As scientists discovered that raw neurological signals could translate into bioelectric information, brain–machine interfaces (BMI) for experimental and clinical studies have experienced massive growth. Developing suitable materials for bioelectronic devices to be used for real-time recording and data digitalizing has three important necessitates which should be covered. Biocompatibility, electrical conductivity, and having mechanical properties similar to soft brain tissue to decrease mechanical mismatch should be adopted for all materials. In this review, inorganic nanoparticles and intrinsically conducting polymers are discussed to impart electrical conductivity to systems, where soft materials such as hydrogels can offer reliable mechanical properties and a biocompatible substrate. Interpenetrating hydrogel networks offer more mechanical stability and provide a path for incorporating polymers with desired properties into one strong network. Promising fabrication methods, like electrospinning and additive manufacturing, allow scientists to customize designs for each application and reach the maximum potential for the system. In the near future, it is desired to fabricate biohybrid conducting polymer-based interfaces loaded with cells, giving the opportunity for simultaneous stimulation and regeneration. Developing multi-modal BMIs, Using artificial intelligence and machine learning to design advanced materials are among the future goals for this field. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.

Conducting polymer-based nanostructured materials for brain–machine interfaces / Ziai, Y.; Zargarian, S. S.; Rinoldi, C.; Nakielski, P.; Sola, A.; Lanzi, M.; Truong, Y. B.; Pierini, F.. - In: WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY. - ISSN 1939-5116. - 15:5(2023), pp. 1-33. [10.1002/wnan.1895]

Conducting polymer-based nanostructured materials for brain–machine interfaces

Sola A.;
2023

Abstract

As scientists discovered that raw neurological signals could translate into bioelectric information, brain–machine interfaces (BMI) for experimental and clinical studies have experienced massive growth. Developing suitable materials for bioelectronic devices to be used for real-time recording and data digitalizing has three important necessitates which should be covered. Biocompatibility, electrical conductivity, and having mechanical properties similar to soft brain tissue to decrease mechanical mismatch should be adopted for all materials. In this review, inorganic nanoparticles and intrinsically conducting polymers are discussed to impart electrical conductivity to systems, where soft materials such as hydrogels can offer reliable mechanical properties and a biocompatible substrate. Interpenetrating hydrogel networks offer more mechanical stability and provide a path for incorporating polymers with desired properties into one strong network. Promising fabrication methods, like electrospinning and additive manufacturing, allow scientists to customize designs for each application and reach the maximum potential for the system. In the near future, it is desired to fabricate biohybrid conducting polymer-based interfaces loaded with cells, giving the opportunity for simultaneous stimulation and regeneration. Developing multi-modal BMIs, Using artificial intelligence and machine learning to design advanced materials are among the future goals for this field. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.
2023
4-mag-2023
15
5
1
33
Conducting polymer-based nanostructured materials for brain–machine interfaces / Ziai, Y.; Zargarian, S. S.; Rinoldi, C.; Nakielski, P.; Sola, A.; Lanzi, M.; Truong, Y. B.; Pierini, F.. - In: WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY. - ISSN 1939-5116. - 15:5(2023), pp. 1-33. [10.1002/wnan.1895]
Ziai, Y.; Zargarian, S. S.; Rinoldi, C.; Nakielski, P.; Sola, A.; Lanzi, M.; Truong, Y. B.; Pierini, F.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1335572
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