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Record W1868414280 · doi:10.1002/jctb.4630

Influence of polymeric materials on the performance of a mesophilic biotrickling filter treating an α‐pinene contaminated gas stream

2015· article· en· W1868414280 on OpenAlexfundno aff
María Montes, María C. Veiga, Christian Kennes

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMinisterio de Ciencia e Innovación
KeywordsPolymerBiodegradationMaterials scienceTRACERChemical engineeringFilter (signal processing)ChemistryPulp and paper industryChromatographyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Recent studies have characterized the most effective absorbent polymers for the removal of volatile hydrophobic pollutants from air. This study evaluates the performance of a laboratory scale α‐pinene‐degrading biotrickling filter ( BTF ) packed with lava rock. A second solid phase, Hytrel ® G3548L (5%, v/v), was added in order to check its effect on the performance of the system during long‐term operation. RESULTS The biodegradation profile was similar in both bioreactor configurations, with or without solid polymers, reaching a maximum elimination capacity ( EC max ) around 25.6 g m −3 h −1 when an inlet loading rate of 57.2 g m −3 h −1 was applied. Tracer studies were carried out in order to determine the effect of the liquid and gas velocities, and to analyze the gas distribution with the different packing materials. CONCLUSIONS Deviation from ideal flow caused by channelling of the fluid was observed from tracer studies. No better performance was observed under steady‐state conditions in the presence of polymeric Hytrel material. Further considerations are needed for the selection of an appropriate absorbent polymer for α‐pinene removal. © 2015 Society of Chemical Industry

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.242
Teacher spread0.228 · 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.

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
Published2015
Admission routes1
Has abstractyes

Explore more

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