Recent Developments in Microneedle Biosensors for Biomedical and Agricultural Applications
Bibliographic record
Abstract
Microneedles have emerged as a versatile technology for biosensing across biomedical domains and are increasingly being explored for other applications like agriculture. This review highlights recent advancements in the development of microneedle-based biosensors in novel areas. Biomedical applications include continuous glucose monitoring, multiplexed biomarker detection beyond glucose, and numerous recent works presenting fully integrated systems comprising microneedle arrays alongside miniaturized wearable electronics. Agricultural applications largely focus on the detection of plant growth markers, hormones, and nutrient levels. Despite significant progress, challenges remain in overcoming biofouling and electrode degradation, optimizing electrode longevity for long-term (weeks to months) in situ monitoring, and creating scalable sensor fabrication processes. Additionally, there is a need for standardized mechanical and electrical testing protocols, and guidelines specifying critical performance metrics that should be reported to facilitate accurate literature comparisons. The review concludes by outlining key opportunities for future research to address these persisting challenges.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".