Percolation onset and conductivity of nanocomposites assuming an incomplete dispersion of graphene nanosheets in a polymer matrix.

Herein, stacks of graphene nanosheets resulting from an incomplete dispersion of nanoparticles in polymer graphene nanocomposites are considered. The volume fraction, aspect ratio and conduction of stacks are expressed by the distance between nanosheets (s), thickness of an individual nanosheet (t), nanosheet diameter (D), thickness of the interphase zone (ti) and tunneling length (d). Moreover, the percolation onset, actual filler quantity and portion of networked nanosheets are stated by the stacks of nanosheets, interphase depth and tunneling length. Finally, an advanced model for the conductivity of a graphene-based system is presented using the mentioned terms. The influence of all properties of stacks, tunneling and interphase areas on the percolation onset, portion of percolated nanosheets and conductivity are examined. Furthermore, the tested values of conductivity are applied to confirm the predictability of the model. The larger quantity of thin sheets included in stacks produces a higher conductivity for samples. In addition, a thicker interphase and smaller tunnels can result in higher conductivity. The calculations of conductivity match the tested data at all filler amounts.

Effect of Atmospheric-Pressure Plasma Treatments on Fracture Toughness of Carbon Fibers-Reinforced Composites.

In this study, nano-scale fillers are added to epoxy matrix-based carbon fibers-reinforced composites (CFRPs) to improve the mechanical properties of multi-scale composites. Single-walled carbon nanotubes (SWCNTs) used as nano-scale fillers are treated with atmospheric-pressure plasma to introduce oxygen functional groups on the fillers' surface to increase the surface free energy and polar component, which relates to the mechanical properties of multi-scale composites. In addition, the effect of dispersibility was analyzed through the fracture surfaces of multi-scale composites containing atmospheric-pressure plasma-treated SWCNTs (P-SWCNTs) under high load conditions. The fillers content has an optimum weight percent load at 0.5 wt.% and the fracture toughness (KIC) method is used to demonstrate an improvement in mechanical properties. Here, KIC was calculated by three equations based on different models and we analyzed the correlation between mechanical properties and surface treatment. Compared to the composites of untreated SWCNTs, the KIC value is improved by 23.7%, suggesting improved mechanical properties by introducing selective functional groups through surface control technology to improve interfacial interactions within multi-scale composites.

Roles of metal/activated carbon hybridization on elemental mercury adsorption.

In this study, the elemental mercury removal behavior of metal (copper or nickel)/activated carbon hybrid materials were investigated. The pore structures and total pore volumes of the hybrid materials were analyzed using the N2/77 K adsorption isotherms. The microstructure and surface morphologies of the hybrid materials were characterized by X-ray diffraction and scanning electron microscopy, respectively. In the experimental results, the elemental mercury adsorption capacities of all copper/activated carbon hybrid materials were higher than that of the as-received material despite the decrease in specific surface areas and total pore volumes after the metal loading. All the samples containing the metal particles showed excellent elemental mercury adsorption. The Ni/ACs exhibited superior elemental mercury adsorption to those of Cu/ACs. This suggests that Ni/ACs have better elemental mercury adsorption due to the higher activity of nickel.

Assessment of electrical conductivity of polymer nanocomposites containing a deficient interphase around graphene nanosheet.

In this study, a poor/imperfect interphase is assumed to express the effective interphase thickness, operative filler concentration, percolation onset and volume share of network in graphene-polymer systems. Additionally, a conventional model is advanced by the mentioned terms for conductivity of samples by the extent of conduction transference between graphene and polymer medium. The model predictions are linked to the experimented data. Likewise, the mentioned terms as well as the conductivity of nanocomposites are expressed at dissimilar ranges of various factors. The novel equations successfully predict the percolation onset and conductivity in the samples containing a poor/imperfect interphase. Thin and long nanosheets with high conduction transportation desirably govern the percolation onset and nanocomposite conductivity, but a bigger tunneling distance causes a lower conductivity.

Influences of defective interphase and contact region among nanosheets on the electrical conductivity of polymer graphene nanocomposites.

