Jairo Pinto de Oliveira

Using magnetic nanoparticles for efficient microalgae harvesting has shown promising results with enhanced harvesting efficiency within a reduced time frame. However, the cost of nanoparticle synthesis presents a potential constraint the widespread implementation of this technique. In this way, this work investigated, for the first time, the efficacy of magnetic nanoparticles (MNPs) derived from particulate matter for harvesting microalgae (Chlorella sp.) both naked and functionalized with commercial tannin. The nanoparticles were characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), Fourier transform infrared (FTIR), and zeta potential. The MNPs present in the particulate material were magnetite with an estimated size (based on 1000 particles) of around 30.5 ± 9.56 nm. Harvesting efficiency (HE %) was optimized using full factorial experiments, in which the most influential variables (pH and MNPs concentration) were combined at different levels, achieving optimal harvesting efficiency. The MNPs (naked) exhibited higher harvesting efficiency (HE % = 86%; MNPs concentration = 1250 mg. L^–1; pH = 3) than functionalized nanoparticles (HE % = 77%; MNP-TANs concentration = 1100 mg/L; pH = 3.5); however, a higher MNPs concentration was necessary in a lower pH. The functionalization contributed to particle stabilization, increasing its reuse cycles from 3 (MNSs) to 7 cycles (MNP-TANs). At pH 10 (the final pH of the microalgae cultivation), both exhibited a similar HE % of 60%.

Magnetic nanobiocatalysts based on Fe ₃ O ₄ coated with TEOS and functionalised with APTS or MPTS were developed and evaluated for cellulase immobilisation.

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.

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.

Sensitive and selective ochratoxin A (OTA) detection in challenging food matrices (wheat, coffee) is achieved using a cost-effective electrochemical method. The approach utilizes commercial carbon black-graphite screen-printed electrodes (SPE CB-G) enhanced via in situ modification with cetyltrimethylammonium bromide (CTAB). Adsorptive stripping differential pulse voltammetry (AdSDPV) parameters are rigorously optimized using central composite design (CCD), resulting in an optimal deposition time of 205 s and a CTAB concentration of 80 μmol L–1. The method achieves a limit of detection (LOD) of 1.39 ng mL–1 and a limit of quantification (LOQ) of 4.20 ng mL–1 in 0.2 mol L–1 phosphate buffer (pH 7.0) solution. In roasted and ground Arabica coffee (Coffea arabica), the method demonstrates a LOD of 2.70 ng mL–1 and a LOQ of 8.20 ng mL–1, with recovery rates ranging from 94.64 to 109.86%, compliant with key regulatory standards. The presence of CTAB minimizes the matrix effects and ensures reliable and reproducible results. This optimized SPE-based sensor offers a practical and efficient alternative to conventional methods for routine food safety monitoring.

Monodisperse and colloidally stable magnetic iron oxide nanoparticles have been developed for diverse biotechnology applications. Although promising for the adsorption of organic molecules, the low density of adsorption sites in these nanoparticles has been a significant challenge. In this study, an optimized factorial design with response surface methodology (RSM) was employed to produce small Superparamagnetic Iron Oxide Nanoparticles (SPIONs) stabilized with tetraethoxysilane (TEOS). Bovine Serum Albumin (BSA) was selected for immobilization on the surface of SPIONs to test adsorption capacity. The model was validated by correlating significant factors with experimental responses, enabling the prediction of the smallest nanoparticle size. We obtained superparamagnetic SPIONs (75.12 emu/g) with high surface area and an average diameter of 11.06 ± 0.84 nm, with stability improved by the adsorption of TEOS (-46.24 mV) and suitable for pH values from 2 to 10 and salt concentrations up to 1 M. The maximum adsorption capacity of the nanoparticles was 87.8 ± 1.79 mg of BSA per gram of nanoparticles. The nanomaterial synthesized here presents a favorable platform for anchoring protein molecules via silanol groups on its electrostatically charged surface. This study introduces an effective strategy for the synthesis and stabilization of SPIONs with potential biotechnology applications.

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.