pH-Dependent Chemical Transformations of Humic-Like Substances and Further Cognitions Revealed by Optical Methods
Bibliographic record
Abstract
Humic-like substances (HULIS) are macromolecular complex groups in water-soluble organic compounds (WSOC). pH is a crucial factor that influences the chemical transformations of HULIS in atmospheric particles, but this has been rarely investigated, especially under varying pH conditions. This study attempted to unveil the chemical transformation mechanisms of HULIS under a range of pH conditions using optical methods. The pH-dependent light absorption and fluorescence properties of HULIS were comprehensively analyzed; the acidity coefficient (p K a ) of HULIS in relation to chemical structures was determined, and the hypothetical chemical transformation mechanisms of HULIS with increasing pH were analyzed by optical characterizations. The results suggested that pH greatly impacted the light absorption and fluorescence efficiencies of HULIS in both winter and summer seasons, and p K a was an important inflection point. The p K a of HULIS ranged from 3.5 to 8.0 in winter and 6.4 to 10.0 in summer. The acidic/basic groups were identified as −OH or −NH 2 substituted quinolines, carboxylic aromatics, and pyridines. The pH-sensitive species accounted for about 6% and 21% of HULIS-C (carbon concentrations of HULIS) in winter and summer, respectively. The varying optical spectra with increasing pH might result from charge transfer or complex reactions with HULIS deprotonation.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".