Density Of Nanoparticles Research Articles

Nanolatex architectonics: Influence of cationic charge density and size on their adsorption onto surfaces with a 2D or 3D distribution of anionic groups

HypothesisIt is theoretically predicted and hypothesized that the charge density and size of spherical nanoparticles are the key factors for their adsorption onto oppositely charged surfaces. It is also hypothesized that the morphology and charge of the surface are of great importance. In-plane 2D (silica) or a volumetric 3D (regenerated TEMPO-oxidized cellulose model surfaces) distribution of charged groups is expected to influence charge compensation and, thus, the adsorption behavior. ExperimentsIn this work, self-stabilized nanolatexes with a range of cationic charge densities and sizes were synthesized through reversible addition − fragmentation chain-transfer (RAFT) polymerization coupled with polymerization-induced self-assembly (PISA). Their adsorption onto silica and anionic cellulose model surfaces was investigated using stagnation point adsorption reflectometry (SPAR) and quartz crystal microbalance with dissipation (QCM-D). FindingsExperiments and theory agree and show that the size of the nanolatex and the difference in charge density compared to the substrate determine the charge compensation and, thus, the surface coverage. Highly charged or large nanolatexes overcompensate the surface charge of non-porous substrates leading to a significant repulsive zone where other particles cannot adsorb. For porous substrates like cellulose, the vertical distribution of charged groups in the 3D volume prevents overcompensation and thus increases the adsorption. This systematic study investigates the isolated effect of surface charge and size and paves the way for on-demand particles specifically designed for a surface with particular characteristics.

Rigid POSS Nanofiller Regulated the Interfacial Self‐assembly of Particles into 2D Monolayer Superlattice with Fixed Interparticle Spacing†

Comprehensive SummaryPlasmonic superlattices of nanoparticles (NPs) possess unique “surface lattice resonances” properties that facilitates their wide applications in plasmonic sensing, photocatalysis, and nanoscale light manipulation. However, it is still challenging to manufacture superlattices with precisely controllable NPs distance and break the size limitation of NPs. Herein, we provided an effective strategy to construct NPs superlattices via shape‐persistent polyhedral oligosilsesquioxane (POSS) molecular nanoparticles govern interfacial assembly. As a nanoscale molecule with diameter of 1.5 nm, the POSS‐SH molecule provides sufficient rigid steric hindrance and hydrophobic effect for tailoring the uniformity and controllable distance between NPs in superlattices. Interestingly, synergistically with hydrophilic ligands of polyethylene glycol (PEG‐SH) with optimized ratio, the rigid POSS ligands can effectively regulate the distance between NPs in a fixed range of 2.3—2.8 nm, which is independent of ligands molecular weight and particle size. Furthermore, the effective approach can be universal to anisotropic NPs for manufacturing monolayer films with high NPs density. We believe this nanoscale molecule tailored interfacial self‐assembly strategy can effectively break the size of NPs and assembly obstacles for superlattice monolayer film. Additionally, the definite distance between NPs in superlattices can minimize optical energy attenuation and facilitates the applications such as surface‐enhanced Raman spectroscopy and photocatalysis.

Theoretical analysis of thermo-responsive behavior of microgels loaded with silver nanoparticles

Temperature is one of the extremely significant factors which is responsible for modifications mostly in physical but also in chemical properties of some particular matter. In present study, we carried out theoretical analysis of thermo-responsive behavior of Ag-p (N-isopropylmethacrylamide) hybrid microgels. We employed the classical Maxwellian distribution function with respect to the velocity of spherical silver nanoparticles inside the microgel’s network. The behavior of distribution function relative to the temperature represents that the distribution function still obeys the Maxwell-Boltzmann law even at low temperatures. Further, the diffusion coefficient, probability density, surface tension and surface energy of silver nano-particles are numerically evaluated and then graphically illustrated.