In the current article, a defective interface is characterized by "Dc," representing the smallest diameter of nanosheets crucial for effective conduction transfer from the conductive filler to the medium, and by "ψ" as interfacial conduction. These parameters define the effective aspect ratio and operational volume fraction of graphene in the samples. The resistances of the graphene and polymer layer in contact zones are also considered to determine the contact resistance between adjacent nanosheets. Subsequently, a model for the tunneling conductivity of composites is proposed based on these concepts. This innovative model is validated by experimental data. Additionally, the effects of various factors on the conductivity of the composites and contact resistance are analyzed. Certain parameters such as filler concentration, graphene conductivity, interfacial conduction, and "Dc" do not affect the contact resistance due to the superconductivity of the nanosheets. However, factors like thin and large nanosheets, short tunneling distance (d), high interfacial conduction (ψ), low "Dc," and low tunnel resistivity (ρ) contribute to increased conductivity in nanocomposites. The maximum conductivity of 0.09 is obtained at d = 2 nm and ψ = 900 S/m, but d > 6 nm and ψ < 200 S/m produce an insulated sample. Additionally, the highest conductivity of 0.11 S/m is achieved with Dc = 100 nm and ρ = 100 Ω m, whereas the conductivity approaches 0 at Dc = 500 nm and ρ = 600 Ω m.

Zwitterions and betaines as highly soluble materials for sustainable corrosion protection: Interfacial chemistry and bonding with metal surfaces.

The primary requirements for interfacial adsorption and corrosion inhibition are solubility and the existence of polar functional groups, particularly charges. Traditional organic inhibitors have a solubility issue due to the hydrophobic moieties they incorporate. Most documented organic inhibitors have aromatic rings, hydrocarbon chains, and a few functional groups. The excellent solubility and high efficacy of zwitterions and betaines make them the perfect replacements for insoluble corrosion inhibitors. Zwitterions and betaines are more easily soluble because of interactions between their positive and negative charges (-COO-, -PO3-, -NH3, -NHR2, -NH2R, -SO3- etc.) and the polar solvents. The positive and negative charges also aid these molecules' physical and chemical adsorption at the metal-electrolyte interfaces. They develop a corrosion-inhibiting layer through their adsorption. After becoming adsorbed at the metal-electrolyte interface, they act as mixed-type inhibitors, slowing both cathodic and anodic processes. They usually adsorb according to the Langmuir adsorption isotherm. In this article, the corrosion inhibition potential of zwitterions and betaines in the aqueous phase, as well as their mode of action, are reviewed. This article details the advantages and disadvantages of utilizing zwitterions and betaines for sustainable corrosion protection.

Progressing of a power model for electrical conductivity of graphene-based composites.

This work presents a power equation for the conductivity of graphene-based polymer composites by the tunneling length, interphase deepness and filler size. The impressions of these factors on the effective concentration and percolation beginning of graphene nano-sheets in nanocomposites are also expressed. The developed equations for percolation beginning and conductivity are examined by the experimented data of some examples, which can guesstimate the interphase depth, tunneling size and percolation exponent. Besides, the impacts of numerous factors on the percolation beginning and conductivity are designed. The developed equation for percolation beginning shows the formation of thick interphase and large tunnels in the reported samples. So, disregarding of tunneling and interphase spaces in polymer graphene nanocomposites overpredicts the percolation beginning. Additionally, the developed model presents the acceptable calculations for the conductivity of samples. Among the mentioned parameters, the concentration and graphene conductivity in addition to the interphase depth induce the strongest effects on the conductivity of composites.

Simulation of electrical conductivity for polymer silver nanowires systems.

A simple model is developed for the conductivity of polymeric systems including silver nanowires (AgNWs). This model reveals the effects of interphase thickness, tunneling distance, waviness and aspect ratio of nanowires, as well as effective filler volume fraction on the percolation and electrical conductivity of AgNW-reinforced samples. The validity of this model is tested by using the measured data from several samples. Based on this model, the conductivity calculations are in proper accordance with the measured values. A large network and a low percolation onset are produced by nanowires with a high aspect ratio developing the nanocomposite conductivity. The results also show that a thicker interphase expands the network, thereby increasing the electrical conductivity. Furthermore, non-waved AgNWs exhibit more conductivity compared to wavy nanowires. It is concluded that the surface energies of polymer medium and nanowires have no effect on the conductivity of samples. On the other hand, the volume fraction and aspect ratio of nanowires, in addition to the interphase thickness and tunneling distance have the greatest influences on the conductivity of nanocomposites.

Rice husk valorization into sustainable Ni@TiO2/biochar nanocomposite for highly selective Pb (II) ions removal from an aqueous media.

