Multilayer thick-film structures based on spinel ceramics
Bibliographic record
Abstract
Temperature-sensitive thick films based on spinel-type semiconducting ceramics of different chemical composition Cu0.1Ni0.1Co1.6Mn1.2O4 (with p+-types of electrical conductivity), Cu0.1Ni0.8Co0.2Mn1.9O4 (with p-types of electrical conductivity), and their multilayers of p+-p and p-p+-p structures were fabricated and studied. These thick-film elements possess good electrophysical characteristics before and after long-term ageing test at 170 °C. It is shown that degradation processes connected with diffusion of metallic Ag into grain boundaries occur in one-layer p- and p+-conductive thick films. The p+-p structures were of high stability, the relative electrical drift being no more than 1%. Positron trapping processes in so-called “free” thick-film structures based on spinel-type Cu0.1Ni0.8Co0.2Mn1.9O4 ceramics are studied. It is shown that two-state positron trapping model is appropriate for an adequate description of changes caused by additional glass phase in these materials. The observed behaviour of defect-related component in the fit of the experimentally measured positron lifetime spectra for thick films in comparison with bulk ceramics testifies in favour of agglomeration of free volume entities during technological process.
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 | 0.000 |
| 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".