Surface-Engineered WS<sub>2</sub> Nanohybrids for Implications in Biomedicine
Bibliographic record
Abstract
Abstract Transition metal dichalcogenides (TMDs) nanosheets, known for their distinctive structural and physicochemical characteristics, have become valuable tools in various biomedical fields, including drug delivery and tissue engineering. Here, we have developed a facile approach to synthesize surface-modified TMD nanosheets that exhibit several smart properties, such as near-infrared (NIR) light-responsiveness, ultrasound-responsiveness, and bactericidal behavior. The surface modification was performed using a redox reaction, which decorated liquid-exfoliated, 2D, ultrathin nanosheets of tungsten disulfide (WS2) with silver nanospheres. TEM and AFM images, along with analytical techniques such as XPS, FTIR, powder-XRD, UV–vis, and Confocal Raman spectroscopy, confirmed the binding of silver to the nanosheets, resulting in heterostructured nanohybrids (nWS2). Additional structural information about this surface-engineered material was obtained using synchrotron radiation-based instrumentation techniques, including X-ray absorption fine structure spectroscopy (XAFS). Moreover, we demonstrate that nWS2 nanohybrids are capable of inhibiting biofilms of methicillin-resistant Staphylococcus aureus (MRSA), a widely prevalent causative agent of healthcare-associated bacterial infections. The nanohybrids can also convert incident near-infrared (NIR) light to thermal energy and exhibit enhanced bactericidal potential. 1 mg/mL of nWS2 was able to increase suspension temperatures by 30 °C. A colony forming unit assay with NIR-exposed nWS2 showed antibiotic-free prevention of MRSA growth. Next, we develop a nWS2-integrated polymeric hydrogel system capable of 3D-biopriting hydrogel structures with user-defined geometry for tissue engineering applications. Finally, we evaluate the in vitro cytocompatibility and in vivo biocompatibility of this nanocomposite hydrogel platform by subcutaneously implanting it in immunocompetent mice. Histological staining revealed excellent host-tissue integration, vasculogenesis, and a minimal immune response around the implant’s periphery. Taken together, we envision surface-engineered WS2 nanosheets, alone or in combination with hydrogels, as a high-performance multifunctional biomaterial for implications in biomedicine.
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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".