Hydrogel-Based Biosensors
Bibliographic record
Abstract
There is an increasing interest in sensing applications for a variety of analytes in aqueous environments, as conventional methods do not work reliably under humid conditions or they require complex equipment with experienced operators. Hydrogel sensors are easy to fabricate, are incredibly sensitive, and have broad dynamic ranges. Experiments on their robustness, reliability, and reusability have indicated the possible long-term applications of these systems in a variety of fields, including disease diagnosis, detection of pharmaceuticals, and in environmental testing. It is possible to produce hydrogels, which, upon sensing a specific analyte, can adsorb it onto their 3D-structure and can therefore be used to remove them from a given environment. High specificity can be obtained by using molecularly imprinted polymers. Typical detection principles involve optical methods including fluorescence and chemiluminescence, and volume changes in colloidal photonic crystals, as well as electrochemical methods. Here, we explore the current research utilizing hydrogel-based sensors in three main areas: (1) biomedical applications, (2) for detecting and quantifying pharmaceuticals of interest, and (3) detecting and quantifying environmental contaminants in aqueous environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".