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Record W4401696011 · doi:10.1149/ma2024-01241415mtgabs

Electrical and Optical Characterization of Atmospheric Pressure Plasmas for the Treatment of OPV Materials

2024· article· en· W4401696011 on OpenAlexaff
Alex Destrieux, Lucia Švandová, Jakub Kelar, Gaétan Laroche, Jacopo Profili

Bibliographic record

VenueECS Meeting Abstracts · 2024
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité de MontréalCentre hospitalier de l'Université LavalUniversité Laval
Fundersnot available
KeywordsCharacterization (materials science)Atmospheric-pressure plasmaPlasmaAtmospheric pressureMaterials scienceEnvironmental scienceOptoelectronicsNanotechnologyEngineering physicsPhysicsMeteorologyNuclear physics

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.264
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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