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Application of aluminosilicate residue-based zeolite from lithium extraction in water treatment

2024· article· en· W4403364295 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMicroporous and Mesoporous Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAluminosilicateZeoliteResidue (chemistry)Extraction (chemistry)Lithium (medication)ChemistryMolecular sieveInorganic chemistryChemical engineeringOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

In a previous study, the Na-P1 type zeolites were synthesized from aluminosilicate residues using an efficient and cost-effective process, exhibiting an excellent adsorption capacity for Ca 2+ in comparison to commercial zeolites 13X and A. Building upon this, the current study evaluates their performance for the adsorption of various elements, including Ca 2+ , Mg 2+ , and NH 4 + . The objective was to evaluate the performance of the Na-P1 type zeolites for the adsorption of various elements, including Ca 2+ , Mg 2+ , and rare earth elements, with a particular emphasis on the adsorption kinetics and water hardness removal in comparison to commercial zeolite A. The results demonstrated that the Na-P1 zeolite exhibited a satisfactory sorption capacity for Ca 2+ and NH 4 + ions (66 mg/g), while displaying a relatively lower effectiveness for the sorption of Mg 2+ ions (5.6 mg/g). The Langmuir model is particularly well suited to the sorption of Ca 2+ , while the Freundlich model is more appropriate for Mg 2+ . Both models demonstrated satisfactory representation of NH₄ ⁺ sorption. Moreover, the pseudo-second-order kinetic model provides an excellent description of the Ca 2 ⁺ and Mg 2 ⁺ sorption processes, while both models effectively describe the NH₄⁺ adsorption kinetics. Additionally, Na-P1 zeolite was observed to effectively reduce water hardness from 322 to 63 mg CaCO₃/L at temperatures of 10, 20, and 38°C, and to 18 mg/L at 58°C. These findings suggest that Na-P1 zeolite has promising potential for applications as a water softening agent. Regarding metals and rare earths, the Na-P1 zeolite demonstrated noteworthy sorption efficiencies for Cd 2+ (138 mg/g), Ce 3+ (209 mg/g), Cr 3+ (56.2 mg/g), and Cu 2+ (60.5 mg/g). However, it demonstrated lower sorption efficiencies for Co 2+ , Mn 2+ , Ni 2+ and Dy 3+ (below 16 mg/g). The findings illustrate that Na-P1 zeolites are effective for the adsorption of diverse elements, offering a promising avenue for the sustainable transformation of industrial waste into valuable materials for environmental applications. • Zeolite Na-P1 synthesized from aluminosilicate residue was used for water treatment. • Zeolite Na-P1 demonstrated a high affinity for Ca 2+ and NH 4 + sorption. • Zeolite Na-P1 shows promising potential for effective applications as water softening agent. • Zeolite Na-P1 showed notable sorption for: Cd 2+ , Cr 3+ , Cu 2+ and Ce 3+ . .

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.538

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 head0.008
GPT teacher head0.247
Teacher spread0.238 · 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