A sustainable colloidal material with sorption and nutrient‐supply capabilities for in situ groundwater bioremediation
Bibliographic record
Abstract
Abstract Microbial degradation of subsurface organic contaminants is often hindered by the low availability of both contaminants and nutrients, especially phosphorus (P). The use of activated carbon and traditional P fertilizers to overcome these challenges has proved ineffective; therefore, we sought to find an innovative and effective solution. By heating bone meal–derived organic residues in water in a closed reactor, we synthesized nonporous colloids composed of aromatic and aliphatic structures linked to P groups. X‐ray absorption near edge spectroscopy analysis revealed that the materials contain mostly bioavailable forms of P (i.e., adsorbed P and magnesium‐bearing brushite). The capacity of the materials to adsorb organic contaminants was investigated using benzene and batch isotherm experiments. The adsorption isotherms were fitted to the linearized Freundlich model; isotherm capacity (logKF) values for the materials ranged between 1.6 and 2.8 μg g–1. These results indicate that the colloidal materials have a high affinity for organic contaminants. This, coupled with their possession of bioavailable P, should make them effective amendments for in situ groundwater bioremediation. Also, the materials’ chemical properties suggest that they are not recalcitrant, implying that they will not become potential contaminants when released into the environment.
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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 | 0.001 | 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".