Imine-Linked 2D Conjugated Porous Organic Polymer Films for Tunable Acid Vapor Sensing
Bibliographic record
Abstract
Two-dimensional (2D) films of conjugated porous organic polymers (C-POPs) can translate the rich in-plane functionalities of conjugated frameworks into diverse optical and electronic applications while addressing the processability issues of their crystalline analogs for adaptable device architectures. However, the lack of facile single-step synthetic routes to obtain large-area high-quality films of 2D-C-POPs has limited their application possibilities so far. Here, we report the synthesis of four mechanically robust imine-linked 2D-C-POP free-standing films using a single-step fast condensation route that is scalable and tunable. The rigid covalently bonded 2D structures of the C-POP films offer high stability for volatile gas sensing in harsh environments while simultaneously enhancing site accessibility for gas molecules due to mesoporosity by structural design. Structurally, all films were composed of exfoliable layers of 2D polymeric nanosheets (NSs) that displayed anisotropy from disordered stacking, evinced by out-of-plane birefringent properties. The tunable in-plane conjugation, different nitrogen centers, and porous structures allow the films to act as ultraresponsive colorimetric sensors for acid sensing via reversible imine bond protonation. All the films could detect hydrogen chloride (HCl) gas down to 0.05 ppm, far exceeding the Occupational Safety and Health Administration's permissible exposure limit of 5 ppm with fast response time and good recyclability. Computational insights elucidated the effect of conjugation and tertiary nitrogen in the structures on the sensitivity and response time of the films. Furthermore, we exploited the exfoliated large 2D NSs and anisotropic optoelectronic properties of the films to adapt them into micro-optical and triboelectric devices to demonstrate their real-time sensing capabilities.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".