Biocompatible polymers with tunable mechanical properties and conductive functionality on two-photon 3D printing
Bibliographic record
Abstract
Two-photon polymerization (TPP)-based 3D printing technology utilizes the two-photon absorption process of near-infrared radiation, enabling the fabrication of micro- and nano-scale three-dimensional structures with extremely high resolution. It has been widely applied in scientific fields closely related to living organisms, such as tissue engineering, drug delivery, and biosensors. Nevertheless, the existing photoresist materials have poor mechanical tunability and are hardly able to be doped with functional materials, resulting in constraints on the preparation of functional devices with micro-nano structures. In this paper, TPP printable polymer formulas with good mechanical tunability, high resolution, strong functional scalability, and excellent biocompatibility are proposed, by using the synergistic effects of a hydroxyl group-containing photocurable resin prepolymer, UV acrylate monomer, long-chain hydrophilic crosslinking monomer and photo-initiator. This can ensure the printability and help to improve the flexibility of the printed polymer, thereby solving the problem the photosensitive materials suitable for two-photon 3D printing in previous research had in balancing the formability and flexibility. The results of nanoindenter analysis showed that the Young's modulus of the printed structure can be adjusted between 0.3 GPa and 1.43 GPa, realizing mechanical tunability. Also, complex structures, such as micro-scaffold structures and high aspect ratio hollow microneedles were printed to explore the structural stability as well as the feasibility of biodevice application. Meanwhile, the proposed polymer formula can be functionalized to be conductive by doping with functional nanomaterial MXene. Finally, the biocompatibility of the proposed polymer formula was studied by culturing with human normal lung epithelial cells. The results indicated a good potential for biodevice applications.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".