Nanoscale Insights into the Interaction Mechanism Underlying the Adsorption and Retention of Heavy Metal Ions by Humic Acid
Bibliographic record
Abstract
The mobility and distribution of heavy metal ions (HMs) in aquatic environments are significantly influenced by humic acid (HA), which is ubiquitous. A quantitative understanding of the interaction mechanism underlying the adsorption and retention of HMs by HA is of vital significance but remains elusive. Herein, the interaction mechanism between HA and different types of HMs (i.e., Cd(II), Pb(II), arsenate, and chromate) was quantitatively investigated at the nanoscale. Based on quartz crystal microbalance with dissipation tests, the adsorption capacities of Pb(II), Cd(II), As(V), and Cr(VI) ionic species on the HA surface were measured as ∼0.40, ∼0.25, ∼0.12, and ∼0.02 nmol cm –2, respectively. Atomic force microscopy force results showed that the presence of Pb(II)/Cd(II) cations suppressed the electrostatic double-layer repulsion during the approach of two HA surfaces and the adhesion energy during separation was considerably enhanced from ∼2.18 to ∼5.05/∼4.18 mJ m –2 . Such strong adhesion stems from the synergistic metal–HA complexation and cation−π interaction, as evidenced by spectroscopic analysis and theoretical simulation. In contrast, As(V)/Cr(VI) oxo-anions could form only weak hydrogen bonds with HA, resulting in similar adhesion energies for HA–HA (∼2.18 mJ m –2 ) and HA–As(V)/Cr(VI)–HA systems (∼2.26/∼1.96 mJ m –2 ). This work provides nanoscale insights into quantitative HM–HA interactions, improving the understanding of HMs biogeochemical cycling.
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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".