Improving the energy storage performance of BaTiO3-based glass ceramics by reconstituting glass network structure via electronegativity tuning
Bibliographic record
Abstract
Developing dielectric capacitors with both excellent recoverable energy storage density (Wrec) and high dielectric breakdown strength (DBS) are highly desired for pulsed power electronic systems. Although glass ceramics are known to potentially possess simultaneously a high DBS and a relatively high dielectric constant (εr), it is still a long-standing challenge to obtain high energy storage performance in glass ceramics. In this work, based on the consideration of electronegativity and its effects on the degree of polymerization, SnO2 addictive was introduced to reconstitute the parent glass network structure and thereby an ultra-high DBS value of 2,809 kV/cm was achieved in the SnO2-doped parent glass. After crystallization of the SnO2-doped parent glass, an ultrahigh Wrec of 10.13 J/cm3 with an efficiency (η) of 85.5% and a superb discharge energy storage density (Wd) of 9.09 J/cm3 at 1,500 kV/cm were obtained in the BaTiO3-based glass ceramic. Meanwhile, this BaTiO3-based glass ceramic displays a good thermal stability over a wide temperature range of 30–120 °C, with the Wrec only decreasing by 3.0% and Wd dropping from 4.40 J/cm to 3.53 J/cm at 800 kV/cm. Furthermore, it also exhibits high optical transmittance (about 60%) in the visible light spectrum. These features indicate that the BaTiO3-based glass ceramic studied in this work has a great potential not only for high-pulsed power applications but also for optical applications, making it a truly multifunctional material.
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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".