Determination of the pyroelectric coefficient within pyroelectric materials by using space charge measurements
Bibliographic record
Abstract
In this paper, a new theory allowed calculating the pyroelectric coefficient "p" using the diffusion of a thermal wave within materials having a pyroelectric effect. The demonstration is performed on materials composed by various percentages of a cycloaliphatic epoxy (CE) resin and of a flexibilizing comonomer (1,6-hexanediol diglycidyl ether -HDGE). By using an electric field measurement technique (the Thermal Step Method - TSM), the signal obtained on these materials (a current) presents an atypical form, which requires the modification of the theoretical model of the TSM. Indeed, the theory of the TSM technique has been initially developed for insulating materials with a high dielectric time constant. An adaptation of the technique is thus necessary to analyze the behavior of materials, which present a modification of their molecular structure during the application of the thermal wave. During a space charge characterization performed on these materials, we have observed an atypical shape for the thermal step currents. To help in the interpretation, the broadband dielectric spectroscopy and the Differential Scanning Calorimetry technique (DSC) were used to obtainadditional information. Considering these results, a circuit containing a resistor and a capacitor in series has been used for modeling the materials. This equivalent circuit constitutes the beginning of the new theory, which is developed in this paper. The pyroelectric coefficient generated by the thermal step diffusion, due to the presence of a pyroelectric effect of these studied materials, has been calculated using the detailed model
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".