From Unprintable Peptidic Gel to Unstoppable: Transforming Diphenylalanine Peptide (Fmoc-FF) Nanowires and Cellulose Nanofibrils into a High-Performance Biobased Gel for 3D Printing
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The growing interest in gel-based additive manufacturing, also known as three-dimensional (3D) gel-printing technology, for research underscores the crucial need to develop robust biobased materials with excellent printing quality and reproducibility. The main focus of this study is to prepare and characterize some composite gels obtained with a low-molecular-weight gelling (LMWG) peptide called Fmoc-diphenylalanine (Fmoc-FF) and two types of cellulose nanofibrils (CNFs). The so-called Fmoc-FF peptide has the ability to self-assemble into a nanowire shape and therefore create an organized network that induces the formation of a gel. Despite their ease of preparation and potential use in biological systems, unfortunately, those Fmoc-FF nanowire gel systems cannot be 3D printed due to the high stiffness of the gel. For this reason, this study focuses on composite materials made of cellulose nanofibrils and Fmoc-FF nanowires, with the main objective being that the composite gels will be suitable for 3D printing applications. Two types of cellulose nanofibrils are employed in this study: (1) unmodified pristine cellulose nanofibrils (uCNF) and (2) chemically modified cellulose nanofibrils, which ones have been grafted with polymers containing the Fmoc unit on their backbone (CNF- g -Fmoc). The obtained products were characterized through solid-state cross-polarization magic angle-spinning 1 H NMR and confocal laser scanning microscopy. Within these two CNF structures, two composite gels were produced: uCNF/Fmoc-FF and CNF- g -Fmoc/Fmoc-FF. The mechanical properties and printability of the composites are assessed using rheology and challenging 3D object printing. With the addition of water, different properties of the gels were observed. In this instance, CNF- g -Fmoc/Fmoc-FF ( c = 5.1%) was selected as the most suitable option within this product range. For the composite bearing uCNF, exceptional print quality and mechanical properties are achieved with the CNF/Fmoc-FF gel ( c = 5.1%). The structures are characterized by using field emission scanning electron microscopy (FESEM) and small-angle X-ray scattering (SAXS) measurements.
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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 | 0.001 |
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".