A review of adsorbents engineered from biological materials developed to remediate estrogen pollution in the environment
Bibliographic record
Abstract
ABSTRACT: Rising concerns over anthropogenic impacts on the environment have increased environmental pollution and water quality research which have demonstrated widespread pollutants characteristics in their low concentrations and few government regulations. Pharmaceuticals are an important subcategory of emerging contaminant pollution which consists of natural and synthetic compounds. Steroids are a notable class of pharmaceutical pollutants due to their high binding affinities and activation of signaling cascades at low concentrations. The impacts of hormonal pollutants necessitate remediation solutions. Adsorption technologies are a favorable method of pollutant removal in research due to the ease of implementation, cost-effectiveness, and potential for environmental friendliness. Biologically derived adsorbent materials offer additional potential benefits of employing properties of biological materials, improving biocompatibility, and valorizing biomass. Hydrogels, biomass, and biochar are some of the most environmentally friendly adsorbents in pollution remediation research and there is a sizable base of publications detailing promising results of these materials. Thus, a review of recent publications of hydrogels, biomass, and biochar applied to adsorb estrogens, the largest class of steroid hormones, will allow their findings to be compared. Estrone (E1), estradiol (E2), estriol (E3), and ethinylestradiol (EE2) were focused on in literature alongside other less-researched hormones including prednisolone, progesterone, hydrocortisone, and dexamethasone. Removal efficiencies of E1, E2, EE2, and E3 by hydrogels ranged from 10 % to 93 %, removal efficiencies of E2 and EE2 with biomasses reached up to 99 % with tree bark, and biochar adsorption peaked in capacity of 233 mg/g of EE2 when synthesized from spent mushroom substrate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".