3D Printing of High Strength Thermally Stable Sustainable Lightweight Corrosion-Resistant Nanocomposite by Solvent Exchange Postprocessing
Bibliographic record
Abstract
3D printing of cellulose acetate (CA)-based, sustainable, thermally stable nanocomposites is challenging due to their fast drying, nozzle clogging, and complex rheological behavior. In this study, we developed an extrusion-printable ink based on CA and cellulose nanocrystals (CNCs) using a trisolvent wet blending method, followed by a postprinting pore-inducing processing technique. The nanocomposites performed well in both tensile- and compression-based mechanical tests. Moreover, the nanocomposites demonstrated malleable deformation during compression testing without any premature fracture, unlike commercial commodity plastics. The thermal stability was assessed using thermogravimetric analysis, showing a ∼ 28 °C improvement in the onset degradation temperature after the addition of 5 wt % CNCs. Solvent tolerance tests against various solvents indicated excellent solvent resistance. The lightweight nanocomposites showed no deterioration, even after long-term exposure to water vapor. Finally, the anticorrosion behavior of the samples was evaluated as a coating material for metal (Al), demonstrating excellent protection against corrosive acid vapors. Thus, the application of 3D-printed CA material exhibits significant promise for implementation in the fields of lightweight, sustainable, and anticorrosive engineering materials.
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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 | 0.000 | 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".