Molecularly Imprinted Semiconducting Polymers for Size- and Interaction-Selective Gas Sensors Based on Organic Thin-Film Transistors
Bibliographic record
Abstract
Achieving high selectivity among chemically similar analytes remains a critical challenge for organic thin-film transistor (OTFT)-based gas sensors. We report a molecular imprinting strategy that imparts both size- and interaction-selective sensing by covalently incorporating ethanol molecules into acid-cleavable acetal side chains of the semiconducting polymer TAT-2. Subsequent HCl vapor treatment cleaves these side chains, generating subnanometer pores and free aldehyde groups in the resulting polymer TFT-2, as confirmed by FTIR analysis. These structural features facilitate selective diffusion and hydrogen-bonding interactions with small alcohols. TAT-2 and TFT-2 exhibit HOMO energy levels of −5.29 and −5.37 eV, respectively, rendering them stable p-type semiconductors with hole mobilities of ∼10 –4 –10 –3 cm 2 V –1 s –1 in OTFTs under nitrogen and ambient air. While TAT-2 OTFTs responded nonselectively to ethanol and other VOCs, TFT-2 devices demonstrated high sensitivities to ethanol (1.11 × 10 –4 ppm –1 ) and methanol (0.61 × 10 –4 ppm –1 ), but much lower responses to isopropanol (0.11 × 10 –4 ppm –1 ), acetone (0.0057 × 10 –4 ppm –1 ), and negligible responses to larger or nonpolar VOCs. Unlike TAT-2 devices, which showed current decreases, TFT-2 devices exhibited current increases upon methanol and ethanol exposure, likely due to pore filling that passivates charge-trapping sites and enhances charge transport. This side-chain engineering approach establishes a new paradigm for molecular imprinting in semiconducting polymers, enabling facile device fabrication with functional microstructures for selective analyte recognition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".