Memory effect in composites of liquid crystal and silica aerosil
Bibliographic record
Abstract
Aerosil silica nanoparticles dispersed in a liquid crystal (LC) possess the interesting property of keeping memory of an electric- or magnetic-field-induced orientation. Two types of memory have been identified: thermally erasable memory arising from the pinning of defect lines versus a "permanent" memory where the orientation persists even after thermal cycling the samples up to the isotropic phase. To address the source of the latter type of memory, solid-state nuclear magnetic resonance spectroscopy and conventional x-ray diffraction (XRD) were first combined to characterize the LC orientational order as a function of multiple in-field temperature cycles. Microbeam XRD was then performed on aligned gels of different concentrations to gain knowledge of the structural properties at the origin of the memory effect. No detectable anisotropy of the gel or significant breaking of silica strands with heating ruled out the formation of an anisotropic silica network as the source of the permanent memory as previously proposed. Instead, support for a role of the surface memory effect, well known for planar substrates, in stabilizing the permanent memory was deduced from "training" of the composites, that is, optimizing the orientational order through the thermal in-field cycling. The ability to train the composites is inversely proportional to the strength of the random-field disorder. The portion of thermally erasable memory also decreases as the silica density increases. We propose that the permanent memory originates from the surface memory effect operating at points of intersection in the silica network. These areas, where the LC is strongly confined with conflicted surface interactions, are trained to achieve an optimized orientation and subsequently act as sites from which the LC orientational order regrows after zero-field thermal cycling up to the isotropic phase.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".