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Record W4404456642 · doi:10.1016/j.jobab.2024.11.003

Remediation and resource utilization of Cr(Ⅲ), Al(Ⅲ) and Zr(Ⅳ)-containing tannery effluent based on chitosan-carboxymethyl cellulose aerogel

2024· article· en· W4404456642 on OpenAlexvenueno aff
Shuang Liang, Xuechuan Wang, Chao Wei, Long Xie, Zhongming Song, Xugang Dang

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2024
Typearticle
Languageen
FieldChemistry
TopicPigment Synthesis and Properties
Canadian institutionsnot available
FundersQuanzhou City Science and Technology ProgramNational Natural Science Foundation of ChinaScience and Technology Projects of Fujian Province
KeywordsAerogelCarboxymethyl celluloseChitosanCelluloseEnvironmental remediationEffluentWaste managementChemistryNuclear chemistryPulp and paper industryMaterials scienceChemical engineeringOrganic chemistryNanotechnologyContaminationEngineeringSodium

Abstract

fetched live from OpenAlex

Composite aerogel based on sodium carboxymethyl cellulose (CMC Na) and chitosan (CS), i.e., CS/CMC Na, was prepared through a sol-gel method. Then, CS/CMC Na was used for simulating the adsorption of metal ions (Cr 3+ , Al 3+ and Zr 4+ ) produced by the tanning industry. The adsorption process is consistent with the Langmuir isotherm adsorption model and pseudo-second order kinetics. The maximum fitted adsorption capacities of Cr 3+ , Al 3+ , and Zr 4+ could reach 250.0, 111.1, and 100.0 mg/g, respectively. After metal ion adsorption, the obtained composite materials (CS/CMC Na-Cr 3+ , CS/CMC Na-Al 3+ , and CS/CMC Na-Zr 4+ ) were used as re-tanning agents in the re-tanning process to leather. The re-tanning agent could increase the shrinkage temperature of leather by up to 5 °C. Compared with the traditional method, the method utilized in this study achieved the integration of mental ions-containing wastewater treatment and waste adsorbent/adsorbates recycling.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.022
GPT teacher head0.236
Teacher spread0.215 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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