Electrical and Optical Characterization of Atmospheric Pressure Plasmas for the Treatment of OPV Materials
Notice bibliographique
Résumé
In the last years, non-equilibrium cold plasma at atmospheric pressure have led to numerous opportunities for material treatment. Depending on the gas mixtures, the electrodes configurations, the electrical waveform used to generate the discharge, or the material, a wide range of surface properties can be created. Among the plethora of applications, the organic photovoltaic (OPV) community has recently explored the use of plasma [1]. Thanks to the versatility of the cold atmospheric pressure plasmas (CAPP), all the layers used in the OPV cells could be potentially treated and modified. The use of dry atmospheric pressure processes also opens the doors to fast treatments at reduced cost compared to low pressure [2]. Possible CAPP configurations for the OPV include plasma jets and dielectric barrier discharges (DBD) in volume or surface [3]. Among the different use of plasma in OPV, surface cleaning has been largely studied. For example, the removal of organic contaminants from indium tin oxide (ITO) layer have successfully been achieved with all the aforementioned configurations as well as different gas mixtures such as N 2 , O 2 , or He [4–6]. Other interesting mechanisms, such as the passivation of the electron transport layer [7], the modification of the work function [8], or for the encapsulation of the whole OPV cells [9] have been also explored. Still, whatever the material, either the metallic electrodes, the semiconductive active layer, or the dielectric substrates, a fine control of the plasma properties is necessary to ensure the good reproducibility and the stability of the process. It is hence necessary to characterize the physical regime and clarify the chemical reactions suitable to assess the good operation of the process during the material treatment. This work focuses on the use of electrical measurements and optical emission spectroscopy (OES) to retrieve key parameters from the process, like the discharge regime (Townsend vs filamentary), the capacitances of the system, or the power dissipated during the treatment [10]. In this context, this work aims at characterizing different discharge regimes for the surface treatment of thin characteristic layers employed in OPV ( i.e., ITO, fluorine doped tin oxide (FTO) and zinc oxide). The influence of different electrical signals (low frequency sinusoidal voltage vs nanopulse), as well as different configurations and dielectric materials are compared. In order to link and allow to use such measurement as monitoring tools for the treatment process of materials used in OPV cells, the treated surfaces are also analyzed by SEM and contact angle. The extracted quantities from the plasma diagnostics could hence be used as a predictive tool to forecast the final properties of the treated materials and improve the processes of material treatment in OPV. [1] Mariotti et al. https://doi.org/10.1002/ppap.201500187 [2] Vida et al. https://doi.org/10.37904/nanocon.2019.8646 [3] Homola et al. https://doi.org/10.1016/B978-0-323-89930-7.00001-7 [4] Chiang et al. https://doi.org/10.1007/s11090-010-9237-4 [5] Yi et al. https://doi.org/10.1016/j.surfcoat.2003.08.011 [6] Hvojnik et al. https://doi.org/10.1016/j.mssp.2021.105850 [7] Polydorou et al. https://doi.org/10.1039/C6TA03594A [8] Chaney et al. https://doi.org/10.1016/S0169-4332(01)00347-6 [9] Juillard et al. https://doi.org/10.1002/admi.202000293 [10] Pipa et al. https://doi.org/10.3390/atoms7010014
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».