Semiconducting Polymers Containing Tellurium: Perspectives Toward Obtaining High-Performance Materials
Bibliographic record
Abstract
Abstract The development of high-carbon-content polymers for optoelectronics is an area of intense research; however, carbon-rich materials have certain limitations that arise from their composition. Some of these limitations can be overcome by the judicious incorporation of heavier elements which do not significantly change the carbon content to a point where it adversely affects cost and processability. Here we examine the use of tellurium as a heavy atom in the design of optoelectronic polymers. Group 16 atom (O, S, Se, Te) substitution is a promising strategy for the development of high performance materials for organic electronic applications. The use of tellurium in place of selenium or sulfur in conjugated polymers lends new properties to these materials such as red-shifted optical absorption, high polarizability, high dielectric constant, and strong intermolecular interactions. These properties are favorable for organic photovoltaics (OPVs) and organic field effect transistors (OFETs). In particular, extending the absorption range to the near-IR allows for more efficient solar harvesting since low-energy photons are most abundant. Additionally, strong Te?Te interactions lead to enhanced interchain electronic coupling, which is expected to facilitate charge transport. The use of polymers containing tellurophene, the tellurium analogue of the well-studied thiophene, has only recently begun to emerge in the literature. New synthetic routes have been developed, and there now exist a handful of tellurophene-containing polymers that have been synthesized and used to fabricate OPVs and OFETs. Their performance in OPVs has not surpassed that of their lighter chalcogen analogues; however, the use of tellurophene-containing materials is a young field, and continued efforts in the development of new materials and device optimization should lead to improved performance. In this Perspective we discuss the current status of tellurium-containing polymers in terms of their synthesis, properties, and performance. We highlight the challenges that have been overcome thus far and emphasize those that should be the focus of future work. This includes overcoming synthetic challenges and developing an understanding of the current limitations in device performance with tellurium-containing polymers through studies of materials properties and excited state dynamics. We also suggest new applications and directions for tellurium-containing materials beyond OPVs and OFETs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".