Dense Platinum/Nickel Oxide Heterointerfaces with Abundant Oxygen Vacancies Enable Ampere‐Level Current Density Ultrastable Hydrogen Evolution in Alkaline

AbstractPlatinum (Pt) remains the benchmark electrocatalyst for alkaline hydrogen evolution reaction (HER), but its industry‐scale hydrogen production is severely hampered by the lack of well‐designed durable Pt‐based materials that can operate at ampere‐level current densities. Herein, based on the original oxide layer and parallel convex structure on the surface of nickel foam (NF), a 3D quasi‐parallel architecture consisting of dense Pt nanoparticles (NPs) immobilized oxygen vacancy‐rich NiOx heterojunctions (Pt/NiOx‐OV) as an alkaline HER catalyst is developed. A combined experimental and theoretical studies manifest that anchoring Pt NPs on NiOx‐OV leads to electron‐rich Pt species with altered density of states (DOS) distribution, which can efficiently optimize the d‐band center and the adsorption of reaction intermediates as well as enhance the water dissociation ability. The as‐prepared catalyst exhibits extraordinary HER performance with a low overpotential of 19.4 mV at 10 mA cm−2, a mass activity 16.3‐fold higher than that of 20% Pt/C, and a long durability of more than 100 h at 1000 mA cm−2. Furthermore, the assembled alkaline electrolyzer combined with NiFe‐layered double hydroxide requires extremely low voltage of 1.776 V to attain 1000 mA cm−2, and can operate stably for more than 400 h, which is rarely achieved.

Stereolithography of Semiconductor Silver and Acrylic-Based Nanocomposites.

Polymer nanocomposites (PNCs) attract the attention of researchers and industry because of their potential properties in widespread fields. Specifically, electrically conductive and semiconductor PNCs are gaining interest as promising materials for biomedical, optoelectronic and sensing applications, among others. Here, metallic nanoparticles (NPs) are extensively used as nanoadditives to increase the electrical conductivity of mere acrylic resin. As the in situ formation of metallic NPs within the acrylic matrix is hindered by the solubility of the NP precursors, we propose a method to increase the density of Ag NPs by using different intermediate solvents, allowing preparation of Ag/acrylic resin nanocomposites with improved electrical behaviour. We fabricated 3D structures using stereolithography (SLA) by dissolving different quantities of metal precursor (AgClO4) in methanol and in N,N-dimethylformamide (DMF) and adding these solutions to the acrylic resin. The high density of Ag NPs obtained notably increases the electrical conductivity of the nanocomposites, reaching the semiconductor regime. We analysed the effect of the auxiliary solvents during the printing process and the implications on the mechanical properties and the degree of cure of the fabricated nanocomposites. The good quality of the materials prepared by this method turn these nanocomposites into promising candidates for electronic applications.

Comprehensive examination of radiative electromagnetic flowing of nanofluids with viscous dissipation effect over a vertical accelerated plate

This research aims to establish the MHD radiating convective nanofluid flow properties with the viscous dissipation across an exponentially accelerating vertical plate. As the plate accelerates, its temperature progressively increases. There are two separate types of water-based nanofluids that include copper (Cu) and titanium dioxide (Ti{O}_{2}) nanoparticles, respectively. The most crucial aspect of this investigation is finding a closed-form solution to a nonlinear coupled partial differential equations scheme. Galerkin finite element method (G-FEM) is used to figure out the initial managing equations. Utilizing graphs, the effect of the flow phenomenon's contributing variables as well as the influence of other factors is determined and depicted. In the part dedicated to the findings and discussion, the properties of these emergent parameters are described in more depth. Nonetheless, the thermal radiation and heat sink factors increase the thermal profile. In addition, the greater density of the copper nanoparticles cause the nanoparticle volume fraction to lessen the velocity delineation.

Controlled fiber core mode and surface mode interaction for enhanced SERS performance.

Three-dimensional surface-enhanced Raman scattering (SERS) platform based on microstructure fibers has many advantages for rapid liquid detection due to its microfluidic channels and light guidance. The fiber mode field distribution determines the light-analyte interaction strength but has rarely been studied in SERS applications. In this paper, we numerically and experimentally investigate the mode field distribution in suspended-core fibers decorated with gold nanoparticles. The interaction between the core mode and surface mode is controlled by changing the density of gold nanoparticles on the inner surface. The avoided crossing wavelength shifts linearly to red with the decrease of the nanoparticle spacing. With an optimized nanoparticle spacing of 20 nm, the avoided crossing occurs near the laser wavelength of 633 nm, which greatly increases the power ratio in the liquid channels and hence improves the SERS performance. The detection limit for crystal violet was 10-9 M, and the enhancement factor was 108. The avoided crossing mechanism can be applied to all fiber SERS probes for sensitivity improvement.

