Photogenerated Radical Amplified D–A–D Metal‐Covalent Organic Frameworks for Highly Efficient Photodynamic Tumor Therapy
Bibliographic record
Abstract
Abstract While metal covalent organic frameworks (MCOFs) possess excellent optical properties and electron transport capabilities, their performance in antitumor therapy is hampered by limited compositional diversity. This constraint impedes the nonradiative relaxation of excited states and reduces the efficiency of reactive oxygen species (ROS) generation. Here, we rationally designed a tri‐component electron donor–acceptor–donator (D–A–D) type MCOFs (CuTD‐COF). Compared to the two‐component D–A type MCOFs (CuT‐COF), CuTD‐COF exhibits an extended excited‐state lifetime, enhanced light‐harvesting capability due to its narrow band gap, and intensified photoradical effects, thereby achieving potent tumor suppression. Mechanistically, the D–A–D structured CuTD‐COF utilizes multiple electron‐transfer pathways. These enhance intra‐layer charge transport, reduce exciton binding energies, accelerate photogenerated carrier separation/migration, and suppress charge recombination, collectively boosting ROS generation. This yields potent photodynamic cytotoxicity in vitro and outstanding antitumor efficacy in HCT116 xenografts. Combined experimental and DFT studies reveal that distinct exciton behavior in regulating ROS generation between two D–A–D MCOFs stems from their divergent band energetics and O 2 adsorption energies. This work not only diversifies photoelectronic architectures in MCOFs but also advances cancer phototherapy development.
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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".