Graphene oxide nanoribbon-based sensors for the simultaneous bio-electrochemical enantiomeric resolution and analysis of amino acid biomarkers

Smartphone-assisted electrochemical sensor for reliable detection of tyrosine in serum

Point-of-care devices have attracted a huge interest by the scientific community because of the valuable potentiality for rapid diagnosis and precision medicine through cost-effective and easy-to-use devices for on-site measurement by unskilled personnel. Herein, we reported a smartphone-assisted electrochemical device consisted of a screen-printed electrode modified with carbon black nanomaterial and a commercially available smartphone potentiostat i.e. EmStat3 Blue, for sensitive detection of tyrosine. Once optimized the conditions, tyrosine was detected in standard solutions by square wave voltammetry, achieving a linear range comprised between 30 and 500 μM, with a detection limit equal to 4.4 μM. To detect tyrosine in serum, the interference of another amino acid i.e. tryptophan was hindered using a sample treatment with an extraction cartridge. The agreement of results analyzing serum samples with HPLC reference method and with the developed smart sensing system demonstrated the suitability of this smartphone-assisted sensing tool for cost-effective and rapid analyses of tyrosine in serum samples.

Novel electrical properties and applications in kaleidoscopic graphene nanoribbons

As one of the representatives of nano-graphene materials, graphene nanoribbons (GNRs) have more novel electrical properties, highly adjustable electronic properties, and optoelectronic properties than graphene due to their diverse geometric structures and atomic precision configurations. The electrical properties and band gaps of GNRs depend on their width, length, boundary configuration and other elemental doping, etc. With the improvement of the preparation technology and level of GNRs with atomic precision, increasing number of GNRs with different configurations are being prepared. They all show novel electrical properties and high tunability, which provides a broad prospect for the application of GNRs in the field of microelectronics. Here, we summarize the latest GNR-based achievements in recent years and summarize the latest electrical properties and potential applications of GNRs.

Graphene for the Building of Electroanalytical Enzyme-Based Biosensors. Application to the Inhibitory Detection of Emerging Pollutants

Graphene and its derivates offer a wide range of possibilities in the electroanalysis field, mainly owing to their biocompatibility, low-cost, and easy tuning. This work reports the development of an enzymatic biosensor using reduced graphene oxide (RGO) as a key nanomaterial for the detection of contaminants of emerging concern (CECs). RGO was obtained from the electrochemical reduction of graphene oxide (GO), an intermediate previously synthesized in the laboratory by a wet chemistry top-down approach. The extensive characterization of this material was carried out to evaluate its proper inclusion in the biosensor arrangement. The results demonstrated the presence of GO or RGO and their correct integration on the sensor surface. The detection of CECs was carried out by modifying the graphene platform with a laccase enzyme, turning the sensor into a more selective and sensitive device. Laccase was linked covalently to RGO using the remaining carboxylic groups of the reduction step and the carbodiimide reaction. After the calibration and characterization of the biosensor versus catechol, a standard laccase substrate, EDTA and benzoic acid were detected satisfactorily as inhibiting agents of the enzyme catalysis obtaining inhibition constants for EDTA and benzoic acid of 25 and 17 mmol·L⁻¹, respectively, and a maximum inhibition percentage of the 25% for the EDTA and 60% for the benzoic acid.

Analytical methods for amino acid determination in organisms

Amino acids are important metabolites for tissue metabolism, growth, maintenance, and repair, which are basic life necessities. Therefore, summarizing analytical methods for amino acid determination in organisms is important. In the past decades, analytical methods for amino acids have developed rapidly but have not been fully explored. Thus, this article provides reference to analytical methods for amino acids in organisms for food and human research. Present amino acid analysis methods include thin-layer chromatography, high-performance liquid chromatography, liquid chromatography-mass spectrometer, gas chromatography-mass spectrometry, capillary electrophoresis, nuclear magnetic resonance, and amino acid analyzer analysis.

Biosensors for D-Amino Acids: Detection Methods and Applications

D-enantiomers of amino acids (D-AAs) are only present in low amounts in nature, frequently at trace levels, and for this reason, their biological function was undervalued for a long time. In the past 25 years, the improvements in analytical methods, such as gas chromatography, HPLC, and capillary electrophoresis, allowed to detect D-AAs in foodstuffs and biological samples and to attribute them specific biological functions in mammals. These methods are time-consuming, expensive, and not suitable for online application; however, life science investigations and industrial applications require rapid and selective determination of D-AAs, as only biosensors can offer. In the present review, we provide a status update concerning biosensors for detecting and quantifying D-AAs and their applications for safety and quality of foods, human health, and neurological research. The review reports the main challenges in the field, such as selectivity, in order to distinguish the different D-AAs present in a solution, the simultaneous assay of both L- and D-AAs, the production of implantable devices, and surface-scanning biosensors. These innovative tools will push future research aimed at investigating the neurological role of D-AAs, a vibrant field that is growing at an accelerating pace.

