Real-Time and In Situ Monitoring of the Synthesis of Silica Nanoparticles

Autor(es)

Ferreira, Larissa F. ; Giordano, Gabriela F. ; Gobbi, Angelo L. ; Piazzetta, Maria H. O. ; Schleder, Gabriel R. ; Lima, Renato S.

• Tipo de documento: Artigo Científico
• Fonte: ACS SENSORS; v. 7, n. 4, p. 13-pg., 2022-04-22.

Resumo

The real-time and in situ monitoring of the synthesis of nanomaterials(NMs) remains a challenging task, which is of pivotal importance by assisting fundamental studies (e.g., synthesis kinetics and colloidal phenomena) and providingoptimized quality control. In fact, the lack of reproducibility in the synthesis of NMsis a bottleneck against the translation of nanotechnologies into the market towarddaily practice. Here, we address an impedimetric millifluidic sensor with dataprocessing by machine learning (ML) as a sensing platform to monitor silicananoparticles (SiO2NPs) over a 24 h synthesis from a single measurement. TheSiO2NPs were selected as a model NM because of their extensive applications.Impressively, simple ML-fitted descriptors were capable of overcoming interferencesderived from SiO2NP adsorption over the signals of polarizable Au interdigitateelectrodes to assure the determination of the size and concentration of nanoparticlesover synthesis while meeting the trade-offbetween accuracy and speed/simplicity ofcomputation. The root-mean-square errors were calculated as similar to 2.0 nm (size) and 2.6x1010nanoparticles mL-1(concentration).Further, the robustness of the ML size descriptor was successfully challenged in data obtained along independent syntheses usingdifferent devices, with the global average accuracy being 103.7 +/- 1.9%. Our work advances the developments required to transform aclosedflow system basically encompassing the reactionalflask and an impedimetric sensor into a scalable and user-friendly platformto assess thein situsynthesis of SiO2NPs. Since the sensor presents a universal response principle, the method is expected to enablethe monitoring of other NMs. Such a platform may help to pave the way for translating "sense-act" systems into practice use in nanotechnology (AU)

• Processo FAPESP: 18/24214-3 - Sensor impedimétrico e machine learning para o monitoramento in-situ de nanopartículas
• Beneficiário: Larissa Fernanda Ferreira
• Modalidade de apoio: Bolsas no Brasil - Doutorado