Quantification of microplastics in complex environmental matrices using a tiered approach with modulated differential scanning calorimetry (MDSC)
Bibliographic record
Abstract
The widespread presence of microplastics (MPs) in biosolids raises significant concerns, primarily because biosolids are commonly used as fertilizers in soil, where MPs can accumulate, disrupt soil health and microbial activity, and potentially enter the food chain. Accurate quantification of MPs in biosolids and soil remains challenging due to their low concentrations, aging-induced property variations, and complex biosolid matrices. To address these challenges, modulated differential scanning calorimetry (MDSC), a high-sensitivity, low-detection limit, and cost-effective thermal analysis approach, was employed to quantify MPs in complex biosolid matrices. Using micron-sized polyethylene (PE), polypropylene (PP), polyamide 6 (PA6), and polyethylene terephthalate (PET) spiked into biosolid matrices, MPs were quantified based on the enthalpies generated from the melting peaks. MDSC exhibited 1.4-2.5 times higher sensitivity than conventional DSC, with a theoretical limit of quantification (LOQ) as low as 7 μg/g. An averaged recovery of 93 ± 20% for four micron-sized plastics from three different sources using MDSC demonstrated good accuracy, confirming its reliability. To highlight its applicability to real-world samples, a tiered workflow incorporating MDSC, Raman spectroscopy, and thermogravimetric analysis (TGA) was employed to identify and quantify MPs in biosolids. These findings indicate that MDSC, especially when combined with complementary techniques, is a sensitive and accurate method for identifying and quantifying MPs in complex matrices.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".