A reaction mechanism for plasma electrolysis of AgNO3 forming silver nanoclusters and nanoparticles

In plasma-driven solution electrolysis (PDSE), gas-phase plasma-produced species interact with an electrolytic solution to produce, for example, nanoparticles. An atmospheric pressure plasma jet (APPJ) directed onto a liquid solution containing a metallic salt will promote reduction of metallic ions in solution, generating metallic clusters that nucleate to form nanoparticles. In this article, results from a computational investigation are discussed of a PDSE process in which a radio-frequency APPJ sustained in helium impinges on a silver nitrate solution, resulting in growth of silver nanoparticles. A reaction mechanism was developed and implemented in a global plasma chemistry model to predict nanoparticle growth. To develop the reaction mechanism, density functional theory was used to generate probable silver growth pathways up to Ag9. Neutral clusters larger than Ag9 were classified as nanoparticles. Kinetic reaction rate coefficients for thermodynamically favorable growth pathways were estimated based on an existing, empirically determined base reaction mechanism for smaller Ag particle interactions. These rates were used in conjunction with diffusion-controlled reaction rate coefficients that were calculated for other Ag species. The role of anions in reduction of Agn ions in forming nanoparticles is also discussed. Oxygen containing impurities or admixtures to the helium, air entrainment into the APPJ, and dissociation of saturated water vapor above the solution can produce additional reactive oxygen species in solution, resulting in the production of anions and O2− in particular. For a given molarity, delivering a sufficient fluence of reducing species will produce similar nanoparticle densities and sizes for all applied power levels. Comparisons are made to alternate models for nanoparticle formation, including charged nanoparticles and use of direct current plasmas.

One-Pot Synthesis of Aminated Bimodal Mesoporous Silica Nanoparticles as Silver-Embedded Antibacterial Nanocarriers and CO2 Capture Sorbents.

Mesoporous silica nanoparticles have highly versatile structural properties that are suitable for a plethora of applications including catalysis, separation, and nanotherapeutics. We report a one-pot synthesis strategy that generates bimodal mesoporous silica nanoparticles via coassembly of a structure-directing Gemini surfactant (C16-3-16) with a tetraethoxysilane/(3-aminopropyl)triethoxysilane-derived sol additive. Synthesis temperature enables control of the nanoparticle shape, structure, and mesopore architecture. Variations of the aminosilane/alkylsilane molar ratio further enable programmable adjustments of hollow to core-shell and dense nanoparticle morphologies, bimodal pore sizes, and surface chemistries. The resulting Gemini-directed aminated mesoporous silica nanoparticles have excellent carbon dioxide adsorption capacities and antimicrobial properties against Escherichia coli. Our results provide an enhanced understanding of the structure formation of multiscale mesoporous inorganic materials that are desirable for numerous applications such as carbon sequestration, water remediation, and biomedical-related applications.

Synergistic effect of CuxOy-NPs/TiO2-NTs heterostructure on the photodegradation of amido black staining

TiO2 nanotube arrays have been extensively investigated for optoelectronic applications besides their excellent photocatalytic activity. The present work aims to study TiO2 nanotubes (NTs) fabricated with the anodization of Ti substrates. CuxOy nanoparticles (NPs) were deposited on TiO2 nanotubes (NTs) using the Successive ionic layer adsorption and reaction (SILAR) method. The obtained nanocomposite was used in the photocatalytic degradation of synthetic organic pollutants (amido black). Particular emphasis was focused on the effect of the morphological, optical, and structural properties of the CuxOy (i.e., the number of SILAR cycles) on the photocatalytic efficiency of the formed heterostructure. The morphological structural and optical properties were investigated by scanning and transmission electron microscopy (SEM and TEM), X-ray diffraction analysis (XRD), UV–vis spectroscopy, and photoluminescence (PL), respectively. The results show the formation of TiO2 anatase NTs decorated with CuO and Cu2O NPs. The density and size of the Cu2O NPs were found to increase with the number of SILAR cycles, whereas the energy band gap is narrowed by 0.7 eV. Photocatalytic tests demonstrate that at 15 SILAR cycles, the Cu2O.CuO-NPs/TiO2-NTs heterostructure exhibits the highest photocatalytic performance, reaching 94 % of amido black degradation under UV irradiation (256 nm) for 90 min. The experimental data modeling indicates that the faster the degradation, the better the photocatalytic performance; i.e., the reaction rate k = 0.025 min-1. The current study highlights the potential of CuxOy decorated TiO2 substrates as efficient photocatalytic systems for degrading hazardous organic pollutants.

