Advances in Plasmonic Photonic Crystal Fiber Biosensors: Exploring Innovative Materials for Bioimaging and Environmental Monitoring
Bibliographic record
Abstract
Abstract This review paper comprehensively analyzes recent advancements in optical fiber‐based biosensors, focusing on conventional fiber and photonic crystal structures. This paper overviews the significant applications of optical fiber biosensors, including bioimaging, quality analysis, food safety, and field environment monitoring, setting the stage for subsequent discussions. The primary objective of the review is to systematically evaluate recent literature concerning optical fiber‐based biosensors, emphasizing their sensitivities and resolutions. The second section explores integrating plasmonic materials such as graphene, TDMC, germanium, black phosphorus, and silicon within optical fiber biosensors, elucidating their roles in enhancing sensitivity and resolution in biosensing applications. A detailed examination of photonic crystal fibers (PCF) follows, categorizing them into internally and externally metal film‐coated biosensors, highlighting their distinct advantages and limitations. Comparative analyses in two tables delineate the performance and sensitivity of optical fiber‐based biosensors, mainly focusing on different coating strategies. The final section of the review discusses emerging trends and applications in optical fiber biosensing technologies, underscoring their potential to transform biomedical and environmental monitoring fields. By synthesizing recent developments and challenges, this review aims to offer researchers and practitioners a comprehensive understanding of optical fiber‐based biosensors, facilitating informed decision‐making and driving further advancements in the field.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".