Impact of organic carbon-Mn oxide interactions on colloid stability and contaminant metals in aquatic environments
Bibliographic record
Abstract
• Formation conditions of C-Mn colloids impact their stability • Stability impacted by electrostatic repulsion, surface functional groups, and Mn(II) generation • Contaminant metals influence the aggregation of C-Mn colloids • C-Mn colloids aggregate rapidly in the presence of Cd 2+ and Zn 2+ • C-Mn colloids remain stable in the presence of Mn 2+ and Co 2+ Interactions between organic carbon and Mn oxides can lead to the formation of C-Mn colloids, which play a crucial role in regulating Mn mobility in the environment. Despite the significance of these interactions, however, the impact of C-Mn oxide interactions on the mobility of these colloids, particularly in the presence of contaminant metals, remains poorly understood. This study investigated the aggregation kinetics of C-Mn colloids formed through the reaction between humic acid and Mn oxides at three C:Mn molar ratios in the presence of divalent cations (Ca 2+ and Mg 2+ ). The introduction of organic carbon increased the stability (i.e., ability to resist aggregation) of C-Mn colloids compared to pure Mn(IV) colloids, as reflected in the higher critical coagulation concentration (CCC). As C:Mn molar ratios rose from 0.5 to 3 during colloid formation, the CCCs for the resulting C-Mn colloids increased from 3.6 mM to 7.2 mM Ca 2+ . However, at the highest C:Mn ratio (C:Mn=15), the CCCs decreased slightly to 7.0 mM Ca 2+ , with a similar trend observed for Mg 2+ . The stability of C-Mn colloids was affected by their characteristics, including electrostatic repulsion, surface functional groups, and Mn(II) content, which resulted upon reaction with dissolved organic carbon. Based on CCCs, C-Mn colloids were most stable in the presence of Mn 2+ (6.9 mM), followed by Co 2+ (5.9 mM), Zn 2+ (2.7 mM), and Cd 2+ (1.9 mM). The capacity of contaminant metals to destabilize C-Mn colloids followed the reverse order, with Cd 2+ having the greatest destabilizing effect. Variations among the different metals were influenced by factors such as atomic radius, hydration shell, electronegativity, and electrostatic repulsion. These results provide new insights into the aggregation behavior of C-Mn colloids and the mechanisms controlling the fate and mobility of associated contaminant metals. This knowledge has important implications for understanding contaminant transport in natural waters and optimizing water treatment processes.
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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 | 0.001 | 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) | 0.002 | 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".