Reporting Performance in Organic Photovoltaic Devices
Bibliographic record
Abstract
Research into organic photovoltaics (OPVs) is rapidly growing worldwide because it offers a route to low temperature, inexpensive processing of lightweight, flexible solar cells that can be mass manufactured cheaply. Unlike silicon or other inorganic semiconductors (e.g., CdTe, CIGs), OPVs are complicated by the requirement of having multiple materials and layers that must be integrated to enable the cell to function. The enormous number of research hours required to optimize all aspects of OPVs and to integrate them successfully is typically boiled down to one number-the power conversion efficiency (PCE) of the device. The PCE is the value by which comparisons are routinely made when modifications are made to devices; new bulk heterojunction materials, electron- and hole-transport layers, electrodes, plasmonic additives, and many other new advances are incorporated into OPV devices and compared with one, or a series of, control device(s). The concern relates to the statistical significance of this all-important efficiency/PCE value: is the observed change or improvement in performance truly greater than experimental error? If it is not, then the field can and will be misled by improper reporting of efficiencies, and future research in OPVs could be frustrated and, ultimately, irreversibly damaged. In this Perspective, the dangers of, for instance, cherry-picking of data and poor descriptions of experimental procedures, are outlined, followed by a discussion of a real data set of OPV devices, and how a simple and easy statistical treatment can help to distinguish between results that are indistinguishable experimentally, and those that do appear to be different.
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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".