Predictive study, using density functional theory and time dependent functional theory, on the structure-property quantification of methylene blue and methyl red dyes for the application in organic solar cells
Bibliographic record
Abstract
In this work, two organic materials as dyes, namely, methylene blue (MB) and methyl red (MR), have been proposed to play the role of the electron donor in organic photovoltaic (OPV) cells. We use the PCBM as a well-known electron acceptor. The density functional theory (DFT) method has been used to determine the electrostatic potential and the frontier molecular orbitals (FMO), of the methylene blue (MB) and the methyl red (MR) compounds. Nonlinear optical (NLO) descriptors have been determined for the two compounds. The potential energy surface analysis has been performed by the DFT method using the exchange and correlation of Becke, Lee, Yang, and Parr Gradient Corrected Functional (B3LYP) with the standard 6-31G(d) base. We have performed another theoretical study using quantum time-dependent density functional theory (TD-DFT) on both MB and MR as organic dyes to determine their UV-Vis spectra. The results of the energy gap, chemical hardness, dipole moment, and hyperpolarizability show that MB may be chemically more reactive Than MR. The present work has proposed a bilayer organic photovoltaic (OPV) cell to contribute to the valorization of the two dyes as solar materials. The developed photovoltaic cell project has used electrical and energetic parameters that can describe the OPV cell based on ([MB or MR]: PCBM). Open-circuit voltage (Voc), excitation energy, and oscillator strength have been theoretically determined. The results of the present work showed a remarkably high open circuit voltage, especially in the case of methyl red (1.55 V) more than in the case of the methylene blue (0.84 V) so that both of the two dyes can be a good candidate for organic solar cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".