Achieving enhanced mechanical properties of SiC/Al–Cu nanocomposites via simultaneous solid-state alloying of Cu and dispersing of SiC nanoparticles

In this study, SiC/Al–Cu nanocomposites were fabricated via simultaneous solid-state alloying of Cu and dispersing of SiC nanoparticles in Al matrix, for the first time. We showed that a complete solid solution of Cu (4.5 wt%) and a uniform dispersion of dense (up to 15 vol%) SiC nanoparticles in Al matrix can be successfully achieved, by accumulative roll-bonding (ARB) of pure Al, Cu and SiC nanoparticles to high cycles. The as-prepared SiC/Al–Cu nanocomposites exhibited excellent mechanical properties at room temperature and at 300 °C. The microhardness and tensile strength of the SiC/Al–Cu nanocomposites were both significantly increased by incorporating Cu solutes and dense SiC nanoparticles, surpassing those of other Al–Cu matrix composites. The SiC/Al–Cu nanocomposites also exhibited enhanced high-temperature creep resistance with the increase of SiC content. The synergy of solid-solution strengthening and Orowan strengthening was responsible for the excellent mechanical properties of the nanocomposites. Our study indicated the high efficiency of the ARB-induced matrix alloying and dispersing of nanoparticles for optimizing the microstructure and mechanical performances of Al matrix composites.

Discerning the Contributions of Gold Species in Butadiene Hydrogenation: From Single Atoms to Nanoparticles.

Identification of the roles of different active sites is vital for the rational design of catalysts. We present a cutting-edge strategy to discern the contributions of different single-atom gold species and nanoparticles in 1,3-butadiene hydrogenation, through coupling of advanced spectroscopic techniques, electron microscopy-based automated image analyses, and steady-state and kinetic studies. While all the carbon-hosted single gold atoms display negligible initial activity, the in situ-evolved gold nanoparticles are highly active. Full metal-species quantification is realized by combining electron-microscopy-based atom recognition statistics and deep-learning-driven nanoparticle segmentation algorithm, allowing the structure-activity correlations for the hybrid catalysts containing different Au architectures to be established. Surface exposure density of Au nanoparticles, as revealed by electron-microscopy-based statistics, is revealed as a new and reliable activity descriptor.

Discerning the Contributions of Gold Species in Butadiene Hydrogenation: From Single Atoms to Nanoparticles

AbstractIdentification of the roles of different active sites is vital for the rational design of catalysts. We present a cutting‐edge strategy to discern the contributions of different single‐atom gold species and nanoparticles in 1,3‐butadiene hydrogenation, through coupling of advanced spectroscopic techniques, electron microscopy‐based automated image analyses, and steady‐state and kinetic studies. While all the carbon‐hosted single gold atoms display negligible initial activity, the in situ‐evolved gold nanoparticles are highly active. Full metal‐species quantification is realized by combining electron‐microscopy‐based atom recognition statistics and deep‐learning‐driven nanoparticle segmentation algorithm, allowing the structure–activity correlations for the hybrid catalysts containing different Au architectures to be established. Surface exposure density of Au nanoparticles, as revealed by electron‐microscopy‐based statistics, is revealed as a new and reliable activity descriptor.

Intelligent PM 2.5 mass concentration analyzer using deep learning algorithm and improved density measurement chip for high-accuracy airborne particle sensor network

The concentration of ultrafine airborne particles is continuously increasing, and research has shown that it has adverse effects when inhaled into the human body. Accordingly, there is a growing demand for a measurement network utilizing sensors to evaluate an individual's exposure to these airborne particles. However, the current low-cost sensors have the limitation of low accuracy. To solve this, we devised a method to calibrate a low-cost mass concentration sensor accurately in real time. In a previous study, we developed an analyzer that could measure the effective density and nanoparticles that cause the low accuracy of the mass concentration sensor. However, it had a hardware stability problem when used to monitor the outside air for a long period. This has been improved by modifying the shape of the Micro-electromechanical system (MEMS)-based chip integrated with the electrical/inertial analysis technology. Hence, the device can now operate with sufficient stability in the outdoor air. In addition, the retrieval algorithm used to convert the measured current values into the effective density and nanoparticle size distribution was prone to errors. It was modified to a deep learning-based physical parameter conversion algorithm to minimize the errors. Thus, we developed a standalone analyzer that integrates the improved nanoparticle and effective density analyzer, temperature and humidity sensor, and low-cost mass concentration sensor. In addition, we developed a technique to calibrate the mass concentration sensor data accurately in real time based on the data measured by each sensor and analyzer and a deep learning-based mass concentration calibration algorithm. The calibration precision was confirmed through comparative evaluation of the analyzer with the results of a beta attenuation mass monitor. In the future, this analyzer can be utilized to build a sensor network that precisely monitors the mass concentration in a large area through multi-point deployment. Alternatively, it can be used for monitoring the mass concentration in an indoor private space, where it is difficult to place expensive and large equipment.

