High Performance Near-Infrared (NIR) Photoinitiating Systems Operating under Low Light Intensity and in the Presence of Oxygen
Bibliographic record
Abstract
Photopolymerization under near-infrared (NIR) light is challenging due to the low energy of the absorbed photon but, if successful, presents significant advantages. For example, this lower energy wavelength is safer than UV light that is currently the standard photocuring light source. Also, NIR allows for a deeper light penetration within the material and therefore resulting in a more complete curing of thicker materials containing fillers for access to composites. In this study, we report the use of three-component systems for the NIR photopolymerization of methacrylates: (1) a dye used as a photosensitizer in the NIR range, (2) an iodonium salt as a photoinitiator for the free radical polymerization of the (meth)acrylates, and (3) a phosphine to prevent polymerization inhibition due to the oxygen and to regenerate the dye upon irradiation. Several NIR-absorbing dyes such as a cyanine borate and a silicon–phthalocyanine are presented and studied. Systems using borate dyes resulted in methacrylate monomer conversion over 80% in air. We report three types of irradiation system: low-power LED at 660 and 780 nm as well as a higher power laser diode at 785 nm. The excellent performance reported in this work is due to the crucial role of the added phosphine.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".