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Record W3023528254 · doi:10.5004/dwt.2020.25096

Adsorptive removal of nickel by modified natural adsorbents: optimization, characterization and application

2020· article· en· W3023528254 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.

Bibliographic record

VenueDesalination and Water Treatment · 2020
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsMinistry of Agriculture
Fundersnot available
KeywordsNickelAdsorptionCharacterization (materials science)Natural (archaeology)Materials scienceChemistryChemical engineeringMetallurgyNanotechnologyEngineeringOrganic chemistryGeology

Abstract

fetched live from OpenAlex

ABSTRACT The adsorptive removal of nickel by persimmon tannin-based adsorbents was first evaluated. NaOH modified persimmon powder-formaldehyde resin (NPPFR) showed significantly enhanced adsorption capacity towards Ni(II). The adsorption process was completely achieved equilibrium within 60 min, and the well-fitted pseudo-second-order kinetics data indicated that chemisorption is the main rate-limiting step. The adsorption isotherms followed the Langmuir model, where the maximum adsorption capacity reached 81.6 mg g –1 at pH 5.0. The adsorbed Ni(II) ions were desorbed by 0.1 mol L –1 HNO 3 and the regenerated adsorbent exhibited undiminished sorption efficiency for 4 cycles. The removal of Ni(II) from actual industrial wastewaters in both batch and column experiments was demonstrated to be effective. The adsorption mechanism was proposed to be electrostatic attraction and ion exchange. In addition, competition and replacement during the adsorption process were found in the multiple metal ions systems. The results indicated that NPPFR can serve as a low-cost, eco-friendly and effective alternative for Ni(II) removal in wastewater treatment.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.711
Threshold uncertainty score0.330

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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