Development of a Novel Fiber Optic Sensor Combined with a Fluorescence Turn-on Probe for Cu (II) Detection
Bibliographic record
Abstract
Existing staining-based methodology for the detection of metal ions is not well suited for real-time or in situ use. This is a significant problem, given that these ions can have a considerable impact on both human health and the environment. Thus, there is growing interest and need for simple, rapid and in-situ monitoring techniques for the purpose of detecting various target analytes (e.g. heavy metals), which is of a significant importance in many fields ranging from environmental monitoring to the study of intracellular processes. Among various sensors developed, optical fiber-optic sensors (FOS), based on fluorescence, are one class of sensors that address this goal [1]. Optical fibers are ideal for environmental sensing applications because of their ability to transmit optical signals to and from the sensing region without the use of free-space optics. In this work, we present, for the first time, a simple FOS incorporating novel fluorescence turn-on mechanism [2] that could detect Cu (II) as low as 10−4 M. Traditionally, fluorescence quenching or “turn-off” was used to detect Cu (II) [3]. In recent years, fluorescence “turn-on” emerges as a preferable tool. The developed fiber-optic sensor has two fiber leads and one probe head. One fiber lead includes 6 fibers for He-Ne laser excitation light delivery (e-fibers). Another fiber lead has one receiving fiber (r-fiber) connected to an Ocean Optics QE65000 scientific grade spectrometer, which is interrogated by a computer via USB connection. The SpectroSuite software is used to observe and to record all spectra. The probe head combines all fibers together to form a coaxial structure with the r-fiber placed in the center. The key component in the proposed fluorescent sensing system is a probe prepared by binding a receptor containing a zwitterionic chromophore (M1), through noncovalent interactions, to the fluorescent polymer (P1) resulting in quenching its emission. The sensing mechanism involves the release of P1 (a fluorescent indicator) from its non-fluorescent indicator/receptor (P1/M1) pair upon exposure to Cu (II). The zwitterionic chromophore (M1) was found to show high selectivity and sensitivity to Cu (II), making it an ideal receptor for the recognition of Cu (II) [4]. The optimum operating conditions and performance characteristics for the new sensor will be discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".