Ultra-Sensitive Abscisic Acid Detection Using Gold and 4-Mercaptopyridine Perovskite-Engineered Robust Nanofibers (GLAMPER-NFs) under Surface-Enhanced Raman Spectroscopy
Bibliographic record
Abstract
This study explores the development and application of gold and 4-mercaptopyridine (MPY) perovskite-engineered robust nanofibers (GLAMPER-NFs) for the ultrasensitive detection of Abscisic acid (ABA) under Raman spectroscopy, a crucial plant hormone. The GLAMPER-NFs composite material, consisting of MAPbCl 3 nanofibers integrated with MPY-coated gold nanostructures, demonstrates exceptional performance in surface-enhanced Raman scattering (SERS)-based sensing. The study elucidates the material structure and properties through comprehensive characterization using scanning electron microscopy (SEM), UV–vis spectroscopy, fluorescence spectroscopy, Fourier transform infrared, and Raman spectroscopy. The SEM analysis reveals uniform nanofibers with diameter of 107.8 ± 3.06 nm, while spectroscopic studies confirm the successful synthesis and integration of the composite components. The SERS-based detection of ABA showcases remarkable sensitivity, with a linear detection range ( R 2 = 0.9957) spanning 7 orders of magnitude (10 –14 –10 –7 M) and presented a limit of detection of 10 –11 and enhancement factor of 1.08 × 10 7 . This surpasses the performance of existing sensing platforms, demonstrating clear spectral responses even at femtomolar concentrations. The synergistic effects of the perovskite structure, plasmonic gold nanoparticles, and MPY linking molecules contributed exceptional sensing capabilities to the GLAMPER-NFs material. Fluorescence studies further corroborate the sensitivity and provide insights into the photophysical interactions between ABA and the composite material. This research advances the understanding of perovskite-based hybrid materials and presents a promising GLAMPER-NFs as SERS substrate for ultrasensitive plant hormone detection, with potential applications in agricultural monitoring and plant science research.
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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) | 0.000 | 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 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".