Two graphene samples co-doped with nitrogen and sulfur were synthesized by the hydrothermal method using thiourea as doping and reducing agent for graphene oxide (GO). An appropriate amount of thiourea was added to the aqueous dispersion of GO, previously sonicated for 30 min. The mixture was poured into an autoclave and placed in the oven for 3 h, at 120 and 200 °C. The samples were denoted NSGr-120 and NSGr-200, respectively, in agreement with the reaction temperatures. They were next morphologically and structurally characterized by advanced techniques, such as SEM/TEM, XPS, XRD, and FTIR. According to XPS analysis, the NSGr-120 sample has higher amounts of heteroatoms in comparison with NSGr-200, indicating that the reaction temperature is a crucial factor that affects the doping degree. In order to reveal the influence of the doping degree on the electrochemical performances of graphene-modified electrodes, they were tested in solutions containing L-cysteine molecules. The electrode with the best electrocatalytic performances, GC/NSGr-120, was tested to detect L-cysteine in a pharmaceutical drug, proving its applicability in real sample analysis.

A graphene sample (EGr) was prepared by electrochemical exfoliation of graphite rods in solution containing 0.05 M (NH₄)₂SO₄ + 0.1 M H₃BO₃ + 0.05 M NaCl. The exfoliation was performed by applying a constant voltage (12 V) between the graphite rods, while the temperature was kept constant (18 °C) with a temperature-controlled cryostat. The structural investigation of the graphene sample, performed by X-ray powder diffraction (XRD), revealed that the sample consists of a mixture of few-layer (69%), multi-layer graphene (14%) and graphene oxide (17%). In addition, XPS analysis proved that the sample was triple-doped with heteroatoms such as nitrogen (1.7 at%), sulfur (2.5 at%), and boron (3 at%). The sample was deposited onto the surface of a clean, glassy carbon electrode (GC) and investigated for the non-enzymatic electrochemical detection of L-tryptophan (TRP). The electrocatalytic properties of the EGr/GC electrode led to a considerable decrease in the oxidation potential from +0.9 V (bare GC) to +0.72 V. In addition, the EGr/GC electrode has higher sensitivity (two times) and a lower detection limit (ten times) in comparison with the bare GC electrode.

The present study proposes the development of new wine recognition models based on Artificial Intelligence (AI) applied to the mid-level data fusion of ¹H NMR and Raman data. In this regard, a supervised machine learning method, namely Support Vector Machines (SVMs), was applied for classifying wine samples with respect to the cultivar, vintage, and geographical origin. Because the association between the two data sources generated an input space with a high dimensionality, a feature selection algorithm was employed to identify the most relevant discriminant markers for each wine classification criterion, before SVM modeling. The proposed data processing strategy allowed the classification of the wine sample set with accuracies up to 100% in both cross-validation and on an independent test set and highlighted the efficiency of ¹H NMR and Raman data fusion as opposed to the use of a single-source data for differentiating wine concerning the cultivar and vintage.

Due to the recent increase in average living standards, food safety has caught public attention. It is necessary to conduct a qualitative and quantitative rapid test of prohibited food additives since the inclusion of food additives or the improper usage of synthetic dyes can negatively impact on the human health. Herein, a highly sensitive method for Sunset Yellow detection based on a glassy carbon electrode modified with few-layer graphenes was proposed. The electrochemical behavior of SY at the GR-exf/GCE modified surface was investigated by Cyclic Voltammetry, Square Wave Voltammetry, Electrochemical Impedance Spectroscopy and Amperometry. The influences of pH, scan rate, and interfering species were studied. Under optimized conditions, the developed sensor shows good linearity over a broad SY concentration range, e.g., 0.028–30 µM, with a low limit of detection (LOD = 0.0085 µM) and quantification (LOQ = 0.028 µM) (data obtained by amperometric technique). Furthermore, the modified electrode shows good selectivity, precision and sensitivity and has been successfully applied for SY quantification from commercially available pharmaceutical formulation as well as from candy bars and orange juice.

Increased evidence has documented a direct association between Ciprofloxacin (CFX) intake and significant disruption to the normal functions of connective tissues, leading to severe health conditions (such as tendonitis, tendon rupture and retinal detachment). Additionally, CFX is recognized as a potential emerging pollutant, as it seems to impact both animal and human food chains, resulting in severe health implications. Consequently, there is a compelling need for the precise, swift and selective detection of this fluoroquinolone-class antibiotic. Herein, we present a novel graphene-based electrochemical sensor designed for Ciprofloxacin (CFX) detection and discuss its practical utility. The graphene material was synthesized using a relatively straightforward and cost-effective approach involving the electrochemical exfoliation of graphite, through a pulsing current, in 0.05 M sodium sulphate (Na₂SO₄), 0.05 M boric acid (H₃BO₃) and 0.05 M sodium chloride (NaCl) solution. The resulting material underwent systematic characterization using scanning electron microscopy/energy dispersive X-ray analysis, X-ray powder diffraction and Raman spectroscopy. Subsequently, it was employed in the fabrication of modified glassy carbon surfaces (EGr/GC). Linear Sweep Voltammetry studies revealed that CFX experiences an irreversible oxidation process on the sensor surface at approximately 1.05 V. Under optimal conditions, the limit of quantification was found to be 0.33 × 10⁻⁸ M, with a corresponding limit of detection of 0.1 × 10⁻⁸ M. Additionally, the developed sensor’s practical suitability was assessed using commercially available pharmaceutical products.