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.

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.

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.

Predicting of tunneling resistivity between adjacent nanosheets in graphene-polymer systems.

In this work, the tunneling resistivity between neighboring nanosheets in grapheme-polymer nanocomposites is expressed by a simple equation as a function of the characteristics of graphene and tunnels. This expression is obtained by connecting two advanced models for the conductivity of graphene-filled materials reflecting tunneling role and interphase area. The predictions of the applied models are linked to the tested data of several samples. The impressions of all factors on the tunneling resistivity are evaluated and interpreted using the suggested equation. The calculations of tunneling resistivity for the studied examples by the model and suggested equation demonstrate the same levels, which confirm the presented methodology. The results indicate that the tunneling resistivity decreases by super-conductive graphene, small tunneling width, numerous contacts among nanosheets and short tunneling length.

Effect of contact number among graphene nanosheets on the conductivities of tunnels and polymer composites.

Simple equations are expressed for tunnel conductivity, tunnel resistance and conductivity of a graphene-filled composite by the number of contacts and interphase part. More specially, the active filler amount is suggested by interphase depth, which changes the contact number. The conductivity of nanocomposite is presented by filler content, filler dimensions, tunneling length and interphase depth. The innovative model is surveyed by the experimented conductivity of real examples. Too, the impacts of numerous issues on the tunnel resistance, tunnel conductivity and conductivity of nanocomposite are discussed to validate the novel equations. The estimates agree with the experimented data and the impacts of several terms on the tunnel resistance, tunnel conductivity and conductivity of system are sensible. Thin and big nanosheets positively affect the nanocomposite's conductivity, but thick nanosheets improve the tunnel conductivity. High conductivity is found at short tunnels, while the nanocomposite's conductivity directly depends on the tunneling length. The dissimilar effects of these features on the tunneling properties and conductivity are described.

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.

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.

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.

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.

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.

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.

A Review on Non-Enzymatic Electrochemical Biosensors of Glucose Using Carbon Nanofiber Nanocomposites.

Diabetes mellitus has become a worldwide epidemic, and it is expected to become the seventh leading cause of death by 2030. In response to the increasing number of diabetes patients worldwide, glucose biosensors with high sensitivity and selectivity have been developed for rapid detection. The selectivity, high sensitivity, simplicity, and quick response of electrochemical biosensors have made them a popular choice in recent years. This review summarizes the recent developments in electrodes for non-enzymatic glucose detection using carbon nanofiber (CNF)-based nanocomposites. The electrochemical performance and limitations of enzymatic and non-enzymatic glucose biosensors are reviewed. Then, the recent developments in non-enzymatic glucose biosensors using CNF composites are discussed. The final section of the review provides a summary of the challenges and perspectives, for progress in non-enzymatic glucose biosensors.

Electrochemical biosensors based on polymer nanocomposites for detecting breast cancer: Recent progress and future prospects.

In recent years, the second-highest mortality rate of any cancer worldwide has been breast cancer (BC), which is one of the three most common and deadly cancers among women. Detecting BC in its earliest stages facilitates treatment, reduces death risk, and improves survival rates for patients. Biosensors have garnered considerable attention in recent years for the early diagnosis of BC owing to their high sensitivity, simplicity, low cost, and low detection limit compared to other diagnostic technologies. Polymer nanocomposites (PNCs) containing conductive nanoparticles (NPs) have been used as electrochemical biosensors. In this study, we review recent developments in electrochemical biosensors based on PNCs containing metallic NPs and carbon nanomaterials (carbon nanotubes and graphene) for the diagnosis of biomarkers associated with BC. The key performance characteristics of electrochemical biosensors, such as linear detection range, limit of detection (LOD), and response time, along with the numerous BC biomarkers and various NPs for the fabrication of biosensors are discussed. Additionally, future challenges of electrochemical biosensors and hindrances to their use in clinical practice are explored.

Modeling of Electrical Conductivity for Polymer-Carbon Nanofiber Systems.

There is not a simple model for predicting the electrical conductivity of carbon nanofiber (CNF)-polymer composites. In this manuscript, a model is proposed to predict the conductivity of CNF-filled composites. The developed model assumes the roles of CNF volume fraction, CNF dimensions, percolation onset, interphase thickness, CNF waviness, tunneling length among nanoparticles, and the fraction of the networked CNF. The outputs of the developed model correctly agree with the experimentally measured conductivity of several samples. Additionally, parametric analyses confirm the acceptable impacts of main factors on the conductivity of composites. A higher conductivity is achieved by smaller waviness and lower radius of CNFs, lower percolation onset, less tunnel distance, and higher levels of interphase depth and fraction of percolated CNFs in the nanocomposite. The maximum conductivity is obtained at 2.37 S/m by the highest volume fraction and length of CNFs.

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.

Advancement of the Power-Law Model and Its Percolation Exponent for the Electrical Conductivity of a Graphene-Containing System as a Component in the Biosensing of Breast Cancer.

The power-law model for composite conductivity is expanded for graphene-based samples using the effects of interphase, tunnels and net on the effective filler fraction, percolation start and "b" exponent. In fact, filler dimensions, interphase thickness, tunneling distance and net dimension/density express the effective filler fraction, percolation start and "b" exponent. The developed equations are assessed by experimented values from previous works. Additionally, the effects of all parameters on "b" exponent and conductivity are analyzed. The experimented quantities of percolation start and conductivity confirm the predictability of the expressed equations. Thick interphase, large tunneling distance, high aspect ratio and big nets as well as skinny and large graphene nano-sheets produce a low "b" and a high conductivity, because they improve the conduction efficiency of graphene nets in the system. Graphene-filled nanocomposites can be applied in the biosensing of breast cancer cells and thus the developed model can help optimize the performance of biosensors.

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.

Tensile Modulus of Polymer Halloysite Nanotube Systems Containing Filler-Interphase Networks for Biomedical Requests.

To date, there have been a limited number of studies modeling the tensile modulus in the polymer halloysite nanotube (HNT) systems before or after percolation onset. In this paper, an equation for a composite's modulus post-percolation onset was developed for HNT-filled samples including the interphase and HNT network. The dispersed nanoparticles and adjoining interphase part were neglected, because they caused ineffective influences on the stiffness of the system after percolation onset. The developed model reflects the impacts of HNTs' size, interphase depth, percolation onset and the volume shares and moduli of the HNT network and its adjacent interphase on the modulus of HNT-based systems. The impacts of issues on the nanocomposite modulus are defendable, confirming the effectiveness of the developed model. HNT length, interphase depth, HNT concentration, net modulus and net portion directly influenced the stiffness, while the HNT radius and percolation onset had inverse effects. Results show that there was a 142% improvement in the modulus of samples at an interphase depth of 40 nm. Moreover, the stiffness improved by 60% at a net modulus of 200 GPa, but it later exhibited a 180% enhancement at a net modulus of 1000 GPa. In addition, the experimental data for the modulus of numerous composites display fine agreement to the predictions, confirming the validity of the developed model.