Elucidation of how metal layer deposition conditions impact the optical responses of microgel-based etalon devices to stimuli
Bibliographic record
Abstract
-isopropylacrylamide) (pNIPAm) microgel-based etalons exhibit visual color, and can be fabricated by sandwiching a monolithic microgel layer between two thin metal layers (typically Au). The color of the devices is a direct result of the device structure, and can be dynamically tuned by varying the thickness of the microgel layer in response to external stimuli. For many applications, the robustness of the etalon's structure, the spatial uniformity of the color, and the color change kinetics are of utmost importance. In this investigation, we determined how the composition of the layers that make up the etalon impacts their performance. Specifically, the results indicated that stable etalons can be constructed by simply depositing a layer of Au on top of the microgel layer, as opposed to using a Cr adhesion layer on top of the microgels prior to Au overlayer deposition. We show that thin Au overlayers without a Cr adhesion layer produces Au films that are discontinuous in nature, which in turn directly influences the kinetics of the etalon response. We also confirm that this response pattern holds true for etalon responses to various salt solutions, presenting potential for the future application to alternative analytes of interest.
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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) | 0.000 | 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".