Impact of Hydrofluoric Acid Treatment on Humic Acid Properties Extracted from Organic Soils and an Organic Amendment: A Technical Evaluation
Bibliographic record
Abstract
The chemical and physical characteristics of humic acids (HA) may differ depending on their source, and the ideal extraction method should not modify the characteristics of HA. Hydrofluoric acid (HF) is often used in HA extraction methods to remove inorganic substances that are often present in the sample in addition to the organic molecules of interest. Organic soils contain up to 90% of organic matter and some may think that the necessity of the HF treatment is not crucial when extracting HA from organic soils. In this study, HA were extracted from turkey litter compost (TLC), an agricultural organic soil (AOS) and a riparian soil (RS), and the impact of HF on the properties of the extracted HA was assessed. HF decreased the ash content and the concentration of inorganic components in extracted materials, especially in the HA from RS, which had higher concentrations of aluminosilicates and amorphous Si. For TLC, no significant difference in either the total charge or charge distribution was observed with HF treated samples compared to untreated samples, while the HF treatment decreased the proton binding capacity at alkaline pH (≥7) in the HA from the two soils. We assume that aluminosilicates, amorphous Si and iron oxides left in the samples interacted with protons at alkaline pH, thus increasing the proton binding capacity. Therefore, HF treatment should be included when samples containing these mineral constituents even for materials rich in organic matter. Core Ideas Hydrofluoric acid did not affect functional group distribution or C concentration in humic acids. Proton binding capacity at alkaline pH of soil humic acids decreased after hydrofluoric acid treatment. Hydrofluoric acid did not affect characteristics of humic acids extracted from turkey litter compost. Humic acids from turkey litter compost had lower proton binding capacity than those from soils.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".