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Record W4408237940 · doi:10.1021/acsabm.4c01803

From Unprintable Peptidic Gel to Unstoppable: Transforming Diphenylalanine Peptide (Fmoc-FF) Nanowires and Cellulose Nanofibrils into a High-Performance Biobased Gel for 3D Printing

2025· article· en· W4408237940 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACS Applied Bio Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsInnovation Cluster (Canada)
FundersFachagentur Nachwachsende Rohstoffe
KeywordsCellulosePeptideNanowireMaterials scienceSelf-assembling peptideChemical engineeringPolymer chemistryNanotechnologyChemistryNanofiberOrganic chemistryBiochemistry

Abstract

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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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.231
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it