| Title |
Digital Surface-Enhanced Raman Spectroscopy–Lateral Flow Test Dipstick: Ultrasensitive, Rapid Virus Quantification in Environmental Dust
|
|---|---|
| Published in |
Environmental Science & Technology, March 2024
|
| DOI | 10.1021/acs.est.3c10311 |
| Pubmed ID | |
| Authors | |
| Abstract |
This study introduces a novel surface-enhanced Raman spectroscopy (SERS)-based lateral flow test (LFT) dipstick that integrates digital analysis for highly sensitive and rapid viral quantification. The SERS-LFT dipsticks, incorporating gold-silver core-shell nanoparticle probes, enable pixel-based digital analysis of large-area SERS scans. Such an approach enables ultralow-level detection of viruses that readily distinguishes positive signals from background noise at the pixel level. The developed digital SERS-LFTs demonstrate limits of detection (LODs) of 180 fg for SARS-CoV-2 spike protein, 120 fg for nucleocapsid protein, and 7 plaque forming units for intact virus, all within <30 min. Importantly, digital SERS-LFT methods maintain their robustness and their LODs in the presence of indoor dust, thus underscoring their potential for accurate and reliable virus diagnosis and quantification in real-world environmental settings. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 75% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 18 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Doctoral Student | 2 | 11% |
| Student > Ph. D. Student | 2 | 11% |
| Student > Master | 2 | 11% |
| Researcher | 2 | 11% |
| Other | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 8 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 17% |
| Chemistry | 2 | 11% |
| Engineering | 2 | 11% |
| Medicine and Dentistry | 1 | 6% |
| Unknown | 10 | 56% |