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Record W2625588221 · doi:10.5004/dwt.2017.0287

Theoretical study of phosphate adsorption from wastewater using Al-(hydr)oxide

2017· article· en· W2625588221 on OpenAlexaff
Nancy Acelas, Elizabeth Flórez

Bibliographic record

VenueDesalination and Water Treatment · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsImpact
Fundersnot available
KeywordsAdsorptionPhosphateWastewaterOxideChemistryChemical engineeringInorganic chemistryEnvironmental scienceEnvironmental engineeringEngineeringPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT The overabundance of phosphorus in water causes eutrophication of aquatic environments. As a consequence, developing an adsorbent and understanding the adsorption process to remove phosphate is vital for the prevention of eutrophication in lakes. In this study, quantum chemical calculations were used to simulate the adsorption of phosphate on variably charged Al-(hydr)oxide, taking into account both explicit and implicit solvation. The corresponding adsorption reactions were modeled via ligand exchange between phosphate species and surface functional groups (–H 2 O/–OH – ). Gibbs free energies of phosphate adsorption, for inner and outer sphere complexes, using three different simulated pH conditions (acidic, intermediate, and basic) were estimated. The theoretical results indicate that the thermodynamic favorability of phosphate adsorption on Al-(hydr)oxide is directly related to pH. At intermediate pH condition, H-bonded and MM 1 complexes present the most thermodynamically favorable mode of adsorption with –126.2 kJ/mol and –107.8 kJ/mol, respectively. At high pH, simulated IR spectra show that the values of P–O and P–OH stretching modes shifted to higher frequencies with respect to those at low pH.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.540

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)0.0000.000

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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2017
Admission routes1
Has abstractyes

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