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Record W4321790281 · doi:10.1002/adfm.202213862

Glyphosate Adsorption from Water Using Hierarchically Porous Metal–Organic Frameworks

2023· article· en· W4321790281 on OpenAlexafffund
Shaghayegh Naghdi, Emily Brown, Mohammad Zendehbad, Ann Duong, Wolfgang Ipsmiller, Santu Biswas, Maytal Caspary Toroker, Hossein Kazemian, Dominik Eder

Bibliographic record

VenueAdvanced Functional Materials · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaTechnische Universität WienAustrian Science FundNancy and Stephen Grand Technion Energy ProgramUniversity of Northern British Columbia
KeywordsAdsorptionMesoporous materialLigand (biochemistry)Materials scienceMetal-organic frameworkGlyphosatePorosityChemical engineeringMetalPhosphoric acidInorganic chemistryOrganic chemistryChemistryComposite materialReceptorCatalysis

Abstract

fetched live from OpenAlex

Abstract The selective removal of one ligand in mixed‐ligand MOFs upon thermolysis provides a powerful strategy to introduce additional mesopores without affecting the overall MOF structure. By varying the initial ligand ratio, MOFs of the MIL‐125‐Ti family with two distinct hierarchical pore architectures are synthesized, resembling either large cavities or branching fractures. The performance of the resulting hierarchically porous MOFs is evaluated toward the adsorptive removal of glyphosate (N‐(phosphonomethyl)glycine) from water, and the adsorption kinetics and mechanism are examined. Due to their strong affinity for phosphoric groups, the numerous Ti–OH groups resulting from the selective ligand removal act as natural anchor points for effective glyphosate uptake. The relationships between contact duration, glyphosate concentration, and adsorbent dosage are investigated, and the impact of these parameters on the effectiveness of glyphosate removal from contaminated water samples is examined. The introduction of additional mesopores has increased the adsorption capacities by nearly 3 times with record values exceeding 440.9 mg g −1 , which ranks these MOFs among the best‐reported adsorbents.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0270.004

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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations80
Published2023
Admission routes2
Has abstractyes

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