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Record W3135548662 · doi:10.1139/cjc-2021-0004

Fabrication of three bio-adsorbents from different parts of rape straw

2021· article· en· W3135548662 on OpenAlexvenueno aff
Juan Xie, Biao Liu, Hu Wang

Bibliographic record

VenueCanadian Journal of Chemistry · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsnot available
Fundersnot available
KeywordsAdsorptionChemistryKeroseneStrawChemical engineeringChromatographyOrganic chemistryNuclear chemistryInorganic chemistry

Abstract

fetched live from OpenAlex

Three kinds of bio-adsorbents are fabricated from the different parts of rape straw, which are adsorbent core, adsorbent hull, and adsorbent stalk, respectively. As the adsorbates, kerosene, paraffin, rapeseed oil, and dibutyl phthalate are employed to evaluate the adsorption performance of the three kinds of bio-adsorbents. The results suggest that adsorbent core has much higher adsorption quantity to all the four adsorbates (27.37, 32.23, 33.37, and 39.28 g/g, respectively) than adsorbent hull (7.39, 8.37, 8.85, and 10.30 g/g) and adsorbent stalk (6.75, 7.25, 7.92, and 9.32 g/g). The adsorption mechanism of the three bio-adsorbents is investigated. The results illustrate that different bio-adsorbents own different micromorphologies. The special microchamber structure is found in the bio-adsorbent of the adsorbent core, which is seen as the main reason for its excellent adsorption performance. The adsorption volume of unit mass (V a ) was proposed to evaluate the intrinsic adsorption properties of the bio-adsorbents. The recovery performance of the bio-adsorbents is investigated by using of two different treatment methods. The effect of treatment on recovery rate is discussed.

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 categoriesInsufficient payload (model declined to judge)
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 score0.013
Threshold uncertainty score0.998

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)0.0030.000

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 head0.024
GPT teacher head0.219
Teacher spread0.195 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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