Ultra-sensitive, on-site pesticide detection for environmental and food safety monitoring using flexible cellulose nano fiber/Au nanorod@Ag SERS sensor
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a highly absorbent and sensitive cellulose nanofiber (CNF)/gold nanorod (GNR)@Ag surface-enhanced Raman scattering (SERS) sensor, fabricated using the vacuum filtration method. By optimizing the Ag thickness in the GNR@Ag core–shell structures and integrating them with CNFs, optimal SERS hotspots were identified using the Raman probe molecule 4-aminothiophenol (4-ATP). To concentrate pesticides extracted from fruit and vegetable surfaces, we utilized the evaporation enrichment effect using hydrophilic CNF and hole-punched hydrophobic polydimethylsiloxane (PDMS). This design leverages the hydrophilic substrate and localized evaporation to create a microfluidic flow that concentrates analytes within a small hole area, enhancing SERS sensitivity by up to 465 %. The sensor achieved on-site detection limits for Thiram as low as 10 −11 M on fruit surfaces, specifically apples and chili peppers. This approach underscores how localized molecule enrichment can substantially improve field-based pesticide analysis. the sensor’s response to interfering substances (e.g., glucose and citric acid) and other harmful molecules (e.g., carbendazim and nitrofurazone was also evaluated, demonstrating high sensitivity and accuracy). The PDMS-assisted CNF/GNR@Ag SERS sensor exhibits flexibility, ease of fabrication, and excellent sensitivity and selectivity, showing significant potential for applications in food safety, agriculture, and environmental monitoring. These advancements are anticipated to promote the practical adoption of SERS-based sensor technology across diverse fields, suggesting broad future utility. • Novel flexible CNF/GNR@Ag SERS sensor, directly applicable to non-planar surfaces. • Localized evaporation enrichment concentrates analytes despite interfering substances. • Detects Thiram (10⁻¹¹ M), CBZ, and NFZ, with broad applicability. • Portable Raman spectrometer enables real-time, on-site pesticide detection. • High sensitivity, accuracy and adaptability, addressing limitations of extant sensors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it