On the fundamentals of organic mixed ionic/electronic conductors
Bibliographic record
Abstract
The first Telluride Science meeting (formerly TSRC) on organic mixed ionic and electronic conductors (OMIECs), Oct 3-7, 2022, brought together researchers across the field to understand the fundamental processes and identify out-standing questions related to this exciting class of materials. OMIECs are organic materials that promote the transport of mobile electronic charge carriers while simultaneously supporting ionic transport and ionic-electronic coupling. These properties open up broad areas of applications from energy to bioelectronics. Devices include batteries, supercapacitors, actuators, electrochromic displays, and organic electrochemical transistors (OECTs). They possess the key strengths of traditional organic electronic materials, such as synthetic tunability and low-temperature processing. Despite the recent advances in devices and applications achieved with such materials, many challenges and gaps in understanding remain. These topics hold the key to designing next-generation materials and devices that continue to push the limits of performance and stability and facilitate novel functionality. This perspective aims to summarize the current understanding, conversations, and debates that made this TSRC particularly engaging, enabling new directions and searching for missing pieces of the OMIEC puzzle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".