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Record W3108955307 · doi:10.1002/cjce.23961

Use of fibroin polypeptide from silk processing waste as an effective biosorbent for heavy metal removal

2020· article· en· W3108955307 on OpenAlexaffvenue
Yifeng Huang, Muhammad Usman Farooq, Prodip K. Kundu, Swapnali Hazarika, Xianshe Feng

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFibroinSorptionAdsorptionChemical engineeringChemistryBombyx moriBiopolymerAqueous solutionNuclear chemistryMaterials sciencePolymer chemistryComposite materialSILKOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Abstract Silk fibroin was extracted from Bombyx mori cocoon and used as a biosorbent to remove Pb 2+ , CrO 4 2− , Cu 2+ , and Co 2+ from aqueous solutions. The sorption isotherms for the metals in fibroin were determined, and a thermodynamic analysis (ΔG ° , ΔH ° , and ΔS ° ) of the sorption process showed that the metal sorption in fibroin was physical, spontaneous, and endothermic. The sorption kinetics data was found to match well with the modified pseudo‐second‐order rate model, and the sorption rate constant was determined based on a rectified approach of model fitting. It was shown that the sorption rate constant was largely independent of the initial sorbate concentration, which was expected because it is a specific quantity characterizing the relationship between sorbate concentration and the rate of sorption. The metal‐loaded fibroin could be regenerated easily by stripping the metal off fibroin using ethylenediamine tetraacetic acid, and the sorption performance of the regenerated fibroin remained the same as pristine fibroin. When used repeatedly over 10 sorption‐regeneration cycles, the fibroin biosorbent showed no change in sorption capacity, which demonstrates the excellent stability and reusability of the fibroin biopolymer for metal capture from water.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.333

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 head0.018
GPT teacher head0.226
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

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

Citations8
Published2020
Admission routes2
Has abstractyes

Explore more

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