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Record W4406070796 · doi:10.1016/j.jece.2024.115274

A review of adsorbents engineered from biological materials developed to remediate estrogen pollution in the environment

2025· review· en· W4406070796 on OpenAlexafffund
Trevor Bell, Jason R. Tavares, Marie‐Josée Dumont

Bibliographic record

VenueJournal of environmental chemical engineering · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsPolytechnique MontréalUniversité Laval
FundersFonds de recherche du Québec – Nature et technologies
KeywordsPollutionEnvironmental scienceEstrogenWaste managementEnvironmental chemistryBiochemical engineeringEnvironmental planningChemistryEngineeringBiologyMedicineEcologyInternal medicine

Abstract

fetched live from OpenAlex

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. • Biologically derived materials developed to adsorb estrogens were reviewed • Methods of estrogen adsorption were reviewed and discussed • Comparisons were made between methods of estrogen adsorption • Differences between hydrogels, biomass, and biochar were compared • Suggestions for future development of adsorbent materials were made

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.250
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2025
Admission routes2
Has abstractyes

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