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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".