A Scalable Synthetic Approach for Producing Homogeneous, Large Area 2D Highly Conductive Polymers
Bibliographic record
Abstract
Abstract The development of 2D materials is rapidly advancing beyond traditional graphene and metal oxides and into new functional organic materials including conducting polymers (CPs). Synthesizing 2D-CPs, typically achieved by polymerizing within a highly confined space (e.g., between lipid bilayers), is a slow and poorly scalable process that is incapable of producing large area films. A ‘tethered-dopant templating’ method is used where a surface-grafted layer of dopant molecules regulates the 3D-growth of poly 3,4-ethylenedioxythiophene (PEDOT) allowing ultrathin and molecular-scale ′2D′ films having thicknesses of just ∼3 nm to be grown in an unconfined geometry over a large area (i.e., cm2). While the tethered-dopant template method is a simple and promising alternative to confined geometry templating, the electrochemical mechanisms of film growth and its impact on the film electrochemical properties have yet to be well understood. This investigation shows that the surface-tethered dopant regulates the polymerization reaction by actively suppressing chain termination to support the growth of longer and more conductive chains (i.e., higher charge carrier mobility). These 2D PEDOT films also become ′hyper-doped′ with dopant to polymer mass fractions as high as 8:1, resulting in metal-like conductivity due to enhanced charge carrier density. Additionally, 2D PEDOT films achieve unprecedented homogeneity with little variability down to submicrometer length scales. This new understanding into the dopant regulation over electropolymerization combined with the unequaled metal-like conductivity, molecular-scale dimensions, and large area of 2D PEDOT represents a significant advance in CP materials that will drive innovation across numerous fields including transparent conductors, optoelectronics, bionics, and biosensing.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".