Bibliographic record
Abstract
The remanent polarization at zero electric field and the capability of being switched between multiple stable states make ferroelectric materials good candidates for non-volatile memories. Practically, ferroelectric materials are commercially available and have been applied to ferroelectric random-access memories (FeRAM) for computers and radio frequency identification (RFID) cards.\nFor application in data storage, three factors are crucial: (i) a fast writing/reading speed, (ii) a reliable data retention, and (iii) a slow degradation during a large number of writing/reading cycles. These challenges have attracted a wide research interest from both industry and academia, since they are not only of practical interests but trigger intriguing fundamental questions related to the ferroelectric materials as well. \nIt is the major scope of this thesis to study the polarization-related device physics motivated by the aforementioned practical requirements. We focus on thin films of the ferroelectric polymer poly-vinylidene-fluoride (PVDF) and its random copolymers with trifluoroethylene [P(VDF-TrFE)]. We shall show that the conclusions derived also apply to inorganic ferroelectric materials such as Pb(Zr,Ti)O3, and BaTiO3. Our investigation is based on macroscopic electrical measurements and nanoscale scanning probe measurements. Modeling at mesoscopic level is involved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".