Nanodiamond/Ti <sub>3</sub> C <sub>2</sub> MXene‐coated quartz crystal microbalance humidity sensor with high sensitivity and high quality factor
Bibliographic record
Abstract
Abstract To address the challenge of achieving both high sensitivity and a high quality factor in quartz crystal microbalance (QCM) humidity sensors, a nanodiamond (ND)/Ti 3 C 2 MXene composite‐coated QCM humidity sensor was fabricated. The material characteristics of ND, Ti 3 C 2 MXene, and ND/Ti 3 C 2 MXene composite were analyzed by transmission electron microscopy (TEM) and Fourier transform infrared (FTIR) spectroscopy. The experimental results demonstrated that the hydrophilic ND nanoparticles coated on Ti 3 C 2 MXene nanosheet prevented the self‐stacking of Ti 3 C 2 MXene and enhanced the sensitivity of Ti 3 C 2 MXene‐based QCM humidity sensor. Moreover, the high mechanical modulus of Ti 3 C 2 MXene material helped ND/Ti 3 C 2 MXene composite‐coated QCM humidity sensor to achieve a high quality factor (> 20,000). ND/Ti 3 C 2 MXene composite‐coated QCM humidity sensor exhibited a sensitivity of 82.45 Hz/%RH, a humidity hysteresis of 1.1%RH, fast response/recovery times, acceptable repeatability, and good stability from 11.3%RH to 97.3%RH. The response mechanism of ND/Ti 3 C 2 MXene composite‐coated QCM humidity sensor was analyzed in combination with a bi‐exponential kinetic adsorption model. Finally, the potential application of ND/Ti 3 C 2 MXene composite‐coated QCM humidity sensor was demonstrated through its frequency response to wooden blocks with different moisture contents.
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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".