Phosphorus Speciation in Agro-Industrial Byproducts: Sequential Fractionation, Solution <sup>31</sup>P NMR, and P <i>K</i>- and <i>L</i><sub>2,3</sub>-Edge XANES Spectroscopy
Bibliographic record
Abstract
Little is known about P species in agro-industrial byproducts from developing countries, which may be either pollutants or valuable soil amendments. The present study speciated P in dry (COD) and wet (COW) coffee, sisal (SIS), barley malt (BEB) and sugar cane processing (FIC) byproducts, and filter cakes of linseed (LIC) and niger seed (NIC)with sequential fractionation, solution (31)P nuclear magnetic resonance (NMR) spectroscopy, and P K- and L(2,3)-edge X-ray absorption near-edge structure (XANES) spectroscopy. The sequential P fractionation recovered 59% to almost 100% of total P (P(t)), and more than 50% of P(t) was extracted by H(2)O and NaHCO(3) in five out of seven samples. Similarly, the NaOH + EDTA extraction for solution (31)P NMR recovered 48-94% of P(t). The (31)P NMR spectra revealed orthophosphate (6-81%), pyrophosphate (0-10%), and orthophosphate monoesters (6-94%). Orthophosphate predominated in COD, COW, SIS, and FIC, whereas BEB, UC, and NIC were rich in orthophosphate monoesters. The concentrations of P(i), and P(o) determined in the sequential and NaOH + EDTA extractions and (31)P NMR spectra were strongly and positively correlated (r = 0.88-1.00). Furthermore, the P K- and L(2,3)-edge XANES confirmed the H(2)SO(4)--P(i) detected in the sequential fractionation by unequivocal identification of Ca--P phases in a few samples. The results indicate that the combined use of all four analytical methods is crucial for comprehensive P speciation in environmental samples and the application of these byproducts to soil.
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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) | 0.000 | 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 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".