Assessing comparable bioconcentration potentials for nanoparticles in aquatic organisms via combined utilization of machine learning and toxicokinetic models

AbstractThe toxicokinetic (TK) model‐derived kinetic bioconcentration factor (BCFk) provides a quantitatively comparable index to estimate the bioaccumulation potential of nanoparticles (NPs) that barely reach thermodynamic equilibrium in aquatic organisms, but experimental data are limited for various NPs. In the present study, a machine learning model was applied to offer reliable in silico predictions for the dynamic body burden of diverse NPs to derive corresponding parameters for the TK model. The developed eXtreme Gradient Boosting‐derived TK (XGB‐TK) model was applied to predict BCFk results for a broad range of metallic or carbonaceous NPs, with an appreciable prediction R2 of 0.96. The BCFk values were predicted based on a random combination of selected variable features, revealing that their bioaccumulation potential showed an overall negative correlation with NP density or organism size. By applying importance analysis and partial dependence plots, NP density and organism size were revealed to be the top essential features that impact the bioaccumulation potential. The conjunctively used XGB‐TK model enabled a prior comparison for diverse NPs and straightforward derivation on the dependency of features, which could also guide the bioaccumulation mechanism exploration and experimental condition formulation.

Tuning the Packing Density of Gold Nanoparticles in Peptoid Nanosheets Prepared at the Oil-Water Interface.

Two-dimensional (2D) arrays of gold nanoparticles that can freely float in water are promising materials for solution-based plasmonic applications like sensing. To be effective sensors, it is critical to control the interparticle gap distance and thus the plasmonic properties of the 2D arrays. Here, we demonstrate excellent control over the interparticle gap distance in a family of freely floating gold nanoparticle-embedded peptoid nanosheets. Nanosheets are made via monolayer assembly and collapse at the oil-water interface, allowing for fine control over the solution nanoparticle concentration during assembly. We used surface pressure measurements to monitor the assembly of the peptoid, nanoparticle, and combined system at the oil-water interface to determine a workable range of nanosheet assembly conditions suitable for controlling the interparticle gap distances within the nanosheets. These measurements revealed that the extent of nanoparticle adsorption to the peptoid monolayer can be tuned by varying the bulk nanoparticle concentration, but the ability for the monolayer to collapse into nanosheets is compromised at high nanoparticle concentrations. Peptoid nanosheets were synthesized with varying bulk nanoparticle concentrations and analyzed using light microscopy and UV-visible spectroscopy. Based on the spectral shift of the localized surface plasmon resonance peaks for the nanoparticles in the nanosheets relative to those well dispersed in toluene, we estimate that we can access interparticle gap distances within the nanosheet interior between 2.9 ± 0.5 and 9 ± 2 nm. Our results suggest that the minimum interparticle distance achievable by this method is limited by the nanoparticle ligand length, and so has the potential to be further tuned by varying the ligand chemical structure. The ability to quantitatively control and monitor the assembly conditions by this method provide an opportunity to readily tune the optoelectronic properties of this new class of 2D nanomaterial, making it a promising platform for plasmonic-based sensing applications.

Functionalized silica nanoparticles coupled with nanoporous membrane for efficient ionic current rectification

In the last few decades, tremendous effort has been dedicated to mimicking the efficient ionic current rectification (ICR) of biological nanopores. Nanoporous membranes and singular nanopores with ICR functionality have been fabricated using advanced, yet costly technologies. We herein demonstrate that a simple, novel, and robust ICR platform can be constructed using 80 nm silica nanoparticles and a piece of 15 nm track-etched polycarbonate membrane. Efficient ICR can be obtained when voltages of different polarities are applied across the membrane, due to the asymmetric electrophoretic migration of silica nanoparticles whose surfaces are modified with different functional groups. The effect of pore size, ionic strength, pH, voltage magnitude, and density of silica nanoparticles on the efficiency of the ICR system has been systematically investigated in this report. Our results clearly show that smaller pore, lower ionic strength, appropriate pH value, higher electrical field strength, lower density of silica nanoparticles can generally enhance the efficiency of the ICR system. The principles of this new ICR system may find many potential applications in controllable drug delivery, energy storage and water purification.

