An optical and electrochemical sensor based on L-arginine functionalized reduced graphene oxide

A novel approach to predict the electrical conductivity of nanocomposites by a weak interphase around graphene network

Herein, we offer a model for estimating the tunneling conductivity of polymer-graphene nanocomposites based on interfacial properties, the proportion of networked graphene, and the wettability value between the polymer medium and the filler. The interfacial properties are influenced by the minimum diameter of the nanosheets (Dc), whose conductivity can be transferred to the medium via interfacial conduction (τ). These parameters impact the actual aspect ratio and the volume proportion of the filler, which, in turn, control the onset of percolation and the proportion of nanosheets in the network. We apply all these parameters to develop a novel model for estimating the conductivity of graphene systems. The predictions obtained from this model across different parameter ranges are discussed. Additionally, experimental measurements are employed to evaluate the proposed equations. High filler conductivity enhances the nanocomposite's conductivity by a strong interfacial conduction. However, the conductivity cannot be transferred to the polymer medium under condition of weak interfacial conduction. Furthermore, a robust interphase and a small Dc contribute to increased conductivity. Ultimately, the developed equations accurately predict the onset of percolation and conductivity, validated by real experimental data.

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.

Graphene Oxide Nanoparticles and Organoids: A Prospective Advanced Model for Pancreatic Cancer Research

Pancreatic cancer, notorious for its grim 10% five-year survival rate, poses significant clinical challenges, largely due to late-stage diagnosis and limited therapeutic options. This review delves into the generation of organoids, including those derived from resected tissues, biopsies, pluripotent stem cells, and adult stem cells, as well as the advancements in 3D printing. It explores the complexities of the tumor microenvironment, emphasizing culture media, the integration of non-neoplastic cells, and angiogenesis. Additionally, the review examines the multifaceted properties of graphene oxide (GO), such as its mechanical, thermal, electrical, chemical, and optical attributes, and their implications in cancer diagnostics and therapeutics. GO's unique properties facilitate its interaction with tumors, allowing targeted drug delivery and enhanced imaging for early detection and treatment. The integration of GO with 3D cultured organoid systems, particularly in pancreatic cancer research, is critically analyzed, highlighting current limitations and future potential. This innovative approach has the promise to transform personalized medicine, improve drug screening efficiency, and aid biomarker discovery in this aggressive disease. Through this review, we offer a balanced perspective on the advancements and future prospects in pancreatic cancer research, harnessing the potential of organoids and GO.

An experimental and computational study of graphene oxide functionalized with tris(hydroxymethyl)aminomethane as an electrode material for supercapacitors

In this research, graphene oxide (GO) functionalized with tris(hydroxymethyl)aminomethane (T) was synthesized with a simple one-pot method, and applied as an electrode material for supercapacitors. Electrochemical measurements on the synthesized tris(hydroxymethyl)aminomethane-functionalized graphene oxide (GO@T) indicated a specific capacitance of 549.8 F g- 1 at a specific current of 2.5 A g- 1 and a specific capacitance of 358 F g-1 at a specific current of 7 A g- 1 in the potential range of - 0.5-0.5 V versus Ag/AgCl. It also showed a high cyclic stability. According to the results, 80 and 68% of the initial capacitance was retained after 5500 and 9300 cycles, respectively. Density functional theory calculations were used to investigate the quantum capacitance, free energy change during functionalization reaction, and the layer distance of GO and GO@T.

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.

Sensitive and Selective Electrochemical Detection of Lead(II) Based on Waste-Biomass-Derived Carbon Quantum Dots@Zeolitic Imidazolate Framework-8

An electrochemical sensor based on carbon quantum dots (CQDs) and zeolitic imidazolate framework-8 (ZIF-8) composite was fabricated to detect lead(II). The CQDs (2.47 ± 0.52 nm) were synthesized from platanus acerifoli leaves by carbonization and the hydrothermal method. Under the optimal conditions, the fabricated electrochemical sensor had excellent performance in detecting Pb²⁺. The linear range for Pb²⁺ was 1 nM-1 μM, and the limit of detection (LOD) was 0.04 nM and the limit of quantification (LOQ) was 0.14 nM. Moreover, when the solution contained Pb²⁺ and Cd²⁺, the linear range for Pb²⁺ was 50 nM to 1 μM and the LOD was 0.02 nM. When the solution contained Pb²⁺ and Cu²⁺, the linear range for Pb²⁺ was 50 nM-750 nM and LOD was 0.07 nM. Furthermore, even if the solution contained Pb²⁺, Cd²⁺ and Cu²⁺, the linear range for Pb²⁺ was 50 nM-1 μM and the LOD was 0.04 nM. The X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectrometer (FTIR) and Brunauer-Emmet-Teller (BET) results indicated that the composite electrode materials had abundant oxygen-containing functional groups, a large specific surface area and pore structure, which are conducive to the adsorption of heavy metal ions and improve the detection performance.

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.

Determination of trace amount of iron cations using electrochemical methods at N, S doped GQD modified electrode

In this work, nitrogen and sulfur co-doped graphene quantum dot-modified glassy carbon electrodes (N, S-GQD/GCE) were used for the recognition of iron cations in aqueous solutions. The dissolved cations are detected based on the faradaic reduction or oxidation current of Fe(III) and Fe(II) obtained at the N, S-GQD/GCE surface. Cyclic voltammetry (CV), square wave voltammetry (SWV), and hydrodynamic amperometry are used as suitable electrochemical techniques for studying electrochemical behavior and determination of Fe cations. Based on the obtained results, it is concluded that the presence of free electrons in the structure of N, S-GQD could facilitate electron transfer reaction between Fe(III) and electrode surface which with increased surface area results in increased sensitivity and lower limit of detection. By performing suitable experiments, the best condition for preparing the modified electrode and determining Fe(III) was selected. Under optimized conditions, the amperometric response is linear from 1 to 100 nM of Fe(III) with a detection limit of 0.23 nM. The validity of the method and applicability of the sensor is successfully tested by the determination of Fe(III) in drug and water real samples. This sensor opened a new platform based on doped nanoparticles for highly sensitive and selective detection of analytes.