Next‐Generation Organic Semiconductors–Materials, Fundamentals, and Applications
Bibliographic record
Abstract
Organic semiconductors continue to draw increasing attention from different disciplines because of the plethora of unique and attractive properties.Recent advances in fundamental understanding, coupled with the introduction of new materials and synthetic routes, have enabled the development of prototypical devices with new functionalities and the performance for some devices is now on par with established inorganic technologies.Besides the surge in number of publications on the topic, such demonstrations are paving the way for many innovative applications in emerging sectors of science and technology.Keeping the above in view, the Symposium EQ03 entitled "Next Generation Organic Semiconductors: Materials, Characterization and Applications" was held during the 2022 MRS Spring meeting.This symposium focused on recent advances on the synthesis, characterization, and application of organic materials and systems.Of particular interest were the molecular design, microstructure, and applications of emerging classes of materials, including macromolecular semiconductors, molecular dopants, self-assembling surface-modifying molecules, open-shell organic semiconductors, two-dimensional organic conjugated networks, non-fullerene acceptors, light-emitting molecules with enhanced reverse intersystem crossing, solid-state lasers, organic thermoelectrics, and mixed ion-electron (hole) conductors.The ultimate aim of the symposium was to provide a venue for researchers with different backgrounds to discuss recent developments, chal-N.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".