Ultra‐Elastic, Durable, Bio‐Degradable, and Recyclable Pulp Foam as an Air Dielectric Substitute for Sustainable Capacitive Pressure Sensing
Bibliographic record
Abstract
Abstract Green carbon‐based cellulosic pulp foams with excellent renewable and biodegradable properties are promising alternatives to traditional petroleum‐based lightweight materials, for reducing carbon emission and plastic pollution. However, the fabrication of super‐elastic and durable pulp‐based foams for high‐value utilization remains challenging. Herein, a novel composite bio‐foam material is prepared by a simple strategy of wet foaming and ionically cross‐linking. The obtained foam assembled by cellulosic pulp fibers and polylactic acid (PLA) fibers at atmospheric pressure shows an oriented lamellar structure with interconnected macropores and super‐elastic property. The prepared PLA@Pulp‐20 foam shows a high compressive strain of up to 90% with the maximum stress of 150 kPa, while retaining ≈91% of its original height even after 30 000 compressive cycles (far superior to the reported pulp‐based foams with compressive cycles <10). Furthermore, the foam exhibits outstanding recyclability and stability in a wide range of temperature and humidity. Remarkably, the potential application of PLA@Pulp foam as a dielectric layer for capacitive sensors is first demonstrated because of its electrical non‐conductivity, and low dielectric constant (comparable to air). The corresponding device achieves non‐contact touch or contact touch sensing, demonstrating highly attractive performance in sustainable super‐elastic pressure sensing, monitoring, and beyond.
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".