A versatile reactive oxygen species‐responsive gels sensor for analysis of metabolic species
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
Abstract We report a flexible redox responsive polymer‐based sensor for detection of reactive oxygen species (ROS). The sensor comprises multilayers of silver nanoparticles (AgNPs), carbon nanotube/cellulose nanocrystal (CNT/CNC) and a redox responsive poly (glycidyl methacrylate‐ co ‐ethylene glycol dimethacrylate‐ co ‐vinyl ferrocene), herewith called poly (GMA‐ co ‐EGDMA‐ co Fc) nanoferrogels. These sensor layers were printed on a micropillared polydimethylsiloxane (PDMS) substrate. The nanoferrogel sensor versatility has been demonstrated in its effective detection of ROS species, specifically H 2 O 2 , peroxylipids, and oxidative deriving species such as cholesterol and unsaturated triglycerides. The nanoferrogel ROS sensor rapidly (~1 min) responds to both H 2 O 2 and peroxylipids with a limit of detection (LOD) of ~0.060 ± 0.001 µg/ml) and 0.012 ± 0.001 µg/ml, respectively. The cholesterol oxidase and lipoxygenase‐based nanoferrogel sensor were successfully evaluated for the detection of cholesterol (LOD of 0.12 ± 0.02 µg/ml) and glyceryltrilinoeate (LOD of 1.8 ± 0.2 ng/ml) standards, respectively. These enzyme‐loaded ROS nanoferrogel sensors were also evaluated for quantification of cholesterol and glyceryltrilinoeate in bacon lard and olive oils. The fabricated flexible ROS sensors are versatile for quantitation of oxidative stress biomarkers, useful in myriad applications including clinical, environmental, food, and plant physiology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it