Recent Advances of Porous Graphene: Synthesis, Functionalization, and Electrochemical Applications

Rapid and unamplified identification of COVID-19 with morpholino-modified graphene field-effect transistor nanosensor

SARS-CoV-2 RNA is identified as a pivotal player to bolster energizing zones of COVID-19 detection. Herein, we develop a rapid and unamplified nanosensing platform for detection of SARS-CoV-2 RNA in human throat swab specimens. A gold nanoparticle (AuNP)-decorated graphene field-effect transistor (G-FET) sensor was fabricated, after which complementary phosphorodiamidate morpholino oligos (PMO) probe was immobilized on the AuNP surface. This sensor allowed for highly sensitive testing of SARS-CoV-2 RdRp as PMO does not have charges, leading to low background signal. Not only did the method present a low limit of detection in PBS (0.37 fM), throat swab (2.29 fM), and serum (3.99 fM), but also it achieved a rapid response to COVID-19 patients’ samples within 2 min. The developed nanosensor was capable of analyzing RNA extracts from 30 real clinical samples. The results show that the sensor could differentiate the healthy people from infected people, which are in high agreement with RT-PCR results (Kappa index = 0.92). Furthermore, a well-defined distinction between SARS-CoV-2 RdRp and SARS-CoV RdRp was also made. Therefore, we believe that this work provides a satisfactory, attractive option for COVID-19 diagnosis.

Functionalized Graphene Oxide-Reinforced Chitosan Hydrogel as Biomimetic Dressing for Wound Healing

A novel chitosan composite hydrogel by combining functionalized graphene oxide (CGO) is fabricated. The introduction of CGO significantly improves the mechanical property of CS hydrogel owing to the enhanced interaction between chitosan and CGO sheets. In comparison to the CS-GO composite hydrogel, the compressive stress of the CS-CGO composite hydrogel increases from 1.9 MPa at strain of 70.4% to 4.2 MPa at strain of 78.4%, the tensile stress and strain improve from 141.2 kPa and 134.6% to 300.2 kPa and 165.9%, respectively. An interconnected porous structure is formed in the CS-CGO composite hydrogel and the pore size decreases as the CGO loading increases, which is desirable in improving its mechanical property. Furthermore, the cytotoxicity tests indicate that the CS-CGO composite hydrogel possesses an excellent biocompatibility and can promote the adhesion and proliferation of fibroblasts. In vivo evaluation on full-thickness excision wounds in experimental rat shows that the CS-CGO composite hydrogel significantly accelerates wound healing, and the wound closure rate reaches up to 92.2% after 21 days. A feasible strategy to fabricate an enhanced chitosan composite hydrogel for application in wound healing is offered.

Performance Evaluation of Porous Graphene as Filter Media for the Removal of Pharmaceutical/Emerging Contaminants from Water and Wastewater

Graphene and its counterparts have been widely used for the removal of contaminants from (waste)water but with limited success for the removal of pharmaceutical contaminants. Driven by this need, this study reports, for the first time, the removal of pharmaceuticals from real contaminated water samples using porous graphene (PG) as a filter-based column. This work systematically evaluates the performance of PG as a filter medium for the removal of widely consumed pharmaceutical/emerging contaminants (ECs) such as atenolol, carbamazepine, ciprofloxacin, diclofenac, gemfibrozil and ibuprofen. Several factors were investigated in these column studies, including different reactive layer configurations, bed packing heights (5-45 mm), filter sizes (inner diameter 18-40 mm), adsorbent dosages (100-500 mg-PG) and water bodies (distilled water, greywater, and actual effluent wastewater). Sustainable synthesis of PG was carried out followed by its use as a filter medium for the removal of pharmaceuticals at high concentrations (10.5 ± 0.5 mg/L) and trace concentrations (1 mg/L). These findings revealed that the double-layered PG-sand column outperformed a PG single-layered configuration for the removal of most of the ECs. The removal efficiency of ECs from their solutions was improved by increasing PG dosages and filter bed height and size. Although the treatment of mixed pharmaceutical solutions from different water bodies was affected by the negative interference caused by competing water compounds, the treatment of ECs-contaminated greywater was not severely affected. Our findings suggest that PG, as a highly efficient filter medium, could be used for the removal of emerging pharmaceutical contaminants from water and wastewater.

Polyaniline/reduced graphene oxide foams as metal-free cathodes for stable lithium-oxygen batteries

Lithium-oxygen batteries (LOBs) are considered as next-generation energy storage devices owing to their high-energy densities, yet they generally suffer from low actual specific capacity and poor cycle performance. To solve these issues, a range of electrocatalysts have been introduced in the cathode to reduce the overpotential during charge/discharge cycles and minimize unwanted side reactions. Due to relative high costs and limited reserves of noble metals and their compounds, it is important to develop low-cost and efficient metal-free electrocatalysts. Here, we report a simple method to prepare three-dimensional porous polyaniline (PANI)/reduced graphene oxide foams (PPGFs) with different PANI contents via a two-step self-assembly process. When these foams are tested as the cathode in LOBs, the device using the PPGF with 50% PANI content exhibits a discharge capacity up to 36 010 mAh g^-1 and an excellent cycling stability (260 cycles at 1000 mAh g^-1 and 500 cycles at 500 mAh g^-1), provid ing new insights into the design of next-generation metal-free cathodes for LOBs.

