Effect of Different Post-Curing Methods on the Degree of Conversion of 3D-Printed Resin for Models in Dentistry
Bibliographic record
Abstract
The aim was to investigate the effects of different post-curing units on the chemical properties (degree of conversion) of 3D-printed resins for producing models in dentistry. The goal is to determine whether less-expensive post-curing units can be a viable alternative to the manufacturer’s recommended units. Forty-five samples were fabricated with an LCD printer (Phrozen Sonic Mini, Phrozen 3D, Hsinchu City, Taiwan) using MSLA Dental Modeling Resin (Apply Lab Work, Torrance, CA, USA). These samples were divided randomly into four different groups for post-curing using four distinct curing units: Phrozen Cure V2 (Phrozen 3D, Hsinchu City, Taiwan), a commercial acrylic nail UV LED curing unit (SUNUV, Shenzhen, China), a homemade curing unit created from a readily available UV LED light produced (Shenzhen, China), and the Triad® 2000™ tungsten halogen light source (Dentsply Sirona, York, PA, USA). The degree of conversion was measured with FTIR spectroscopy using a Nicolet 6700 FTIR Spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Phrozen Cure V2 had the highest overall mean degree of conversion (69.6% with a 45 min curing time). The Triad® 2000 VLC Curing Unit had the lowest mean degree of conversion value at the 15 min interval (66.2%) and the lowest mean degree of conversion at the 45 min interval with the homemade curing unit (68.2%). The type of light-curing unit did not yield statistically significant differences in the degree of conversion values. There was a statistically significant difference in the degree of conversion values between the 15 min and 45 min curing intervals. When comparing individual light-curing units, there was a statistically significant difference in the degree of conversion for the post-curing units between the 15 min and 45 min curing time (p = 0.029).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".