Functionalization of calcium-deficient nanohydroxyapatite improves the mechanical properties of 3D printed biopolymer nanocomposites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Agglomerations of nanoparticles in a polymer matrix can drastically reduce the mechanical properties of a polymer nanocomposite , especially its strength . The grafting of nanoparticle surfaces with suitable functional groups can provide improved dispersion and stronger interfacial bonding, improving the fracture resistance of the nanocomposite. In this study, calcium-deficient nanohydroxyapatite (nHA) particles were functionalized with an amino acid-based urethane methacrylate (lysine urethane methacrylate, LUM) and subsequently reacted with hydroxyethyl methacrylate . We mixed these functionalized nHA particles with resin, composed of methacrylated acrylated epoxidized soybean oil, methacrylated isosorbide , and triethylene glycol dimethacrylate , and 3D-printed nanocomposites using masked stereolithography . We hypothesized that the functionalized nanoparticles would enhance the mechanical performance of the 3D-printed nanocomposites due to the greater dispersion and stronger interface. Flexural, tensile, compression and Mode-I fracture toughness test specimens were fabricated using a mSLA printer and tested following ASTM standards. The LUM functionalization of nHA improved the dispersion and increased the viscosity of the uncured nanocomposite ink. The flexural fracture strength, yield strength, and mode-I fracture toughness values were increased by 10 %, 30 %, and 11 %, respectively. The LUM improved the strength and fracture toughness by providing a stronger, more stable interface, resisting debonding between the matrix and particles, allowing for greater plastic deformation .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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