Novel integrated workflow for microplastics extraction, quantification, and characterization in organic fertilizing residuals using micro-Fourier transform infrared spectroscopy (μ-FTIR)
Bibliographic record
Abstract
Organic fertilizing residuals (OFRs) enhance soil fertility and support sustainable agriculture due to their rich nutrient and organic matter content. However, these materials are increasingly recognized as a significant source of microplastics (MPs) in agricultural soils, raising concerns about the safety of agroecosystems. Therefore, there is an urgent need to develop a reliable workflow for MP analysis in diverse OFRs, given the challenges of extracting small MPs from such organic matter-rich matrices. This study presents an oxidative-alkaline tandem digestion method that achieves an average organic matter (OM) removal efficiency of 93 % across various OFRs. In addition, density centrifugation with NaCl and ZnCl 2 brines was utilized to recover six microplastic polymers (PP, PVC, PET, PS, PE, and HDPE), achieving a recovery rate of over 95 % for large MPs (600 μm–4.75 mm) and over 83 % for small MP-PE beads (38–45 μm). Micro-Fourier transform infrared spectroscopy (μ-FTIR) analysis confirmed that digestion and separation steps did not affect MPs' spectral integrity and chemical identification. To validate the workflow, we applied it to analyze MPs in various OFRs from Québec, allowing for the successful detection of 19 MP polymers with sizes down to 10–50 μm. This workflow can be applied to multiple OFRs to extract, quantify, and characterize MPs. Ultimately, this workflow will facilitate efficient MPs analysis across diverse OFRs, providing essential data for robust risk assessment and better environmental management to mitigate MP pollution from OFR applications in agricultural soils. • We have developed a novel analytical workflow to analyze microplastics in various organic fertilizing residuals. • The tandem oxidative-alkaline digestion removed an average of 93 % of organic matter. • The extraction method achieved over 83 % recovery of small MP particles (35–45 μm). • The digestion and extraction did not affect the chemical identification of MPs using μ-FTIR. • The workflow-enabled the identification of 19 distinct polymer types in OFRs, about 78.3 % less than 200 μm.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".