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Enregistrement W4231049272 · doi:10.1149/ma2018-01/11/902

Organic-Inorganic Nanohybrid Materials for Photovoltaic Applications

2018· article· en· W4231049272 sur OpenAlexaff
Alexander E. Kobryn, Sergey Gusarov, Karthik Shankar

Notice bibliographique

RevueECS Meeting Abstracts · 2018
Typearticle
Langueen
DomaineEngineering
ThématiqueChalcogenide Semiconductor Thin Films
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésSolvationChemical physicsDensity functional theoryMaterials scienceMiscibilityMoleculeCharge densityAdsorptionMolecular dynamicsThermodynamicsChemistryComputational chemistryPhysical chemistryPhysicsOrganic chemistryPolymer

Résumé

récupéré en direct d'OpenAlex

Thin film morphology is a key factor determining the performance of bulk heterojunction organic solar cells through its influence on charge separation, charge transport and recombination losses in donor-acceptor blends. With this respect, both descriptive and predictive modeling of structural properties of blends of PCBM or organic-inorganic hybrid perovskites of the type CH 3 NH 3 PbX 3 (X=Cl, Br, I) with P3HT, P3BT or some squaraine SQ2 dye sensitizer, including adsorption on TiO 2 clusters having rutile (110) surface, is presented with the use of a methodology that allows computing the microscopic structure of blends on the nanometer scale and getting insight on miscibility of its components at various thermodynamic conditions. The methodology is based on the integral equation theory of molecular liquids in the reference interaction site representation/model (RISM) and uses the universal force field. It is statistical mechanical theory to describe the equilibrium structure of molecular liquids in terms of the site-site pair correlation functions, from which all the thermodynamic quantities can be derived. It provides a detailed microscopic insight into the organization of solvent molecules in the solvation shell structure and their contribution to the solvation thermodynamics. Input parameters for RISM, such as optimized molecular geometries and charge distribution of interaction sites, are derived with the use of the density functional theory (DFT) methods. In particular, the environment effects were taken into account by Conductor-like Screening Model (COSMO) while the charges were obtained by fitting them to reproduce the molecular electrostatic potential (ESP). To compare the diffusivity of the PCBM in binary blends with P3HT and P3BT, respectively, the study is complemented with MD simulation. In addition, the analysis of MD trajectories allows us to better choose the initial positions of molecules in the follow-up DFT analysis which is focused on the study of electronic properties on the border of the perovskite-polymer or perovskite-macromolecule system. These properties define charge transfer processes from donor to acceptor and are utilized in development of new generation organic solar cells. For better understanding, the DFT analysis is also run to study the electronic structure of perovskite surfaces cleaved by (001) and (110) planes. To explore the dependence from the layer thickness we use slabs of different width formed by clusters of unit cells in a slab plain, while to characterize the dependence of electronic structure from layer width we refer to induced partial charges of surface sites (lead and halogens) as well as their electrophilic and nucleophilic Fukui indexes (local softness and hardness), which could be used as quantitative indications for chemical reactivity. As the result of this multiscale modeling approach that includes DFT, RISM, and MD, a remarkably good agreement with available experimental data and results of alternative modeling/simulation is observed for PCBM in P3HT system. We interpret this as a step in validation of the use of our approach for organic photovoltaics and support of its results for new systems that do not have reference data for comparison or calibration. In particular, for the less studied P3BT our results show that expectations about its performance in binary blends with PCBM may be overestimated, as it does not demonstrate the required level of miscibility and short-range structural organization. The RISM model is very sensitive to the blend composition, which happens to be in very good agreement with experimental observations. The inability to find physically meaningful solutions to RISM equations at certain solute concentrations is interpreted as an indicator of the existence of an unstable region on the composition-temperature phase diagram. In case of PCBM in P3BT, the physically meaningful solutions to RISM equations were found for the infinite dilution only, which suggests a much stronger tendency toward phase segregation in PCBM-P3BT blends. This suggestion was confirmed once again in MD simulations that we run (also with the universal force fields) to quickly estimate the diffusion coefficients of PCBM solute in P3HT or P3BT solvents at infinite dilution. Quantitatively, the diffusion coefficients of PCBM in P3HT fall into the range of the ones that are typical for high polymers, while the diffusion coefficients of PCBM in P3BT are closer to the ones that are found for diffusion in fluids. The performance of P3BT with perovskites, however, seems as expected. The calculated nanoscale morphologies of blends of P3HT, P3BT or SQ2 with perovskites, including adsorption on TiO 2 , are all new and serve as an instrument in rational design of organic/hybrid photovoltaics. They are used in collaboration with experts who actually make prototypes or devices for practical applications.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,232
Écart entre enseignants0,217 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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