Preparation and characterization of CuO nanopowders doped (Ba_(0.87)Ca_(0.09)Sr_(0.04))(Ti_(0.90)Zr_(0.04)Sn_(0.06))O_3-based Y5V ceramics
Bibliographic record
Abstract
Aim To study the effects of different amount of CuO nano-powders and sintering temperature on the microstructures and the dielectric properties of BCSTZS ceramics by preparing(Ba0.87Ca0.09Sr0.04)(Ti0.90Zr0.04Sn0.06)O3 ceramics(BCSTZS) with CuO nano-powders as sintering aids.Methods A series of BCSTZS ceramics as samples were synthesized by doping CuO nano-powders with solid phase method,then not only were the samples characterized with XRD,TEM and SEM methods but also dielectric properties of the ceramics were measured.Results The density and dielectric constant of ceramics increased obviously with the increase of the Nb content in the low-temperature sintering.In all the additive systems,the single addition of CuO nano-powders was an effective way to lower the sintering temperature of BCSTZS ceramics from 1 300 ℃ to 1 150 ℃.When CuO nano-powders of 1.5%(wt/wt) was mixed with BCSTZS powders,the derived ceramics demonstrated dense microstructure with a high dielectric constant(emax=8 690),low dielectric loss 1.67% and met the EIA Y5V standard.Conclusion The ceramics synthesized by doping CuO nano-powders with solid phase method feature less pores,higher density,more uniform grain size and homogeneous distribution.The method can be used to prepare the BSCTZS-based ceramics which meet Y5V standard,so this study has important application prospect.
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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".