Correlation between Critical Energy, Penetration Depth, and Photopolymerization Kinetics in Aluminum–Phosphate–Silicate Hybrid Materials for Vat Photopolymerization
Bibliographic record
Abstract
The photopolymerization of aluminum–phosphate–silicate resins obtained from the hybrid sol–gel route for Vat photopolymerization (VPP) process was investigated. The printing parameters derived from Jacob’s work curve model, critical energy ( E c ) and penetration depth ( D p ), were determined as a function of laser power and MPTMS (silicate) concentration for materials with stoichiometry Si ( x ) -(Al + P) (1– x ), 0 ≤ x ≤ 0.7. The kinetics of photopolymerization was further explored using steady- and unsteady-state photo-DSC experiments. The oxygen inhibition and primary termination had similar contributions to the polymerization process for all compositions, while the propagation and bimolecular termination constants increased with MPTMS concentration. These experimental results were used to test the validity of the E c ∝ k t 1/2 / k p and D p ∝ ϵ relationship derived from a photochemical model for VPP assuming steady-state kinetics. Both E c ∝ ( k t 1 / 2 / k p ) and D p ∝ ϵ may be used to predict critical energy and penetration values for an arbitrary resin without calculating its work curve function, according to our study.
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.
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 | 0.001 |
| 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".