Computational Explanation of the Photovoltaic Cells Properties of the PCBM and PC71BM Derivatives using the Density Functional Theory
Bibliographic record
Abstract
Abstract Organic photovoltaic cells are electronic devices that convert sunlight into electricity. To this end, the number of studies on organic photovoltaic cells (OVCs) is growing, and this trend is expected to continue. Computational studies are still needed to verify and prove the capability of CVOs, specifically the nanometer molecule PCBM, based on successful experimental results. In this paper, we present a theoretical and computational investigation of PCBM and PC71BM derivatives using the DFT method. On this basis, we employ independent and time-dependent density theories. HOMO, LUMO and GAPH-L energies, ionization potentials and electronic affinity are determined and found to be in agreement with experiments. Using DFT theory based on B3LYP and M062X methods with bases 6-31G (d,p) and 6-311G (d), calculations show that the most efficient acceptors are presented in the group of PC71BM derivatives and are in substantial agreement with experiments. The geometries of the structures are optimized by Gaussian 09.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".