Gabriel Fernandes Souza dos Santos

Magnetic nanoparticles (MNPs) are widely used for nucleic acid (NA) extraction, but their performance strongly depends on the synthesis and surface functionalization. In this work, we applied a design of experiments (DoE) approach to optimize the coprecipitation synthesis of iron oxide nanoparticles, identifying NH₄OH flow rate and reaction temperature as the key factors. Under optimal conditions (5.5 mL min^–1, 65 °C), Fe₃O₄ nanoparticles were coated with SiO₂ and subsequently functionalized with (3-aminopropyl)­triethoxysilane (Fe₃O₄@SiO₂-APTES). The resulting nanoparticles (∼12 nm) were stable and magnetically responsive and provided efficient NA binding. Their performance in NA extraction was validated by RT-qPCR, yielding Ct values (20–25 for S, ORF, and N genes) comparable to those of both silica column and commercial magnetic bead methods. These results demonstrate that DoE is an effective strategy for tailoring nanoparticle synthesis and highlight Fe₃O₄@SiO₂-APTES as a simple, cost-effective, and reliable material for nucleic acid purification in routine laboratory applications.

Developing advanced materials is crucial for improving electrochemical sensing platforms, particularly by enhancing sensitivity, selectivity, and miniaturization. In this study, we introduce a CO ₂ plasma‐treated multilayer graphene (MLG) paper as a novel electrode material for the electrochemical detection of paracetamol (PAR). A chemometric strategy based on design of experiments (DoE) was applied to efficiently optimize the parameters of the electroanalytical techniques employed for PAR detection. The electrode surface, characterized by scanning electron and atomic force microscopy, revealed a significant roughness and defect density, resulting in a larger electrochemically active surface area. The MLG electrodes were evaluated as voltammetric sensors using PAR as the target analyte, since it is relevant in pharmaceutical and environmental analysis. Electrochemical performance was assessed through cyclic voltammetry and square‐wave adsorptive stripping voltammetry in various supporting electrolytes. The CO ₂ plasma‐treated electrode (MLG‐t) exhibited notably improved sensitivity toward PAR detection. The optimized sensor exhibited a linear working range of 0.30−8.4 µmol L ⁻¹ and a limit of detection of 0.080 µmol L ⁻¹ . The analysis of the pharmaceutical tablets revealed recovery values in the range of 101.7% to 104.0%. These findings demonstrate that CO ₂ plasma treatment, coupled with DoE optimization, represents a valuable strategy for engineering cost‐effective and high‐performance graphene‐based electrochemical sensors.

Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful analytical tool for ultrasensitive detection of environmental contaminants, particularly pesticides. Recent developments have focused on nanomaterials engineering and SERS-active sensors to enhance signal intensity. Gold (AuNPs) and silver nanoparticles (AgNPs) of various morphologies and sizes have been widely explored due to their plasmonic properties, as well as hybrid or combined nanoparticle systems. Innovative approaches have also been developed, such as embedding nanoparticles in gels to enhance stability and reproducibility or using magnetic nanoparticles for sample interaction and preconcentration. Additionally, the integration of graphene oxide has gained attention because of its ability to improve the chemical enhancement mechanism via π–π interactions with analyte molecules. Despite these advances, SERS-based detection remains challenging, particularly with regard to selectivity in complex matrices. To address this issue, recent strategies have combined SERS substrates with biorecognition molecules such as antibodies, aptamers, and enzymes, thereby improving specificity and facilitating the development of SERS-based biosensors. This review highlights the current state-of-the-art SERS applications for pesticide detection in food and environmental samples, discussing the key technological advances, material innovations, and analytical challenges. This paper also offers perspectives on future research directions to increase the sensitivity, reproducibility, and field applicability of SERS-based detection platforms.

Quality parameters, physical-chemical characteristics, and antioxidant capacity are important to ensure the successful commercialization of honey. On the other hand, the presence of antioxidants in foods gives them greater added value. Thus, based on the study of total antioxidant capacity (TAC), along with the analysis of total phenolic compounds (TPC) and total flavonoid compounds (TFC), it is possible to classify honey according to its composition. First, all the samples were evaluated according to their polinic origin (melissopalynological analysis). The TAC was determined using both spectrophotometric methods (ferric reducing antioxidant power, FRAP) and electrochemical methods (ceric reducing antioxidant capacity, CRAC). All types of honey exhibited significant values for TAC, TPC, and TFC. To assess honey quality, methodologies such as moisture content, diastase activity, hydroxymethylfurfural (HMF), total acidity (TA), and water activity (A _w) were employed. Among the analyzed samples, 5 and 2 honey showed high levels of moisture (20.10-20.87%), TA (65.12-65.49 mequiv kg^-1), and HMF (>60 mg kg^-1), surpassing the limits set by Brazilian legislation. This indicates a potentially longer shelf life or the possibility of adulteration. Furthermore, it was concluded that the color of honey may interfere with FRAP (r = 0.837). In this regard, the CRAC assay presents a new alternative for evaluating TAC in bee products.

This work presents an optimization of the construction, treatment, and activation of 3D-printed electrochemical sensors (E-3D). For this, was used a 2³-full factorial design examining three key variables at two levels: electrode height, electrode diameter, and printing speed. Moreover, it evaluates various physical, chemical, and electrochemical methods to treat and activate the E-3D surface. The techniques of electrochemical impedance spectroscopy and cyclic voltammetry (CV) shows that the sequential physical, chemical, and electrochemical treatments lead to the highest treatment efficiency and activation. Raman spectroscopy and atomic force microscopy characterize untreated and treated E-3D sensor surfaces. The optimal treatment and activation methodology was applied to the electroanalysis of paracetamol (PAR) and caffeine (CAF) simultaneously using CV and differential pulse anodic stripping voltammetry (DPASV). DPASV measurements reveal limits of detection of 0.44 and 0.58 μmol L^-1 in a 0.5 mol L^-1 H₂SO₄ medium for PAR and CAF, respectively, with the treated and activated E-3D sensor. The principal achievement of this work was emphasizing the critical role of surface treatment and activation in enhancing the performance of the developed electrodes, thereby advancing technological applications of 3D-printed electrochemical sensors.

This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The comprehensive database utilized Raman spectra from all 594 blood serum samples. Machine learning investigations were carried out using the Scikit-Learn library and were implemented in Python, and the characteristics of Raman spectra of positive and negative SARS-CoV-2 samples were extracted using the Uniform Manifold Approximation and Projection (UMAP) technique. The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). The kNN model led to a sensitivity of 0.943, specificity of 0.9275, and accuracy of 0.9377. This study showed that combining Raman spectroscopy and a machine algorithm can be an effective diagnostic method. Furthermore, we highlighted the advantages and disadvantages of each algorithm, providing valuable information for future research.