Enhanced Detection of Surface Plasmon Resonance Microscope via Multiangle Illumination and Pretrained Deep Neural Network

Surface plasmon resonance microscopy (SPRM) has proved to be a powerful tool for label‐free imaging, detection, and mass/size measurement of single isolated virus, cells, and nanoparticles. Herein, SPRM that can be extended to detect the dense nanoparticles, such as counting the number density of nanoparticles, is demonstrated. To improve the counting accuracy, multiangle illumination is adopted in a conventional SPRM, then a deep learning method based on pretrained neural network approach is proposed to deal with the large number of SPRM images. Numerical simulations show that the new approach can significantly enhance the accuracy in detecting the number densities of nanoparticles. The ability to precisely count the number of particles is important for the monitor and detection of environmental pollution because that the number density of the particulate matter is strongly related to the air quality and atmospheric visibility.

(Digital Presentation) Charge Trapping in TiO2 Nanoparticles

TiO2 is a common component of electrode surfaces and photocatalytic systems. Depending on the preparation, the oxide can be formed in different shapes and morphology which can effect the selectivity and efficiency of the electrode [1]. Since the morphology and shape of the nanoparticles can be easily tuned with experimental conditions, this may offer a new, unexplored strategy to improve electrode properties in these systems. The goal of our computational studies is to understand better how charge carriers behave in these oxide nanoparticles.We studied the electronic states around the bandgap in anatase-type TiO2 nanoparticles of different shapes with (101) and (001) facets using Density Functional based Tight Binding (DFTB) and DFT methods. The previous calculation has already shown that most orbitals of the valence and conduction bands form delocalized band-like orbitals; however, there are a significant number of localized states at the lower edge of the conduction band, too. The calculations clearly show the position of these trapping sites when we compare the electron density of neutral and charged nanoparticles. Reference: [1] Selectivity enhancement in the electrochemical reduction of oxalic acid over titanium dioxide nanoparticles achieved by shape and energy-state control. H Eguchi, K Kato, G Juhasz, M Yamauchi - Catalysis Science & Technology, 2021Figure 1. The electron density difference between negatively charged and neutral nanoparticle (left); electron density difference reflected in Mulliken charges of the atoms (right). Figure 1

Metal Oxide Inclusion in Polycrystalline Pt-Based Electrocatalyst for an Ammonia Oxidation Reaction

In April 2021, an average daily amount of 421 ppm of carbon dioxide (CO2) was recorded in the atmosphere, CO2 has increased more than 100 ppm in the last 66 years. Fuel cells are a suitable option for addressing the major issues of meeting current and future energy demand while also reducing CO2 pollution. If the fuel source originates from renewable sources, this makes the overall process CO2-neutral. In the quest for sustainable fuels with high energy density, focus is currently on nitrogen-containing fuels, such as ammonia (NH3) since their oxidation will yield predominantly dinitrogen (N2). Different platinum precursors (K2PtCl6 and H2Pt(OH)6) were used to study the effect on the electrocatalytic activity of Pt for an ammonia oxidation reaction when different metal oxides are added to Pt particles. The metal oxides chosen are CeO2, Fe2O3, and Ag2O. The morphology and element distribution of the Pt catalyst was determined by SEM equipped with an EDS detector. The crystalline phase present in the samples was determined by X-ray diffraction. Cyclic voltammetry was used to characterize the particles and to study the catalytic activity of Pt electrocatalyst for ammonia oxidation. No significant difference was found (by student t-test) between the onset potential for ammonia electrooxidation of Pt particles and Pt precursor. Average onset potential for ammonia electrooxidation was -0.464 V vs Ag|AgCl. The highest average current peak densities were reported by catalyst from non-chlorinated precursor with addition of Fe2O3. Current densities of nanoparticles synthesized from the H2Pt(OH)6 precursor decreased to 24.9 x 10-2 mA/cm-2 with CeO2 addition, but increased to 28.2 x 10-2 mA/cm-2 when Fe2O3 was added. We are currently working on the electrochemical characterization and oxidation of ammonia with catalyst from Ag2O addition