Stimuli Responsive Polymer-Based 3D Optical Crystals for Sensing
Bibliographic record
Abstract
3D optical crystals have found their applications in sensing, actuation, optical devices, batteries, supercapacitors, etc. The 3D optical crystal devices are comprised of two main components: colloidal gels and nanoparticles. Nanoparticles self-assemble into face center cubic structures in colloidal gels. The inherent 3D optical crystal structure leads to display of structural colors on these devices following light impingement. As such, these optical properties have led to the utilization of these 3D optical crystals as self-reporting colorimetric sensors, which is the focus of this review paper. While there is extensive work done so far on these materials to exhaustively be covered in this review, we focus here in on: mechanism of color display, materials and preparation of 3D optical crystals, introduction of recent sensing examples, and combination of 3D optical crystals with molecular imprinting technology. The aim of this review is to familiarize the reader with recent developments in the area and to encourage further research in this field to overcome some of its challenges as well as to inspire creative innovations of these materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".