Design‐to‐Device Approach Affords Panchromatic Co‐Sensitized Solar Cells

Cooper, Christopher B.; Beard, Edward J.; Vázquez‐Mayagoitia, Álvaro; Stan, Liliana; Stenning, Gavin B. G.; Nye, Daniel W.; Vigil, Julian A.; Tomar, Tina; Jia, Jing‐Ming; Bodedla, Govardhana Babu; Chen, Song; Gallego, Lucía; Franco, Santiago; Carella, Antonio; Thomas, K. R. Justin; Xue, Song; Zhu, Xia; Cole, Jacqueline M.

doi:10.1002/aenm.201802820

Cited by 48 publications

(68 citation statements)

References 52 publications

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“…When we look at the behavior of the photoanode GK5 finally obtained from aqueous solutions, we see that it is very similar to that obtained in acidified water and in this case the amino is entirely free, while the catechol is engaged in hydrogen bonds with water [5]. A last consideration can be made by looking at the values of Voc in GK4 and GK5 that are the highest except when the GK5 is prepared from the acid solution, in that case is lowered since the doublet of the amino does not change the level of the conduction band of TiO 2 [6,7].…”

Section: Photoelectrochemical Characterization Of Dsscssupporting

confidence: 53%

A Photoelectrochemical Study of Bioinspired 2-Styryl-1-Benzopyrylium Cations on TiO2 Nanoparticle Layer for Application in Dye-Sensitized Solar Cells

et al. 2019

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In the present work, five 2-styryl-1-benzopyrylium salts and their relative self-assembly processes towards TiO2 nanocrystalline layers were evaluated as photosensitizers in dye-sensitized solar cells (DSSCs). Integration of these 2-styryl-1-benzopyrylium salts with the semiconductor allow for the performance of highly specific functions suitable for smart applications in material science. Spectroscopic and photoelectrochemical measurements conducted on these five bio-inspired dyes, in solution and upon adsorption onto titanium dioxide films, allowed detailed discussion of the anchoring ability of the different donor groups decorating the 2-styryl-1-benzopyrylium core and have demonstrated their ability as photosensitizers. Our results suggest that the introduction of a dimethylamino group in position 4′ of the 2-styryl-1-benzopyrylium skeleton can alter the conjugation of the molecule leading to larger absorption in the visible region and a stronger electron injection of the dye into the conduction band of TiO2. Moreover, our experimental data have been supported by theoretical calculations with the aim to study the energy of the excited states of the five compounds. In this specific case, the simulations reported contributed to better describe the properties of the compounds used and to help create the necessary basis for the design of new and targeted bio-inspired molecules.

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Section: Photoelectrochemical Characterization Of Dsscssupporting

confidence: 53%

A Photoelectrochemical Study of Bioinspired 2-Styryl-1-Benzopyrylium Cations on TiO2 Nanoparticle Layer for Application in Dye-Sensitized Solar Cells

et al. 2019

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“…While our current models enable us to predict phase-transition temperatures for known compounds, our ultimate goal is to predict and experimentally validate new classes of compounds for magnetic and superconducting applications. While data-driven materials discovery has been achieved in other fields of research 10,40,41 , it remains a distant goal in the magnetic and superconductivity domain. Yet, our toolkit is poised for this endeavour since its databank utility could be reverse engineered with some toolkit adaptations to predict material compositions that have desired phase transitions.…”

Section: Discussionmentioning

confidence: 99%

“…There is also the growing trend for data to be published through other forms of media, and in formats that are at different stages of data processing. In addition, while data continue to be generated from experiments, materials data are increasingly computed; examples of highthroughput computational databases in materials science have already been mentioned above [8][9][10] . Many computational data hosted by online databases, as well as raw data from high-end experiments, are being given DOIs so that they can be identified just like a journal article.…”

Section: Associated Data To Corroborate Phase-transition Temperature mentioning

confidence: 99%

“…Although computationally expensive, these approaches present significant savings in time and cost compared with experimentally driven research, thereby decreasing the timeline of materials discovery from decades to months. High-throughput projects that integrate computational and experimental data are rare, but afford actual materials discovery where they do exist 10 .…”

Section: Introductionmentioning

confidence: 99%

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Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning

Court

Cole

2020

npj Comput Mater

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Predicting the properties of materials prior to their synthesis is of great importance in materials science. Magnetic and superconducting materials exhibit a number of unique properties that make them useful in a wide variety of applications, including solid oxide fuel cells, solid-state refrigerants, photon detectors and metrology devices. In all these applications, phase transitions play an important role in determining the feasibility of the materials in question. Here, we present a pipeline for fully integrating data extracted from the scientific literature into machine-learning tools for property prediction and materials discovery. Using advanced natural language processing (NLP) and machine-learning techniques, we successfully reconstruct the phase diagrams of well-known magnetic and superconducting compounds, and demonstrate that it is possible to predict the phase-transition temperatures of compounds not present in the database. We provide the tool as an online open-source platform, forming the basis for further research into magnetic and superconducting materials discovery for potential device applications.npj Computational Materials (2020) 6:18 ; https://doi.

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“…If public databases are not available in some cases, natural language processing (NLP) can be applied to extract data from published literature. For example, Cooper et al [141] adopted the text-mining tool ChemDataExtractor [142] to construct a database of 9431 dye candidates in their polymeric solar cell study. Their database constructed through NLP contains the information of chemical structures, maximum absorption wavelengths, and molar extinction coefficients.…”

Section: Materials Databasementioning

confidence: 99%

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

et al. 2020

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Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.

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Design‐to‐Device Approach Affords Panchromatic Co‐Sensitized Solar Cells

Cited by 48 publications

References 52 publications

A Photoelectrochemical Study of Bioinspired 2-Styryl-1-Benzopyrylium Cations on TiO2 Nanoparticle Layer for Application in Dye-Sensitized Solar Cells

A Photoelectrochemical Study of Bioinspired 2-Styryl-1-Benzopyrylium Cations on TiO2 Nanoparticle Layer for Application in Dye-Sensitized Solar Cells

Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

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