Herein, we successfully prepared sustainable nanocomposites from agriculture waste (rice husk)-derived biochar precursor, and followed by nickel-doped, base-treated titanium dioxide nanomaterials loading for efficient lead (Pb2+) removal from aqueous media. By varying the loading contents of active materials, the optimized sample (Ni0.01@Na-TiO2/BC) possessed an efficient Pb2+ adsorption capability of 122.3 mg g-1 under the under optimum adsorption parameters, which is attributable to its specific surface area (138.09 m2 g-1) and excess functional sites. Kinetic and Isothermal examination illustrated that Pb2+ adsorption phenomena was well followed through pseudo 2nd order and Langmuir models. In addition, superior Pb2+ ions adsorption selectivity was recorded by optimized sample in a multi-metallic system over other existing ion (such as Cd2+, Mg2+, Ca2+, Cu2+, and Zn2+). Desorption experiments has been performed by using desorbing agent that demonstrates the good regeneration ability of sample. Hence, these findings provide new insight for the biowaste management by converting them into innovative adsorbents for commercial scale environmental remediation.

Development of Kovacs model for electrical conductivity of carbon nanofiber-polymer systems.

This study develops a model for electrical conductivity of polymer carbon nanofiber (CNF) nanocomposites (PCNFs), which includes two steps. In the first step, Kovacs model is developed to consider the CNF, interphase and tunneling regions as dissimilar zones in the system. In the second step, simple equations are expressed to estimate the resistances of interphase and tunnels, the volume fraction of CNF and percolation onset. Although some earlier models were proposed to predict the electrical conductivity of PCNFs, developing of Kovacs model causes a better understanding of the effects of main factors on the nanocomposite conductivity. The developed model is supported by logical influences of all factors on the conductivity and by experimented conductivity of several samples. The calculations show good accordance to the experimented data and all factors rationally manage the conductivity of PCNFs. The highest conductivity of PCNF is gained as 0.019 S/m at the lowest ranges of polymer tunnel resistivity (ρ = 500 Ω m) and tunneling distance (d = 2 nm), whereas the highest levels of these factors (ρ > 3000 Ω m and d > 6 nm) cannot cause a conductive sample. Also, high CNF volume fraction, poor waviness, long and thin CNF, low "k", thick interphase, high CNF conduction, high percentage of percolated CNFs, low percolation onset and high interphase conductivity cause an outstanding conductivity in PCNF.

Simulating of effective conductivity for grapheme-polymer nanocomposites.

The efficient conductivity of graphene-polymer systems is expressed supposing graphene, tunneling and interphase components. The volume shares and inherent resistances of the mentioned components are used to define the efficient conductivity. Besides, the percolation start and the share of graphene and interphase pieces in the nets are formulated by simple equations. Also, the resistances of tunneling and interphase parts are correlated to graphene conductivity and their specifications. Suitable arrangements among experimented data and model's estimates as well as the proper trends between efficient conductivity and model's parameters validate the correctness of the novel model. The calculations disclose that the efficient conductivity improves by low percolation level, dense interphase, short tunnel, large tunneling pieces and poor polymer tunnel resistivity. Furthermore, only the tunneling resistance can govern the electron transportation between nanosheets and efficient conductivity, while the big amounts of graphene and interphase conductivity cannot play a role in the efficient conductivity.

PEGylation of a shell over core-shell MOFs-a novel strategy for preventing agglomeration and synergism in terms of physicochemical and biological properties.

We demonstrate a new strategy of PEGylation over core-shell MOFs of HKUST-1 and Cu-MOF-2 by a solvothermal method. The novel synthesized PEGylated core-shell MOFs has synergistic enhancement in terms of physicochemical and biological properties. FTIR spectroscopy and XRD analysis described the bonding characteristics of the double-shelled-core MOFs PEG@HKUST-1@CuMOF-2 and PEG@CuMOF-2@HKUST-1. XPS and EDAX spectroscopy confirmed the structural features of the PEG@core-shell MOFs. The as-synthesized PEG-modified core-shell MOFs showed a readily identifiable morphology with a reduction in particle size. The significant observation from SEM and TEM was that agglomeration disappeared completely, and the morphology of individual core-shell MOFs was clearly revealed. BET analysis provided the surface characteristics of MOF compounds. The chemical states of frameworks were established by XPS. The designed PEG-modified copper MOFs were evaluated for their activity against Gram-positive (Staphylococcus aureus, Enterococcus faecalis), Gram-negative (Escherichia coli and Klebsiella pneumoniae) bacterial species and activity against fungal species (Aspergillus niger and Candida albicans). This research work highlights a facile and synergistic approach to design promising biocompatible nano-dimensional core-shell MOFs for biological applications.

