Laser Sintering of CNT/PZT Composite Film
Bibliographic record
Abstract
The discovery of piezoelectricity inspired several sensing applications. For these applications, the thinness and flexibility of the device increase the range of implementations. A thin lead zirconate titanate (PZT) ceramic piezoelectric sensor is advantageous compared with bulk PZT or a polymer when it comes to having minimal impacts on dynamics and high-frequency bandwidth provided by low mass or high stiffness, while satisfying constraints regarding tight spaces. PZT devices have traditionally been thermally sintered inside a furnace and this process consumes large amounts of time and energy. To overcome such challenges, we employed laser sintering of PZT that focused the power onto selected areas of interest. Furthermore, non-equilibrium heating offers the opportunity to use low-melting-point substrates. Additionally, carbon nanotubes (CNTs) were mixed with PZT particles and laser sintered to utilize the high mechanical and thermal properties of CNTs. Laser processing was optimized for the control parameters, raw materials and deposition height. A multi-physics model of laser sintering was created to simulate the processing environment. Sintered films were obtained and electrically poled to enhance the piezoelectric property. The piezoelectric coefficient of laser-sintered PZT increased by approximately 10-fold compared with unsintered PZT. Moreover, CNT/PZT film displayed higher strength compared with PZT film without CNTs after the laser sintering while using less sintering energy. Thus, laser sintering can be effectively used to enhance the piezoelectric and mechanical properties of CNT/PZT films, which can be used in various sensing applications.
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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) | 0.002 | 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".