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Record W4403190196 · doi:10.3390/su16198630

Recovery of Magnetic Ni Particles from Spent Catalyst Leachate by Direct Cementation

2024· article· en· W4403190196 on OpenAlexaff
Farhad Moosakazemi, Ali Entezari-Zarandi, Sina Ghassa, Faı̈çal Larachi

Bibliographic record

VenueSustainability · 2024
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsLeachateCementation (geology)Environmental scienceCatalysisWaste managementMagnetic separationMetallurgyMaterials scienceChemistryEngineeringCement

Abstract

fetched live from OpenAlex

An alternative method based on cementation for the recovery of nickel from spent Ni/Al2O3 reforming catalyst pregnant leach solution (PLS) was proposed to overcome the limitations of traditional two-step extraction and precipitation processes. Thermodynamic analysis was used to evaluate the potential interference of key reactions, such as nickel and sacrificial metal leaching, with the selective cementation of nickel from the PLS. Key variables in the cementation process were optimized using response surface methodology (RSM) combined with Box–Behnken design (BBD). Under optimal conditions—pH 2.2 ± 0.1, processing time of 15 min, and Al/Ni molar ratio of 2.65—a maximum nickel recovery of 73.2% was achieved. Extensive characterization confirmed the high quality of the cemented nickel product: (i) ICP-OES indicated nickel purity of 99.47%, (ii) XRD patterns verified the presence of pure face-centered cubic nickel, (iii) SEM-EDS and vibrating sample magnetometry confirmed the high purity of the metallic nickel particles.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.001

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.

Opus teacher head0.008
GPT teacher head0.253
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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