Plasmon-boosted titanium nitride-based nanoplatform for synergistic photothermal-chemodynamic cancer therapy with smart degradability
Bibliographic record
Abstract
The development of multifunctional nanoplatforms offers promising strategies for advancing cancer treatment, given the generally limited efficacy of single function nanomaterial-based therapeutics. Herein, a plasmon-enhanced “sandwich-like” nanoplatform, titanium nitride@mesoporous silica-iron oxide/polyethylenimine (TiN@mSiO 2 -Fe 3 O 4 /PEI), is designed for synergistic photothermal and chemodynamic therapy (PTT/CDT). The core comprises multiple TiN nanoparticles exhibiting strong plasmon coupling, while the mSiO 2 shell is decorated with ultrasmall, surface-exposed Fe 3 O 4 nanozymes (∼3.2 nm) to facilitate catalytic reactions with tumor-associated substrates. Under near-infrared irradiation, the nanoplatform demonstrates a favorable photothermal conversion efficiency (∼39.3%), making it well-suited for mild-temperature PTT. Meanwhile, the localized heat generated by TiN effectively enhances the catalytic activity of adjacent Fe 3 O 4 nanozymes, thereby promoting hydroxyl radical production and intracellular glutathione depletion. The synergistic photothermal-catalytic interactions within TiN@mSiO 2 -Fe 3 O 4 /PEI result in augmented therapeutic effect by combining efficient PTT with intensified CDT by in situ thermally accelerated Fenton reactions. This is evidenced by >90% cancer cell killing efficiency in vitro and ∼96% tumor inhibition rate in MOC1 xenograft models. Moreover, the mSiO 2 shell, with its large mesopores, exhibits pH-responsive degradability that enables controlled Fe 3 O 4 release in the acidic tumor microenvironment, which in turn improves therapeutic specificity and reduces systemic toxicity. Collectively, these results demonstrate the potential of TiN@mSiO 2 -Fe 3 O 4 /PEI as a highly effective and versatile nanoplatform for advanced cancer nanotherapy. • Tailored nanostructure enhances localized plasmonic heating and nanozyme catalysis. • Thermally boosted nanozyme activity amplifies ROS generation for effective CDT. • Multifunctional nanoplatform achieves synergistic photothermal-chemodynamic therapy. • pH-responsive mSiO 2 degradation enables tumor-specific nanozyme release. • Nanoplatform yields >90% cancer cell killing and ∼96% tumor inhibition in vivo.
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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 | 0.001 | 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".