Measuring Energy Gaps of Organic Semiconductors by Electron Energy Loss Spectroscopies
Bibliographic record
Abstract
Herein is explored the use of electron energy loss due to π–π* transition to measure energy gaps of organic semiconductors. Sources of kinetic electrons studied include an external electron gun (reflection electron energy loss spectroscopy (REELS)) and an internal core shell excitation by X‐rays (i.e., X‐ray photoelectron spectroscopy (XPS)). To obtain the bandgap accurately, a data analysis method is proposed to extract the optical bandgap from the π–π* inelastic electron energy loss spectra. This method uses a Gaussian function to fit experimental data yielding a peak width w and position E 0 . The energy gap of an organic semiconductor, i.e., the onset of the π–π* transition peak, can then be calculated by . Through examination of 11 organic semiconductors, it is found that the bandgaps measured by REELS agree well with optical bandgaps. The bandgaps measured by XPS, however, do not always agree with optical gaps. This indicates that the XPS π–π* inelastic peak in some material systems may convolute with other core shell processes such as shake‐up. In addition, it is shown that all key energy structures of an organic semiconductor can be measured concurrently by REELS and ultraviolet photoemission spectroscopy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".