The influence of sorbitol doping on aggregation and electronic properties of PEDOT:PSS: a theoretical study

Friederich, Pascal ORCID: https://orcid.org/0000-0003-4465-1465, Leon Cabanillas, Salvador ORCID: https://orcid.org/0000-0002-2757-9417, Perea, José Dario, Roch, Loïc M ORCID: https://orcid.org/0000-0003-1771-2023 and Aspuru Guzik, Alán ORCID: https://orcid.org/0000-0002-8277-4434 (2021). The influence of sorbitol doping on aggregation and electronic properties of PEDOT:PSS: a theoretical study. "Machine Learning: Science And Technology", v. 2 (n. 1); pp.. ISSN 26322153. https://doi.org/10.1088/2632-2153/ab983b.

Descripción

Título: The influence of sorbitol doping on aggregation and electronic properties of PEDOT:PSS: a theoretical study
Autor/es:
Tipo de Documento: Artículo
Título de Revista/Publicación: Machine Learning: Science And Technology
Fecha: 1 Marzo 2021
ISSN: 26322153
Volumen: 2
Número: 1
Materias:
Palabras Clave Informales: Atoms; Basis-Sets; charge transport; charge-carrier mobility; Design; Disorder; Machine Learning; multiscale modeling; Neural Networks; ORGANIC CONDUCTORS; Organic Polymers; organic semiconductors; PEDOT:PSS
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Ingeniería Química Industrial y del Medio Ambiente
Licencias Creative Commons: Reconocimiento

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Resumen

Many organic electronics applications such as organic solar cells or thermoelectric generators rely on PEDOT:PSS as a conductive polymer that is printable and transparent. It was found that doping PEDOT:PSS with sorbitol enhances the conductivity through morphological changes. However, the microscopic mechanism is not well understood. In this work, we combine computational tools with machine learning to investigate changes in morphological and electronic properties of PEDOT:PSS when doped with sorbitol. We find that sorbitol improves the alignment of PEDOT oligomers, leading to a reduction of energy disorder and an increase in electronic couplings between PEDOT chains. The high accuracy (r(2) > 0.9) and speed up of energy level predictions of neural networks compared to density functional theory enables us to analyze HOMO energies of PEDOT oligomers as a function of time. We find a surprisingly low degree of static energy disorder compared to other organic semiconductors. This finding might help to better understand the microscopic origin of the high charge carrier mobility of PEDOT:PSS in general and potentially help to design new conductive polymers.

Proyectos asociados

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Horizonte 2020
795206
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ID de Registro: 94251
Identificador DC: https://oa.upm.es/94251/
Identificador OAI: oai:oa.upm.es:94251
URL Portal Científico: https://portalcientifico.upm.es/es/ipublic/item/9339711
Identificador DOI: 10.1088/2632-2153/ab983b
URL Oficial: https://iopscience.iop.org/article/10.1088/2632-21...
Depositado por: iMarina Portal Científico
Depositado el: 24 Feb 2026 06:34
Ultima Modificación: 24 Feb 2026 06:34