Field-Deployable Immuno-Solid-Phase Microextraction Coupled with Photothermal Imaging for Rapid Pathogen Surveillance in Environmental and Clinical Matrices
Bibliographic record
Abstract
Rapid, sensitive, and portable pathogen detection is critical for infectious disease control and environmental public health surveillance but remains constrained by conventional methods requiring laborious sample pretreatment and bulky instrumentation. We rationally integrate immunosolid-phase microextraction (iSPME) with portable photothermal imaging (PI), enabling rapid, on-site pathogen detection in complex matrices. This platform combines two innovations: (1) antibody-functionalized SPME fibers with fibrous SiO 2 microspheres (342.76 m 2 /g surface area) engineered via CTAB-templated synthesis, enabling rapid (15 min) and high-efficiency pathogen capture (73.7–98.9% recovery) through covalent antibody immobilization on carboxylated surfaces; (2) NIR-responsive plasmonic Au shell nanoprobes optimized via Ag/Au redox etching, achieving a red-shifted LSPR peak at 798 nm and a 57.1% photothermal conversion efficiency for interference-free signal transduction. The iSPME-PI platform eliminates sample transfer steps by forming a sandwich immunocomplex directly on the fiber, enabling spatially resolved photothermal quantification under an 808 nm laser excitation. It delivers ultralow detection limits (74.8 pg/mL SARS-CoV-2 N, 68.5 pg/mL Flu A NP) and robust performance in saliva, milk, and sewage (RSD < 8.3%). Clinical samples showed 100% concordance for SARS-CoV-2. Furthermore, wastewater monitoring using the iSPME-PI platform accurately tracked influenza A outbreaks in real time, demonstrating its potential for environmental biosurveillance and early warning systems.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".