Application of l-methionine γ-lyase in chiral amino acid analysis

Here, a conventional chiral amino acid analysis method using high-performance liquid chromatography was coupled with a sample pretreatment using l-methionine γ-lyase from Pseudomonas putida ICR 3460 for the selective analysis of l-methionine and l-tryptophan. The sample was analyzed after the degradation of l-methionine with l-methionine γ-lyase, as l-methionine coelutes with l-tryptophan under the standard chiral amino acid analytical conditions used for precolumn derivatization with o-phthalaldehyde and N-acetyl-l-cysteine. The l-tryptophan in the sample was then eluted as a clearly separated peak and analyzed further. Since the l-methionine γ-lyase did not act on l-tryptophan, we were able to calculate the l-methionine or l-tryptophan concentration based on the data obtained from 2 individual runs: the sample with and without l-methionine γ-lyase pretreatment. The concentration of l-tryptophan was calculated from the data obtained from the sample with l-methionine γ-lyase pretreatment, while the concentration of l-methionine was calculated using the following equation: l-methionine concentration = {the data from the sample without l-methionine γ-lyase pretreatment}-{the data from the sample with l-methionine γ-lyase pretreatment}. Model samples containing authentic amino acids and a fermented food sample were analyzed by our method, and the calculated concentrations of l-methionine and l-tryptophan were consistently in agreement with the theoretical values.

Reduced graphene oxide nanoribbon immobilized gold nanoparticle based electrochemical DNA biosensor for the detection of Mycobacterium tuberculosis

Tuberculosis is one of the most dreadful diseases caused by Mycobacterium tuberculosis with more than 9 million individuals suffering from it in 2014. Traditional methods of detection are not efficient enough for its quick and reliable detection; therefore, it is imperative to develop methods of its detection in the early stages. Consequently, we report a highly sensitive and selective biosensor for detection of Mycobacterium tuberculosis. In this work, gold nanoparticles (AuNPs, dia. ∼6 nm, 1.81 wt% loading) are immobilized over reduced graphene oxide nanoribbons (RGONRs). An ssDNA/Au/RGONR electrode is prepared by immobilizing Au nanoparticles followed by covalent modification of Au nanoparticles with 5'SH-ssDNA. As per the best knowledge of the authors, the target DNA of Mycobacterium tuberculosis is detected using a ssDNA/Au/RGONR bioelectrode by cyclic voltammetry and chronoamperometric methods for the first time. With high detection efficiency (0.1 fM), the ssDNA/Au/RGONR bioelectrode exhibited better signal amplification and electrochemical response as compared to bare Au and RGONR electrodes. Additionally, the ssDNA/Au/RGONR bioelectrode displayed good linear response to different concentrations of target M. tuberculosis DNA. The ssDNA/Au/RGONR has shown excellent specificity (92%) to Mycobacterium tuberculosis target DNA as compared with non-complementary DNA. The Au/RGONR matrix has the potential to be used as an immobilization platform for single-stranded probe DNAs of different diseases other than tuberculosis reported here.

Paper-Based Sensing Device for Electrochemical Detection of Oxidative Stress Biomarker 8-Hydroxy-2'-deoxyguanosine (8-OHdG) in Point-of-Care

This work presents a cost-effective, label-free in point-of-care (POC) biosensor for the sensitive detection of 8-hydroxy-2′-deoxyguanosine (8-OHdG), the most abundant oxidative product of DNA, that may allow a premature assessment of cancer disease, thereby improving diagnosis, prognostics and survival rates. The device targets the direct detection of 8-OHdG by using for the first time a carbon-ink 3-electrode on a paper substrate coupled to Differential Pulse Voltammetry readings. This design was optimized by adding nanostructured carbon materials to the ink and the conducting polymer PEDOT, enhancing the electrocatalytic properties of the sensor towards 8-OHdG detection. Meanwhile, the ability of this oxidative stress biomarker to undertake an oxidation reaction enabled the development of the sensing electrochemical device without the need of chemical probes and long incubation periods. This paper-modified sensor presented high electrochemical performance on the oxidation of 8-OHdG with a wide linear range (50–1000 ng/ml) and a low detection limit (14.4 ng/ml). Thus, our results showed the development of a direct and facile sensor with good reproducibility, stability, sensitivity and more importantly, selectivity. The proposed carbon-based electrochemical sensor is a potential candidate to be miniaturized to small portable size, which make it applicable for in-situ 8-OHdG sensing in real biological samples.

