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Record W2291138674 · doi:10.1002/cjce.22469

Immobilization of horseradish peroxidase on modified PAN‐based membranes for the removal of phenol from buffer solutions

2016· article· en· W2291138674 on OpenAlexvenueno aff
Shuai Wang, Wei Liu, Jinwang Zheng, Xiaoping Xu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsnot available
Fundersnot available
KeywordsHorseradish peroxidasePhenolBuffer (optical fiber)MembraneChemistryPeroxidaseChromatographyChemical engineeringOrganic chemistryComputer scienceBiochemistryEnzymeEngineering

Abstract

fetched live from OpenAlex

Polyacrylonitrile ultrafiltration membranes have been widely used in many separation processes. In this paper, horseradish peroxidase was immobilized onto polyacrylonitrile ultrafiltration membrane by crosslinking with glutaraldehyde. The immobilized enzyme possessed a protein loading of 0.025 mg/cm2membrane and a specific activity of 105 U/mgprotein (105 μmol/min/mgprotein). The initial modified and immobilized membranes were observed using SEM and FTIR. Potential applications of HRP membranes were investigated in the removal of phenol through oxidation with the addition of hydrogen peroxide. The optimum pH of the immobilized enzyme was determined to be 6.0, and the optimum hydrogen peroxide concentration to be 30 mmol/L. Almost 100 % removal of phenol (1 ∼ 10 mg/L) from water was achieved by HRP membranes. For high concentrated solutions, successive cycles were successfully used to improve the degree of phenol oxidation. Furthermore, the immobilized horseradish peroxidase was operationally stable. These results suggest that the HRP membrane has promising applications for the removal of phenol.

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)0.001
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
Metaresearch0.0010.000
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 integrity0.0010.001
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 head0.014
GPT teacher head0.182
Teacher spread0.169 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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