| Autor: |
Ferreira LF; Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil.; Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil., Giordano GF; Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil., Gobbi AL; Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil., Piazzetta MHO; Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil., Schleder GR; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States., Lima RS; Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil.; Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil.; Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo 09210-580, Brazil.; São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 13566-590, Brazil. |
| Abstrakt: |
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 providing optimized quality control. In fact, the lack of reproducibility in the synthesis of NMs is a bottleneck against the translation of nanotechnologies into the market toward daily practice. Here, we address an impedimetric millifluidic sensor with data processing by machine learning (ML) as a sensing platform to monitor silica nanoparticles (SiO 2 NPs) over a 24 h synthesis from a single measurement. The SiO 2 NPs were selected as a model NM because of their extensive applications. Impressively, simple ML-fitted descriptors were capable of overcoming interferences derived from SiO 2 NP adsorption over the signals of polarizable Au interdigitate electrodes to assure the determination of the size and concentration of nanoparticles over synthesis while meeting the trade-off between accuracy and speed/simplicity of computation. The root-mean-square errors were calculated as ∼2.0 nm (size) and 2.6 × 10 10 nanoparticles mL -1 (concentration). Further, the robustness of the ML size descriptor was successfully challenged in data obtained along independent syntheses using different devices, with the global average accuracy being 103.7 ± 1.9%. Our work advances the developments required to transform a closed flow system basically encompassing the reactional flask and an impedimetric sensor into a scalable and user-friendly platform to assess the in situ synthesis of SiO 2 NPs. Since the sensor presents a universal response principle, the method is expected to enable the monitoring of other NMs. Such a platform may help to pave the way for translating "sense-act" systems into practice use in nanotechnology. |
