New approach to 3D-print methacrylic acid/polyethylene glycol diacrylate-based membranes based on polymerization induced phase separation
Bibliographic record
Abstract
We present a novel freeform fabrication strategy for polymer membranes via 3D printing, in which porosity is generated through a polymerization-induced phase separation mechanism. The approach employs a vat-photopolymerization compatible formulation to produce a macroporous network with coexisting mesoporosity. Hydrophilic flat-sheet membranes (400 μm thick) were fabricated and subjected to post-treatments involving CO 2 supercritical drying and solvent exchange. The resulting membranes exhibited permeance of up to 36 and 29 LMH.bar −1 , respectively, under a transmembrane pressure of 2 bar. An in-depth evaluation was conducted on membranes produced from photocurable formulations containing methacrylic acid (MA, 30 vol%) and polyethylene glycol diacrylate (PEGDA, 20 vol%), with varying ratios of 1-butanol and 2-phenoxyethanol as porogens. SEM analysis revealed a porous morphology throughout the membrane cross-section. The top-performing sample exhibited the highest specific surface area of 12.8 m 2 /g and the smallest mean pore diameters of 45 nm and 42 nm, as determined by adsorption branches of Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH) analyses, respectively. Pore size distribution was compared with liquid-liquid displacement method, which revealed mean pore size of 8.8 nm, with a small quantity of pores of approximately 121 nm, which were in better agreement with pure water flux values. The influence of post-polymerization porogen removal methods—solvent exchange and supercritical drying—was also assessed. Post-treated membranes demonstrated lower compaction factors and more stable flux over time and under increasing pressure. This technique offers a promising route for the customized design of membrane geometries and architectures tailored to specific separation challenges. We further demonstrate such concept by printing textured membranes with wavy and rugged surface profiles.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 teacher head, 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".