From 2D Graphene Nanosheets to 3D Graphene-based Macrostructures

The combination of exceptional functionalities offered by 3D graphene-based macrostructures (GBMs) has attracted tremendous interest. 2D graphene nanosheets have a high chemical stability, high surface area and customizable porosity, which was extensively researched for a variety of applications including CO₂ adsorption, water treatment, batteries, sensors, catalysis, etc. Recently, 3D GBMs have been successfully achieved through few approaches, including direct and non-direct self-assembly methods. In this review, the possible routes used to prepare both 2D graphene and interconnected 3D GBMs are described and analyzed regarding the involved chemistry of each 2D/3D graphene system. Improvement of the accessible surface of 3D GBMs where the interface exchanges are occurring is of great importance. A better control of the chemical mechanisms involved in the self-assembly mechanism itself at the nanometer scale is certainly the key for a future research breakthrough regarding 3D GBMs.

A sulfur, nitrogen dual-doped porous graphene nanohybrid for ultraselective Hg(ii) separation over Pb(ii) and Cu(ii)

Two-dimensional (2D) porous graphene is attractive as a high-permeability membrane for ionic and molecular separation. Here, we propose a sulfur, nitrogen dual-doped 2D porous graphene (SNPG) nanohybrid by adopting a facile one-step process. The resulting sandwich-like porous nanohybrid features uniform ion-gated nanopores for efficient transport of target heavy metal ions while blocking undesired ions, as well as abundant multi-binding ligands for selectively chelating permeated heavy metal ions. We show from systematic experiments that this SNPG nanohybrid exhibits outstanding selectivity and ability to separate Hg(ii) ions in mixtures with eight other metal ions. An excellent uptake capability (803 mg g^-1) and high removal ability (>99%) within the entire pH range of 2-10 can be obtained. Given the specific 2D porous nanostructure and specific binding ligands, SNPG exhibits an ultrahigh separation factor towards Hg(ii) that is up to four orders of magnitude higher than those of Pb(ii), Cd(ii) and Cu(ii) ions, significantly higher than those of most reported adsorbents. These findings provide a new opportunity to develop selective materials and devices for applications such as efficient recognition, extraction and separation of target metal ions in complex aqueous environments.

A self-powered aptasensor using the capacitor-amplified signal of a photofuel cell and a portable digital multimeter readout

A self-powered aptasensor for streptomycin detection was constructed with a photofuel cell combined with a capacitor and a digital multimeter. The sensitivity of the proposed sensor was 8.7 times of that without using a capacitor amplifier circuit.

Intelligent Identification of MoS2 Nanostructures with Hyperspectral Imaging by 3D-CNN

Increasing attention has been paid to two-dimensional (2D) materials because of their superior performance and wafer-level synthesis methods. However, the large-area characterization, precision, intelligent automation, and high-efficiency detection of nanostructures for 2D materials have not yet reached an industrial level. Therefore, we use big data analysis and deep learning methods to develop a set of visible-light hyperspectral imaging technologies successfully for the automatic identification of few-layers MoS₂. For the classification algorithm, we propose deep neural network, one-dimensional (1D) convolutional neural network, and three-dimensional (3D) convolutional neural network (3D-CNN) models to explore the correlation between the accuracy of model recognition and the optical characteristics of few-layers MoS₂. The experimental results show that the 3D-CNN has better generalization capability than other classification models, and this model is applicable to the feature input of the spatial and spectral domains. Such a difference consists in previous versions of the present study without specific substrate, and images of different dynamic ranges on a section of the sample may be administered via the automatic shutter aperture. Therefore, adjusting the imaging quality under the same color contrast conditions is unnecessary, and the process of the conventional image is not used to achieve the maximum field of view recognition range of ~1.92 mm². The image resolution can reach ~100 nm and the detection time is 3 min per one image.

Functional nanostructure-loaded three-dimensional graphene foam as a non-enzymatic electrochemical sensor for reagentless glucose detection

Non-enzymatic and reagentless electrochemical sensors for convenient and sensitive detection of glucose are highly desirable for prevention, diagnosis and treatment of diabetes owing to their unique merits of simplicity and easy operation. Facile fabrication of a three-dimensional (3D) sensing interface with non-enzymatic recognition groups and an immobilized electrochemical probe remains challenge. Herein, a novel non-enzymatic electrochemical sensor was developed for the sensitive and reagentless detection of glucose by loading functional nanostructure on 3D graphene. Monolithic and macroporous 3D graphene (3DG) foam grown by chemical vapor deposition (CVD) served as the electrode scaffold. Prussian blue (PB) and gold nanoparticles (AuNPs) were first co-electrodeposited on 3DG (3DG/PB-AuNPs) as immobilized signal indicator and electron conductor. After a polydopamine (PDA) layer was introduced on 3DG/PB-AuNPs via facile self-polymerization of dopamine to stabilize internal PB probes and offer chemical reducibility, the second layer of AuNPs was in situ formed to assemble the recognition ligand, mercaptobenzoboric acid (MPBA). Owing to the high stability of PB and good affinity between MPBA and glucose, the non-enzymatic sensor was able to be used in reagentless detection of glucose with high selectivity, wide linear range (5 μM–65 μM) and low detection limit (1.5 μM). Furthermore, the sensor was used for the detection of glucose level in human serum samples.

A non-enzymatic electrochemical sensor was fabricated by loading functional nanostructure on three-dimensional graphene foam for reagentless detection of glucose with high sensitivity and stability.