A Conductive Hydrogel‐Based Microneedle Platform for Real‐Time pH Measurement in Live Animals
Bibliographic record
Abstract
Conventional microneedles (MNs) have been extensively reported and applied toward a variety of biosensing and drug delivery applications. Hydrogel forming MNs with the added ability to electrically track health conditions in real-time is an area yet to be explored. The first conductive hydrogel microneedle (HMN) electrode that is capable of on-needle pH detection with no postprocessing required is presented here. The HMN array is fabricated using a swellable dopamine (DA) conjugated hyaluronic acid (HA) hydrogel, and is embedded with poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) to increase conductivity. The catechol-quinone chemistry intrinsic to DA is used to measure pH in interstitial fluid (ISF). The effect of PEDOT:PSS on the characteristics of the HMN array such as swelling capability and mechanical strength is fully studied. The HMN's capability for pH measurement is first demonstrated using porcine skin equilibrated with different pH solutions ranging from 3.5 to 9. Furthermore, the HMN-pH meter is capable of in vivo measurements with a 93% accuracy compared to a conventional pH probe meter. This HMN technology bridges the gap between traditional metallic electrochemical biosensors and the direct extraction of ISF, and introduces a platform for the development of polymeric wearable sensors capable of on-needle detection.
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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 | 0.000 |
| 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 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".