Rapid Detection of Micro/Nanoplastics Via Integration of Luminescent Metal Phenolic Networks Labeling and Quantitative Fluorescence Imaging in A Portable Device
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The fact that there is an accumulation of micro-and nano-plastics (MNPs) in ecosystems which poses tremendous environmental risks for terrestrial and aquatic organisms is undeniable. Thus, designing improved rapid, field-deployable, and sensitive analytical devices that can assess the potential risks of MNPs pollution is critical. Since current techniques for MNPs detection have limited effectiveness, we sought to design a wireless portable device that will allow rapid, sensitive, and on-site detection of MNPs. Coupling this capacity with remote data processing via machine learning algorithms in a mobile device APP will further enable quantitative fluorescence imaging of MNPs. To achieve this goal, we utilized a developed supramolecular labeling strategy, employing luminescent metal-phenolic networks (L-MPNs) composed of zirconium ions, tannic acid, and rhodamine B, to label a wide range of MNP sizes (i.e.,10 μm, 1 μm, 500 nm, and 50 nm). Results showed that our device can quantify MNPs and detect particle quantities as low as 330 micro-plastic particles and 3.08×106 nano-plastic particles in less than 20 min; while also successfully facilitating quantitative analysis of real-world MNPs samples. The determination of diverse types of MNPs released from commercial plastic cups revealed that the quantity of released plastic particles reached ranges of hundred-million after exposure to boiling water and subsequent 30 min cooling. The device was shown to be user-friendly and operative on a mobile APP by untrained personnel to conduct data processing remotely and effectively. The analytical platform integrating quantitative fluorescence imaging, customized data processing, decision tree model and low-cost analysis ($0.015 per assay) has great potential for high-throughput screening of various types of MNPs in agri-food and environmental systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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