Stimuli-responsive polymer/nanomaterial hybrids for sensing applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Chemical and biological/biochemical sensors are capable of generating readout signals that are proportional to the concentration of specific analytes of interest. Signal sensitivity and limit of detection/quantitation can be enhanced through the use of polymers, nanomaterials, and their hybrids. Of particular interest are stimuli-responsive polymers and nanomaterials due to their ability to change their physical and/or chemical characteristics in response to their environment, and/or in the presence of molecular/biomolecular species of interest. Their individual use for sensing applications have many benefits, although this review focuses on the utility of stimuli-responsive polymer and nanomaterial hybrids. We discuss three main topics: stimuli-responsive nanogels, stimuli-responsive network polymers doped with nanomaterials, and nanoparticles modified with stimuli-responsive polymers.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it