Chitin Nanofibers Enable the Colloidal Dispersion of Carbon Nanomaterials in Aqueous Phase and Hybrid Material Coassembly
Bibliographic record
Abstract
Chitin nanofibrils (ChNF) sourced from discarded marine biomass are shown as effective stabilizers of carbon nanomaterials in aqueous media. Such stabilization is evaluated for carbon nanotubes (CNT) considering spatial and temporal perspectives by using experimental (small-angle X-ray scattering, among others) and theoretical (atomistic simulation) approaches. We reveal that the coassembly of ChNF and CNT is governed by hydrophobic interactions, while electrostatic repulsion drives the colloidal stabilization of the hybrid ChNF/CNT system. Related effects are found to be transferable to multiwalled carbon nanotubes and graphene nanosheets. The observations explain the functionality of hybrid membranes obtained by aqueous phase processing, which benefit from an excellent areal mass distribution (correlated to piezoresistivity), also contributing to high electromechanical performance. The water resistance and flexibility of the ChNF/CNT membranes (along with its tensile strength at break of 190 MPa, conductivity of up to 426 S/cm, and piezoresistivity and light absorption properties) are conveniently combined in a device demonstration, a sunlight water evaporator. The latter is shown to present a high evaporation rate (as high as 1.425 kg water m –2 h –1 under one sun illumination) and recyclability.
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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".