Electrical and Optical Characterization of Atmospheric Pressure Plasmas for the Treatment of OPV Materials
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".