Leveraging Liquid–Liquid Interfaces in a 3D-Printable Reactor to Form Sub-Micron Freestanding Membrane Selective Layers
Bibliographic record
Abstract
Fabricating ultrathin, defect-free polymer nanofilms on varying substrates remains a persistent challenge in thin-film composite (TFC) membrane development, particularly when substrate interference, solution intrusion, and film transfer limit reproducibility and performance. We introduce a modular, cost-effective, solvent-compatible, 3D-printed reactor─FIPzR (Freestanding Interfacial Polymerization Reactor)─designed to fabricate thin, defect-free polymer films at liquid–liquid interfaces. Using iterative CAD-based design and additive manufacturing (AM), the device is engineered to decouple film formation from the underlying substrate, enabling reproducible fabrication of high-quality films with controlled morphologies and direct transfer onto both porous and nonporous substrates using a floating guide ring. The reactor accommodates multiple fabrication strategies, demonstrated here through interfacial polymerization to synthesize polyamide (PA) membranes of varying morphologies, drop casting of a preformed polysulfone (PSU) solution, and curing of a reactive polydimethylsiloxane (PDMS) mixture─with thicknesses spanning ultrathin (<20 nm) to submicron scales. The desalination performance of smooth and rough PA membranes was evaluated in a custom-built crossflow setup under standard brackish water reverse osmosis (RO) conditions, exhibiting water permeance and salt rejection characteristics in line with standard RO membranes. PSU and PDMS membranes were tested in a custom-built gas separation setup to verify structural integrity and defect-free film quality, showing CO 2 /N 2 selectivity consistent with reported literature benchmarks. The FIPzR offers a reproducible and substrate-independent polymer nanofilm fabrication, with potential utility in membrane separations, coatings, flexible electronics, and sensing technologies.
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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) | 0.001 | 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 | 0.001 | 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".