Investigating the deposition behavior of different polystyrene nanoplastics onto mineral surfaces using QCM-D
Bibliographic record
Abstract
Nanoplastic, as primary or secondary plastics, emerges as a contaminant across all environmental compartments. In terrestrial settings, the vadose zone is considered a plastic sink. Yet, leaching into deeper saturated subsurface areas and groundwater may occur via preferential flow paths, changing hydro-chemical conditions, or direct infiltration in low lying or recharge areas. Understanding transport and deposition behavior of nanoplastics in aquifer settings is crucial as it i) is expected to deviate from that of engineered nanoparticles (ENPs) due to its more complex physical and chemical properties, and ii) to be able to develop and inform numerical models to upscale nanoplastic contaminant transport when e.g., exploring groundwater resources. Quartz-crystal microbalance with dissipation monitoring (QCM-D) was used to investigate the deposition behavior of various model polystyrene nanoparticles onto two of the most abundant mineral species on Earth: quartz and kaolinite under various chemical settings.Three types of polystyrene of ~ 100 nm were used herein: A non-functionalized spherical polystyrene (PLAIN), a spherical carboxyl functionalized polystyrene (CARBO) and a hexagonal secondary polystyrene (GRIND) produced by mechanical grinding of larger polystyrene beads. Furthermore, divalent ion concentrations in terrestrial environments are inducing larger effects on nanoplastic processes than monovalent ions and therefore only the effect of increasing Ca2+ concentration in solution was tested. Moreover, natural organic matter (NOM) in terrestrial environments is usually degraded with depth, thus its presence in saturated groundwater can be negligible, yet to consider even low concentrations, we also tested the effect of technical grade humic acid as a model NOM. We found that deposition behavior differs between various particles and mineral surfaces as well as with Ca2+ concentration. For quartz surfaces, non-spherical particles showed the highest deposition rates, while with the increasing mineral complexity (kaolinite), this effect diminished, and other factors gained more importance. Kaolinite surfaces showed the highest deposition rates among all particle types. This suggests the involvement of surface charge driven processes, where positive Al-OH sites of the kaolinite more effectively attract negatively charged nanoplastics as compared to negatively charged quartz. Increasing the ionic strength increased the deposition behavior until a peak deposition observed at 15 mM Ca2+ due to a gradual charge decrease of particles and minerals. Beyond 15 mM, deposition decreases as a result of reduced particle stability, and consequently lowered convective-diffusive transport to the mineral surface. Surprisingly, highly carboxylated CARBO particles showed a large increase in deposition on kaolinite irrespective of Ca2+ concentration. This may be explained by the importance of Al-OH sites, which bind -COOH groups more effectively than Si-O sites. Adding 1mg/L humic acid at 15 mM Ca2+ reduced the deposition behavior significantly at both mineral surfaces. Our results highlight important processes between nanoplastics and mineral surfaces and thereby also important impacts in understanding nanoplastic transport in subsurface terrestrial environments. Charge driven processes dominate in simple mineral settings (quartz), while with increasing mineral complexity, chemical processes and specific ion binding interactions will dominate nanoplastic deposition and transport.
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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) | 0.000 | 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) | 0.001 | 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".