Emerging 2D Nanomaterials‐Integrated Hydrogels: Advancements in Designing Theragenerative Materials for Bone Regeneration and Disease Therapy
Why this work is in the frame
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Bibliographic record
Abstract
This review highlights recent advancements in the synthesis, processing, properties, and applications of 2D-material integrated hydrogels, with a focus on their performance in bone-related applications. Various synthesis methods and types of 2D nanomaterials, including graphene, graphene oxide, transition metal dichalcogenides, black phosphorus, and MXene are discussed, along with strategies for their incorporation into hydrogel matrices. These composite hydrogels exhibit tunable mechanical properties, high surface area, strong near-infrared (NIR) photon absorption and controlled release capabilities, making them suitable for a range of regeneration and therapeutic applications. In cancer therapy, 2D-material-based hydrogels show promise for photothermal and photodynamic therapies, and drug delivery (chemotherapy). The photothermal properties of these materials enable selective tumor ablation upon NIR irradiation, while their high drug-loading capacity facilitates targeted and controlled release of chemotherapeutic agents. Additionally, 2D-materials -infused hydrogels exhibit potent antibacterial activity, making them effective against multidrug-resistant infections and disruption of biofilm generated on implant surface. Moreover, their synergistic therapy approach combines multiple treatment modalities such as photothermal, chemo, and immunotherapy to enhance therapeutic outcomes. In bio-imaging, these materials serve as versatile contrast agents and imaging probes, enabling their real-time monitoring during tumor imaging. Furthermore, in bone regeneration, most 2D-materials incorporated hydrogels promote osteogenesis and tissue regeneration, offering potential solutions for bone defects repair. Overall, the integration of 2D materials into hydrogels presents a promising platform for developing multifunctional theragenerative biomaterials.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it