Cellulosic Nonwovens Incorporated with Fully Utilized MXene Precursor as Smart Pressure Sensor and Multi‐Protection Materials
Bibliographic record
Abstract
Abstract Currently, multifunctional MXene‐integrated wearable textiles (MWTs) are particularly appealing due to their various applications such as health monitoring, smart protection, and medical treatment. However, scalable manufacture of durable, stable, and high‐performance multifunctional MWTs still face challenges due to the poor oxidation stability of MXene and low utilization of precursor titanium aluminum carbide (MAX). Herein, an improved preparation strategy for zinc ion (Zn 2+ ) intercalation is proposed to create high antioxidative MXene (ZM) and exceptionally conductive and printable gel ink based on MXene sediments (ZMS‐ink), while multifunctional wearable textiles are fabricated using spray‐coating and screen‐printing techniques on cellulosic nonwoven textiles (CNWs), achieving the complete utilization of MXene precursor. Benefiting from the inherent disordered stacking and porous structure of CNWs, along with the highly conductive ZM and ZMS‐ink, as‐prepared smart, wearable and green‐based pressure sensor offered proper breathability, high sensitivity (2602.26 kPa −1 ), wide sensing range (0–141 kPa), and excellent cycling stability (>5000 cycles). Additionally, the sensor exhibited efficient photothermal/photodynamic therapy antibacterial activity and exceptional electromagnetic interference shielding performance (57.5 dB). Therefore, this work paves the way for the future development of integrated and scalable multifunctional wearable devices building on the environmental‐friendly CNWs incorporated with fully utilized MAX, offering a green and cost‐effective approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".