Measurement report: Effects of transition metal ions on the optical properties of humic-like substances (HULIS) reveal a structural preference – a case study of PM <sub>2.5</sub> in Beijing, China
Bibliographic record
Abstract
Humic-like substances (HULIS) are complex macromolecules in water-soluble organic compounds (WSOCs) containing multiple functional groups, and transition metal ions (TMs) are ubiquitous in atmospheric particles. In this study, potential physical and chemical interactions between HULIS and four TM species, including Cu 2+ , Mn 2+ , Ni 2+ , and Zn 2+ , were analyzed by optical method under acidic, weakly acidic, and neutral conditions. The results showed that Cu 2+ , Mn 2+ , and Zn 2+ only slightly enhanced mass absorption efficiency (MAE 365 ) of HULIS in winter and had indiscernible effects on the absorption Ångström exponent (AAE) of HULIS in both seasons under all acidity conditions. All four TMs had fluorescence quenching effects on winter HULIS, and only Cu 2+ had similar effects on summer HULIS, with the highest quenching coefficients found under weakly acidic conditions in both seasons. The 1 H-nuclear magnetic resonance ( 1 H-NMR) and Fourier-transform infrared (FTIR) spectra revealed that Cu 2+ mainly bound with aromatic species and tightened the molecule structures of HULIS. The parallel factor analysis (PARAFAC) results extracted four components of HULIS, including low-oxidized humic-like substances (C1), N-containing compounds (C2), highly oxidized humic-like substances (C3), and the mixing residuals (C4), from the fluorescence spectra in both winter and summer. The spectral characteristic of HULIS with Cu 2+ additions under three acidity conditions indicated that electron-donating groups of HULIS mainly corresponded to C1 and C3, with Cu 2+ binding with HULIS by replacing protons, while electron-withdrawing groups of HULIS could correspond to C2, with its connection with Cu 2+ through electrostatic adsorption or colliding-induced energy transfer.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".