Two-dimensional superlattice films of gold nanoparticle-polystyrene composites: a bioactive platform for bone homeostasis maintenance
Bibliographic record
Abstract
Abstract Osseo-integration between the implant and bone is a crucial factor to create a strong, durable bond that allows the implant to function effectively. However, regular implant surface with poor osseo-integration ability may cause aseptic loosening, resulting in the failure of implants. Herein, a serial of macroscopic one-particle thick superlattice films generated by self-assembly of diverse size of gold nanoparticles (GNPs) were termed as SFGs and were considered as bioactive implant coatings for enhancing osseo-integration. A hydroquinone-assisted seed method is established to fabricate homogenous GNPs with controllable sizes (20, 60, and 90 nm), which were further employed as building blocks to generate macroscopic one-particle thick superlattice films of GNPs (SFGs-20, SFGs-60, and SFGs-90) with the assistance of ploystryrene. The SFGs present a size-dependent performance on bone homeostasis, where SFGs-90 demonstrated the most pronounced facilitation of osteogenic differentiation of osteoblasts as well as deactivation of osteoclasts compared with SFGs-20 and SFGs-60. Considering the universal applicability of SFGs for depositing on various substrates, these SFGs with enhanced osseo-integration capabilities could serve as a bioactive platform for surface modification of orthopedic implants, effectively addressing the issue of aseptic loosening. Graphical abstract Two-dimensional superlattice films of gold nanoparticle-polystyrene composites exhibit enhanced osteogenic-stimulation and osteoclastic-inhibition effects for regulating bone homeostasis maintenance.
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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".