Core-shell heterostructured multiwalled carbon nanotubes@reduced graphene oxide nanoribbons/chitosan, a robust nanobiocomposite for enzymatic biosensing of hydrogen peroxide and nitrite

A robust nanobiocomposite based on core-shell heterostructured multiwalled carbon nanotubes@reduced graphene oxide nanoribbons (MWCNTs@rGONRs)/chitosan (CHIT) was described for the fabrication of sensitive, selective, reproducible and durable biosensor for hydrogen peroxide (H2O2) and nitrite (NO2-). The excellent physicochemical properties of MWCNTs@rGONRs such as, presence of abundant oxygen functionalities, higher area-normalized edge-plane structures and chemically active sites in combination with excellent biocompatibility of CHIT resulting in the versatile immobilization matrix for myoglobin (Mb). The most attractive property of MWCNTs@rGONRs which distinguishes it from other members of graphene family is its rich edge density and edge defects that are highly beneficial for constructing enzymatic biosensors. The direct electron transfer characteristics such as, redox properties, amount of immobilized active Mb, electron transfer efficiency and durability were studied. Being as good immobilization matrix, MWCNTs@rGONRs/CHIT is also an excellent signal amplifier which helped in achieving low detection limits to quantify H2O2 (1 nM) and NO2- (10 nM). The practical feasibility of the biosensor was successfully validated in contact lens cleaning solution and meat sample.

Carbon Nanomaterial Based Biosensors for Non-Invasive Detection of Cancer and Disease Biomarkers for Clinical Diagnosis

The early diagnosis of diseases, e.g., Parkinson's and Alzheimer's disease, diabetes, and various types of cancer, and monitoring the response of patients to the therapy plays a critical role in clinical treatment; therefore, there is an intensive research for the determination of many clinical analytes. In order to achieve point-of-care sensing in clinical practice, sensitive, selective, cost-effective, simple, reliable, and rapid analytical methods are required. Biosensors have become essential tools in biomarker sensing, in which electrode material and architecture play critical roles in achieving sensitive and stable detection. Carbon nanomaterials in the form of particle/dots, tube/wires, and sheets have recently become indispensable elements of biosensor platforms due to their excellent mechanical, electronic, and optical properties. This review summarizes developments in this lucrative field by presenting major biosensor types and variability of sensor platforms in biomedical applications.

Methyl parathion detection in vegetables and fruits using silver@graphene nanoribbons nanocomposite modified screen printed electrode

We have developed a sensitive electrochemical sensor for Organophosphorus pesticide methyl parathion (MP) using silver particles supported graphene nanoribbons (Ag@GNRs). The Ag@GNRs nanocomposite was prepared through facile wet chemical strategy and characterized by TEM, EDX, XRD, Raman, UV-visible, electrochemical and impedance spectroscopies. The Ag@GNRs film modified screen printed carbon electrode (SPCE) delivers excellent electrocatalytic ability to the reduction of MP. The Ag@GNRs/SPCE detects sub-nanomolar concentrations of MP with excellent selectivity. The synergic effects between special electrocatalytic ability of Ag and excellent physicochemical properties of GNRs (large surface area, high conductivity, high area-normalized edge-plane structures and abundant catalytic sites) make the composite highly suitable for MP sensing. Most importantly, the method is successfully demonstrated in vegetables and fruits which revealed its potential real-time applicability in food analysis.

Fe(III)-based metal-organic framework-derived core-shell nanostructure: Sensitive electrochemical platform for high trace determination of heavy metal ions

A new core-shell nanostructured composite composed of Fe(III)-based metal-organic framework (Fe-MOF) and mesoporous Fe₃O₄@C nanocapsules (denoted as Fe-MOF@mFe₃O₄@mC) was synthesized and developed as a platform for determining trace heavy metal ions in aqueous solution. Herein, the mFe₃O₄@mC nanocapsules were prepared by calcining the hollow Fe₃O₄@C that was obtained using the SiO₂ nanoparticles as the template, followed by composing the Fe-MOF. The Fe-MOF@mFe₃O₄@mC nanocomposite demonstrated excellent electrochemical activity, water stability and high specific surface area, consequently resulting in the strong biobinding with heavy-metal-ion-targeted aptamer strands. Furthermore, by combining the conformational transition interaction, which is caused by the formation of the G-quadruplex between a single-stranded aptamer and high adsorbed amounts of heavy metal ions, the developed aptasensor exhibited a good linear relationship with the logarithm of heavy metal ion (Pb²⁺ and As³⁺) concentration over the broad range from 0.01 to 10.0nM. The detection limits were estimated to be 2.27 and 6.73 pM toward detecting Pb²⁺ and As³⁺, respectively. The proposed aptasensor showed good regenerability, excellent selectivity, and acceptable reproducibility, suggesting promising applications in environment monitoring and biomedical fields.

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes' status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.