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Record W4312094211 · doi:10.1021/acs.biomac.2c01241

Mapping 3D Printability of Ionically Cross-Linked Cellulose Nanocrystal Inks: Architecting from Nano- to Macroscale Structures

2022· article· en· W4312094211 on OpenAlexafffund
Majed Amini, Milad Kamkar, Farhad Ahmadijokani, Saeed Ghaderi, Orlando J. Rojas, Hadi Hosseini, Mohammad Arjmand

Bibliographic record

VenueBiomacromolecules · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of WaterlooUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsRheologyMaterials scienceNanocrystal3D printingNanotechnologyElectrolyteCelluloseSalt (chemistry)Nano-ColloidChemical engineeringComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Engineering the rheological properties of colloidal inks is one of the main challenges in achieving high-fidelity 3D printing. Herein, we provide a comprehensive study on the rheological behavior of inks based on cellulose nanocrystals (CNCs) in the presence of given salts to enable high-quality 3D printing. The rheological properties of the CNC suspensions are tailored by considering the nature of the electrolyte (i.e., 10 types of salts featuring different ion sizes, charge numbers, and inter- and intra-molecular interactions) at various concentrations (25-100 mM). A high printing fidelity is achieved in a narrow CNC and salt concentration range, significantly depending on the salt type. The structure-property relationship is explored in a "3D-printing" space (2D map), introducing a guideline for researchers active in this field. To further unravel the effect of salt type on morphological properties, CNC aerogels are developed by freeze-drying the printed structures. The results illustrate that enhancing viscoelastic properties render a denser structure featuring smaller pores.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.300
Teacher spread0.280 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2022
Admission routes2
Has abstractyes

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