Artificial humic substances as sustainable carriers for manganese: Development of a novel bio-based microfertilizer
Bibliographic record
Abstract
This study presents a new strategy for the production of sustainable manganese fertilizer based on artificial humic substances (AHS). AHS with different manganese concentrations (0‒20%) were synthesized from poplar bark under alkaline conditions via hydrothermal treatment. For the 20% manganese formulation, the interaction of manganese with AHS resulted in reduced solubility (from 25.2% to 12.3% organic carbon) and average molecular weight of humic acids (from 11.6 to 3.9 KDa), indicating preferential binding of the high-molecular-weight fraction. The formulation with 5% of manganese achieved optimal manganese loading without compromising the AHS solubility (19.4%). Structural analyses showed only minor changes in AHS in the presence of manganese, indicating that the main structural fragments of the AHS were preserved. Structural, morphological, and spectroscopic characterizations confirmed the formation of amorphous manganese complexes within the AHS matrix, primarily in the plant-available Mn(II) oxidation state. Plant bioassays showed increased manganese uptake with the application of AHS containing 5% Mn compared to MnCl2 alone (64 mg/kg vs. 40 mg/kg in dry cucumber biomass). Interestingly, unmodified AHS at higher concentrations (50 mg/L) further enhanced manganese (67 mg/kg) and iron (up to 209 mg/kg) uptake, highlighting the potential role of AHS in facilitating metal transport.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".