Graphene-Based Electrochemical Biosensors for Breast Cancer Detection.

Breast cancer (BC) is the most common cancer in women, which is also the second most public cancer worldwide. When detected early, BC can be treated more easily and prevented from spreading beyond the breast. In recent years, various BC biosensor strategies have been studied, including optical, electrical, electrochemical, and mechanical biosensors. In particular, the high sensitivity and short detection time of electrochemical biosensors make them suitable for the recognition of BC biomarkers. Moreover, the sensitivity of the electrochemical biosensor can be increased by incorporating nanomaterials. In this respect, the outstanding mechanical and electrical performances of graphene have led to an increasingly intense study of graphene-based materials for BC electrochemical biosensors. Hence, the present review examines the latest advances in graphene-based electrochemical biosensors for BC biosensing. For each biosensor, the detection limit (LOD), linear range (LR), and diagnosis technique are analyzed. This is followed by a discussion of the prospects and current challenges, along with potential strategies for enhancing the performance of electrochemical biosensors.

Synthesis of Fe-Doped Peroxidase Mimetic Nanozymes from Natural Hemoglobin for Colorimetric Biosensing and In Vitro Anticancer Effects.

Despite their efficiency and specificity, the instability of natural enzymes in harsh conditions has inspired researchers to replace them with nanomaterials. In the present study, extracted hemoglobin from blood biowastes was hydrothermally converted to catalytically active carbon nanoparticles (BDNPs). Their application as nanozymes for the colorimetric biosensing of H2O2 and glucose and selective cancer cell-killing ability was demonstrated. Particles that were prepared at 100 °C (BDNP-100) showed the highest peroxidase mimetic activity, with Michaelis-Menten constants (Km) of 11.8 mM and 0.121 mM and maximum reaction rates (Vmax) of 8.56 × 10-8 mol L-1 s-1 and 0.538 × 10-8 mol L-1 s-1, for H2O2 and TMB, respectively. The cascade catalytic reactions, catalyzed by glucose oxidase and BDNP-100, served as the basis for the sensitive and selective colorimetric glucose determination. A linear range of 50-700 µM, a response time of 4 min, a limit of detection (3σ/N) of 40 µM, and a limit of quantification (10σ/N) of 134 µM was achieved. In addition, the reactive oxygen species (ROS)-generating ability of BDNP-100 was employed for evaluating its potential in cancer therapy. Human breast cancer cells (MCF-7), in the forms of monolayer cell cultures and 3D spheroids, were studied by MTT, apoptosis, and ROS assays. The in vitro cellular experiments showed dose-dependent cytotoxicity of BDNP-100 toward MCF-7 cells in the presence of 50 µM of exogenous H2O2. However, no obvious damage was induced to normal cells in the same experimental conditions, verifying the selective cancer cell-killing ability of BDNP-100.

Colors of graphene and graphene-oxide multilayers on various substrates.

We investigated the colors of graphene and graphene-oxide multilayers that were deposited on various dielectric layers. In particular, the effects of the material thickness, the types of dielectric layers, and the existence of a back silicon substrate were analyzed. The colors of graphene-oxide layers on a SiO2/Si substrate were found to periodically change as the material thickness increased. However, the colors of graphene layers on the same substrate became saturated without a similar periodic change. The calculated colors corresponding to the material thicknesses were verified by optical microscopy and profilometry. We believe that these results demonstrate the possibility of utilizing color as a simple tool for detecting and estimating the thicknesses of graphene and graphene-oxide multilayers.

Development of a model for modulus of polymer halloysite nanotube nanocomposites by the interphase zones around dispersed and networked nanotubes.

Theoretical studies on the mechanical properties of halloysite nanotube (HNT)-based nanocomposites have neglected the HNT network and interphase section, despite the fact that the network and interphase have significant stiffening efficiencies. In the present study, the advanced Takayanagi equation for determining the modulus of nanocomposites is further developed by considering the interphase zones around the dispersed and networked HNTs above percolation onset. Furthermore, simple equations are provided to determine the percolation onset of HNTs and the volume portions of HNTs and interphase section in the network. The experimental values obtained for many samples and the assessments of all relevant factors validate the proposed model. The high ranges of HNT concentration, interphase depth, HNT modulus, HNT length, network modulus, interphase modulus, interphase concentration, and network fraction enhance the system modulus. However, the low levels of HNT radius, percolation onset, and matrix modulus can intensify the reinforcing effect. Notably, the moduli of the dispersed HNTs and the surrounding interphase negligibly affect the modulus of the samples. Moreover, HNTs cannot reinforce the polymer medium when the HNT volume fraction is lower than 0.01 and the interphase depth is less than 5 nm.

