Surface Modification of Poly(<scp>d</scp>,<scp>l</scp>-Lactic Acid) Scaffolds for Orthopedic Applications: A Biocompatible, Nondestructive Route via Diazonium Chemistry
Bibliographic record
Abstract
Scaffolds made with synthetic polymers such as polyesters are commonly used in bone tissue engineering. However, their hydrophobicity and the lack of specific functionalities make their surface not ideal for cell adhesion and growth. Surface modification of these materials is thus crucial to enhance the scaffold's integration in the body. Different surface modification techniques have been developed to improve scaffold biocompatibility. Here we show that diazonium chemistry can be used to modify the outer and inner surfaces of three-dimensional poly(D,L-lactic acid) (PDLLA) scaffolds with phosphonate groups, using a simple two-step method. By changing reaction time and impregnation procedure, we were able to tune the concentration of phosphonate groups present on the scaffolds, without degrading the PDLLA matrix. To test the effectiveness of this modification, we immersed the scaffolds in simulated body fluid, and characterized them with scanning electron microscopy, X-ray photoelectron spectroscopy, Raman, and infrared spectroscopy. Our results showed that a layer of hydroxyapatite particles was formed on all scaffolds after 2 and 4 weeks of immersion; however, the precipitation was faster and in larger amounts on the phosphonate-modified than on the bare PDLLA scaffolds. Both osteogenic MC3T3-E1 and chondrogenic ATDC5 cell lines showed increased cell viability/metabolic activity when grown on a phosphonated PDLLA surface in comparison to a control PDLLA surface. Also, more calcium-containing minerals were deposited by cultures grown on phosphonated PDLLA, thus showing the pro-mineralization properties of the proposed modification. This work introduces diazonium chemistry as a simple and biocompatible technique to modify scaffold surfaces, allowing to covalently and homogeneously bind a number of functional groups without degrading the scaffold's polymeric matrix.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".