with these experiments included the changes in physical properties of the gas mixture when temperature and pressure were varied. Changes in the relative humidity (RH) as a function of pressure and temperature also had a significant effect on the different behaviors of the mobility distributions of particles. In order to accomplish reliable measurement and minimize uncertainties, an integrated on-line control system was utilized. As the pressure decreased in a range of 500-980 hPa, the peak concentration of both ions and nanometer-sized particles decreased, which suggests that higher pressure tended to enhance the growth of particles nucleated by ion-induced nucleation. Moreover, the modal diameters of the measured particle size distributions showed a systematic shift to larger sizes with increasing pressure. However, in the temperature range of 5-20 °C, temperature increases had no significant effects on the mobility distribution of particles. The effects of residence time, RH (7%-70%), and SO(2) concentration (0.08-6.7 ppm) on ion-induced nucleation were also systematically investigated. The results show that the nucleation and growth were significantly dependent on the residence time, RH, and SO(2) concentration, which is in agreement with both a previous model and previous observations. This research will be inevitable for a better understanding of the role of ions in an atmospheric nucleation mechanism.