Effect of contact resistance on the electrical conductivity of polymer graphene nanocomposites to optimize the biosensors detecting breast cancer cells.

This study focuses on the contact regions among neighboring nanoparticles in polymer graphene nanocomposites by the extension of nanosheets. The resistance of graphene and the contact zones represent the total resistance of the prolonged nanosheets. Furthermore, the graphene size, interphase depth, and tunneling distance express the effective volume portion of graphene, while the onset of percolation affects the fraction of percolated nanosheets. Finally, a model is developed to investigate the conductivity of the samples using the graphene size, interphase depth, and tunneling size. In addition to the roles played by certain factors in conductivity, the experimental conductivity data for several samples confirm the conductivity predictions. Generally, the polymer sheet in tunnels determines the total resistance of the extended nanosheets because graphene ordinarily exhibits negligible resistance. In addition, a large tunnel positively accelerates the onset of percolation, but increases the tunneling resistance and attenuates the conductivity of the nanocomposite. Further, a thicker interphase and lower percolation threshold promote the conductivity of the system. The developed model can be applied to optimize the biosensors detecting the breast cancer cells.

An innovative model for conductivity of graphene-based system by networked nano-sheets, interphase and tunneling zone.

This study presents a simple equation for the conductivity of graphene-filled nanocomposites by considering graphene size, amount of filler in the net, interphase deepness, tunneling size, and properties of the net. The amount of nanoparticles in the net is related to the percolation threshold and effective filler content. The novel model is analyzed using the measured conductivity of numerous examples and the factors' impacts on the conductivity. Both experienced data and parametric examinations verify the correctness of the novel model. Among the studied factors, filler amount and interphase deepness implicitly manage the conductivity from 0 to 7 S/m. It is explained that the interphase amount affects the operative quantity of nanofiller, percolation threshold, and amount of nets.

Modeling of Electrical Conductivity for Graphene-Filled Products Assuming Interphase, Tunneling Effect, and Filler Agglomeration Optimizing Breast Cancer Biosensors.

In this study, the percolation inception, actual filler amount, and concentration of nets are expressed using the filler size and agglomeration, interphase depth, and tunneling size. A modified form of the power-law model is recommended for the conductivity of graphene-polymer products using the mentioned characteristics. The modified model is used to plot and evaluate the conductivity at dissimilar ranges of factors. In addition, the prediction results of the model are compared with the experimented values of several samples. A low percolation inception and high-volume portion of nets that improve the conductivity of nanoparticles are achieved at a low agglomeration extent, thick interphase, large aspect ratio of the nanosheets, and large tunnels. The developed equation for percolation inception accurately predicts the results assuming tunneling and interphase parts. The innovative model predicts the conductivity for the samples, demonstrating good agreement with the experimented values. This model is appropriate to improve breast cancer biosensors, because conductivity plays a key role in sensing.

Effective Conductivity of Carbon-Nanotube-Filled Systems by Interfacial Conductivity to Optimize Breast Cancer Cell Sensors.

Interfacial conductivity and "Lc", i.e., the least carbon-nanotube (CNT) length required for the operative transfer of CNT conductivity to the insulated medium, were used to establish the most effective CNT concentration and portion of CNTs needed for a network structure in polymer CNT nanocomposites (PCNT). The mentioned parameters and tunneling effect define the effective conductivity of PCNT. The impact of the parameters on the beginning of percolation, the net concentration, and the effective conductivity of PCNT was investigated and the outputs were explained. Moreover, the calculations of the beginning of percolation and the conductivity demonstrate that the experimental results and the developed equations are in acceptable agreement. A small "Lc" and high interfacial conductivity affect the beginning of percolation, the fraction of networked CNTs, and the effective conductivity. Additionally, a low tunneling resistivity, a wide contact diameter, and small tunnels produce a highly effective conductivity. The developed model can be used to optimize breast cancer cell sensors.