Inkjet-printed microgel-based etalon sensor
Bibliographic record
Abstract
Stimuli-responsive microgel-based etalons are promising optical and bio-sensors. These sensors play a pivotal role in modern healthcare by enabling rapid biomolecule detection, contributing to organ-on-chip applications and early disease diagnosis. This study investigates the suitability of poly(N-isopropylacrylamide) (pNIPAm)-based microgels for inkjet printing, focusing on optimizing their properties for effective deposition. Key parameters, including surface tension, viscosity, and particle size, are characterized to ensure compatibility with inkjet-printing requirements. The addition of surfactants tunes the suspensions’ properties to be in line with the requirements of inkjet printing. Jetting of pNIPAm-based microgels on gold-coated substrates forms a cohesive drop in a range of a few millimeters. Optical and scanning electron microscopy confirm the formation of a uniform microgel layer. The optical reflectance spectroscopy results indicate that inkjet-printed microgel-based etalons can effectively respond to changes in temperature and glucose concentration. In-liquid atomic force microscopy demonstrates the swelling dynamics of the microgels in different glucose concentrations, shedding light on their response dynamics. Our work demonstrates, for the first time, the feasibility of printing microgels in a controlled way, fabricating biocompatible inkjet-printed microgel-based etalon sensors with precise dimensions. The size precision and the sensitive monitoring capabilities of biomolecules hold great promise for in situ and continuous sensing in a wide range of biological and organ-on-chip applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".