Thermo/photo-responsive porous organic frameworks for sustainable gas separation and bio-applications
Bibliographic record
Abstract
Porous organic frameworks including metal–organic frameworks (MOFs), covalent organic frameworks (COFs), and hydrogen-bonded organic frameworks (HOFs), have emerged as structurally diverse and functionally tunable platforms in advanced materials science. Among these, stimuli-responsive porous organic frameworks that undergo reversible structural or physicochemical transformations under external stimuli such as temperature and light have attracted increasing attention. This review provides a critical overview of recent advances in the design, mechanisms, and functions of thermal- and photo-responsive porous organic frameworks. We categorize response strategies according to framework type and responsive element, and highlight how these features contribute to dynamic performance across multiple length scales. Applications such as spatiotemporally controlled drug release, selective gas separation, and switchable enzyme-mimetic catalysis are discussed as model systems to illustrate the functional impact of stimuli responsiveness. Despite recent progress, challenges remain in achieving high responsiveness without compromising stability, in tuning selectivity toward specific stimuli, and in integrating these systems into real-world applications. Looking ahead, a deeper understanding of structure–response correlations, coupled with advances in in situ characterization and computational modeling, will be key to unlocking the full potential of stimuli-responsive porous organic frameworks in next-generation adaptive systems. • Stimuli-responsive porous frameworks offer dynamic tunability in material properties. • This review covers thermal and photo response mechanisms in MOFs, COFs and HOFs. • Multi-scale dynamic behaviors and underlying mechanisms are systematically discussed. • Applications include controlled drug release, gas separation and switchable catalysis. • Current challenges and future directions for practical applications are outlined.
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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) | 0.002 | 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".