MXene/Bacterial Cellulose Hybrid Materials for Sustainable Soft Electronics
Bibliographic record
Abstract
This work evaluated bacterial cellulose (BC) as a possible biodegradable soft electronics substrate in comparison to polyethylene terephthalate (PET), while also focusing on evaluating hybrid MXene/BC material as potential flexible electronic sensor. Material characterization studies revealed that the BC material structure consists of nanofibers with diameters ranging from 70 to 140 nm, stacked layer-by-layer. BC samples produced are sensitive to post-treatment with isopropanol resulting in a change of structural and mechanical properties. The viscoelastic properties of the BC substrates have been studied experimentally in comparison with the PET film. Aged BC substrate showcased similar viscoelastic properties stability, while exhibiting better properties above 70 °C, with total storage modulus change of −15% and loss modulus change of 21%. MXenes prepared using the Minimally Intensive Layer Delamination (MILD) method were screen-printed onto BC substrates and PET films to form MXene/BC (MX/BC) and MXene/PET (MX/PET) devices. The electrical properties results showcased different resistive behavior on both BC and PET substrate samples with different impedance moduli. MX/PET presented lower sheet resistance of around 156 Ω·sq−1, while MX/BC was 2733 Ω·sq−1. Finally, the MX/BC and MX/PET devices were subjected to repeatable quasi-static load tests and the piezoresistive sensing behavior of the devices has been reported.
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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".