Bibliographic record
Abstract
Hydrogels have emerged as a versatile platform technology for analyte sensing, offering unique advantages in tunable chemistry, for loading with sensors across multiple length scales, and biocompatibility. These smart materials undergo predictable changes in optical properties, conductivity, swelling, and porosity upon analyte interaction, enabling their function as biosensors. While hydrogels can respond to a variety of stimuli, their responses are most effectively quantified through optical and electrical readouts, which enable direct, real-time, and quantitative sensing in complex biological fluids. Optical approaches leverage fluorescence, chemiluminescence, and colorimetry, whereas electrical approaches leverage conductive fillers or redox-active groups. Hybrid platforms integrate multiple readout mechanisms, enhancing sensitivity, robustness, and multiplexing capabilities. Many of these systems were validated in various biological matrices, such as interstitial fluid, sweat, and wound exudates. Beyond technical advances, we discuss translational challenges including selectivity, stability, nonreversibility, signal standardization, device portability, and regulatory approval, as well as emerging opportunities in coupling hydrogel sensors with artificial intelligence for improved data interpretation and clinical integration. Together, these developments position hydrogel-based diagnostics as promising candidates for next-generation, real-time, point-of-care biosensing.
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 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 | 0.002 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".