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      Influence of filament aging and conductive additive in 3D printed sensors

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          Abstract

          <p class="first" id="d4737637e134">3D printing technology combined with electrochemical techniques have allowed the development of versatile and low-cost devices. However, some aspects need to be considered for the good quality and useful life of the sensors. In this work, we have demonstrated herein that the filament aging, the conductive material, and the activation processes (post-treatments) can influence the surface characteristics and the electrochemical performance of the 3D printed sensors. Commercial filaments and 3D printed sensors were morphologically, thermally, and electrochemically analyzed. The activated graphene-based (Black Magic®) sensor showed the best electrochemical response, compared to the carbon black-filament (Proto-Pasta®). In addition, we have proven that filament aging harms the performance of the sensors since the electrodes produced with three years old filament had a considerably lower intra-days reproducibility. Finally, the activated graphene-based sensor has shown the best performance for the electrochemical detection of bisphenol A, demonstrating the importance of evaluating and control the characteristics and quality of filaments to improve the mechanical, conductive, and electrochemical performance of 3D printed sensors. </p>

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          Is Open Access

          3D printing of ceramics: A review

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            Raman spectroscopy of graphene-based materials and its applications in related devices.

            Graphene-based materials exhibit remarkable electronic, optical, and mechanical properties, which has resulted in both high scientific interest and huge potential for a variety of applications. Furthermore, the family of graphene-based materials is growing because of developments in preparation methods. Raman spectroscopy is a versatile tool to identify and characterize the chemical and physical properties of these materials, both at the laboratory and mass-production scale. This technique is so important that most of the papers published concerning these materials contain at least one Raman spectrum. Thus, here, we systematically review the developments in Raman spectroscopy of graphene-based materials from both fundamental research and practical (i.e., device applications) perspectives. We describe the essential Raman scattering processes of the entire first- and second-order modes in intrinsic graphene. Furthermore, the shear, layer-breathing, G and 2D modes of multilayer graphene with different stacking orders are discussed. Techniques to determine the number of graphene layers, to probe resonance Raman spectra of monolayer and multilayer graphenes and to obtain Raman images of graphene-based materials are also presented. The extensive capabilities of Raman spectroscopy for the investigation of the fundamental properties of graphene under external perturbations are described, which have also been extended to other graphene-based materials, such as graphene quantum dots, carbon dots, graphene oxide, nanoribbons, chemical vapor deposition-grown and SiC epitaxially grown graphene flakes, composites, and graphene-based van der Waals heterostructures. These fundamental properties have been used to probe the states, effects, and mechanisms of graphene materials present in the related heterostructures and devices. We hope that this review will be beneficial in all the aspects of graphene investigations, from basic research to material synthesis and device applications.
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              3D printing of smart materials: A review on recent progresses in 4D printing

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                Author and article information

                Contributors
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                Journal
                Analytica Chimica Acta
                Analytica Chimica Acta
                Elsevier BV
                00032670
                January 2022
                January 2022
                : 1191
                : 339228
                Article
                10.1016/j.aca.2021.339228
                35033250
                fa739fa7-81c6-48e4-b1b1-adaef277799d
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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