Effect of Water Vapor Adsorption on Electrical Properties of Carbon Nanotube/Nanocrystalline Cellulose Composites
Bibliographic record
Abstract
It has been long known that the electrical properties of cellulose are greatly influenced by adsorption of water vapor. Incorporating conductive nanofillers in a cellulose matrix is an example of an approach to tailor their characteristics for use in electronics and sensing devices. In this work, we introduce two new nanocomposites comprising carbon nanotubes (CNTs) and conventional or electrosterically stabilized nanocrystalline celluloses matrices. While conventional nanocrystalline cellulose (NCC) consists of a rigid crystalline backbone, electrosterically stabilized cellulose (ENCC) is composed of a rigid crystalline backbone with carboxylated polymers protruding from both ends. By tuning CNT loading, we can tailor a CNT/NCC composite with minimal electrical sensitivity to the ambient relative humidity, despite the fact that the composite has a high moisture uptake. The expected decrease in CNT conductivity upon water vapor adsorption, due to electron donation, is counterbalanced by an increase in the conductivity of NCC due to proton hopping at an optimum CNT loading (1-2%). Contrary to the CNT/NCC composite, a CNT/ENCC composite at 1% CNT loading shows insulating behavior for relative humidities up to 75%, after which the composite becomes conductive. This interesting behavior can be ascribed to the low moisture uptake of ENCC at low and moderate relative humidities due to the limited number of hydroxyl groups and hydrogen bond formation between carboxyl groups on ENCC, which endow ENCC with limited water molecule adsorption sites.
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 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".