Understanding the Interactions of Nanoparticles and Dissolved Organic Matter at the Molecular Level by <sup>1</sup> H 2D Multi‐Exponential Transverse Relaxation NMR Spectroscopy
Bibliographic record
Abstract
ABSTRACT The interaction between humic acid (HA) and engineered nanoparticles (NPs) is critical in environmental sciences, especially for understanding the behavior of NPs in natural waters. This study employs 1 H 2D Multi‐Exponential Transverse Relaxation (METR) NMR spectroscopy to examine the molecular‐level interactions between Pahokee Peat humic acid (HA) and carboxyl‐functionalized iron oxide nanoparticles (NPCOs). First, 1 H 2D METR NMR spectroscopy allowed not only the identification of HA in terms of its chemical composition but also the separation of molecules with the same chemical shift values but different rates of molecular tumbling. Then, using solutions with varying NPCO concentrations (0, 10, 40, and 100 μM), we observed significant changes in the T 2 relaxation times of HA components, indicating interactions between HA and NPCO. Analysis showed the biggest effect on two chemical shift regions, corresponding to lipids and carbohydrates, revealing that smaller molecules within these regions exhibit the most significant changes in T 2 values upon the addition of NPCO. This suggests that these molecules are the initial sites of interaction, with the entire HA system being affected at higher NPCO concentrations. These findings highlight the utility of METR NMR spectroscopy in studying complex environmental mixtures and provide insights into the behavior of HA and NPs, essential for understanding the fate of NPs in the environment.
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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".